The campaign is ready to go after two weeks of preparation. The creative is approved, the landing page is set up, and the media is booked. Now, we just need to complete a compliance review, which is being discussed over emails and via a Slack channel. Three reviewers are involved, and two versions of the disclosure are being shared. However, it seems unclear which comments have been addressed and who gave the final approval. By the time everything gets cleared, the team has lost valuable time and may feel a bit frustrated with the legal process.

In regulated finance, we often see a familiar situation where marketing leaders view it as a legal challenge. They feel that reviewers take too long and that the rules are too strict. However, a more helpful way to view the situation is to see it as a problem with workflow design. The compliance review process involves multiple parties and requires solid evidence, but many content teams use tools meant for casual conversations.

By designing the workflow correctly, compliance can actually help regulated brands publish quickly and effectively. Nearly half of enterprise marketers — 47% — name workflow and content approvals as a challenge, according to Content Marketing Institute research. In regulated finance, that challenge carries legal weight that businesses in unregulated industries typically don’t face.

This article offers a five-component blueprint for building a compliance-first content workflow, along with a helpful legal-and-marketing operating model to keep everything running smoothly.

Key takeaways

• Compliance-first content architecture builds review routing, approval gates, disclosure libraries, audit trails, and retention directly into the publishing workflow.
• In regulated finance, compliance review is a legal precondition to publishing, and workflow design is where teams can recover the time it costs.
• Moving compliance review upstream to the brief shapes the work before rework gets expensive.
• Audit trails and automated retention keep communications aligned with FINRA Rule 2210 and SEA Rule 17a-4 recordkeeping obligations.
• A four-level maturity model — ad hoc, documented, systematized, and compliance-first — helps teams locate their stage and choose the next step.

Why traditional content workflows break under regulatory load

Most marketing workflows treat review as a single approval step at the end. A senior team member looks over the almost-final asset, gives a quick thumbs-up, and the team moves forward.

For regulated content, that process falls short of FINRA and SEC requirements. Regulated content calls for a more thorough review involving multiple parties. Firms need to document who approved what and be able to reproduce that record even years later. Refining the workflow brings the work into alignment with these regulations.

Three challenges come up again and again:

• Ad-hoc routing: Reviews happen over email and Slack, which makes it hard to track who approved each version. Confusion follows when someone has to sort through threads to reconstruct the approval history.
• Improvised disclosures: Writers recreate required disclosures from memory each time, so wording varies across assets — a problem teams solve by templatizing required elements. That inconsistency creates compliance risk and muddies brand voice.
• No retention discipline: Published communications aren’t always archived systematically. When regulators request information, the search through inboxes and shared drives turns into a scramble.

These challenges can go beyond delays. Each gap is also a regulatory risk. But the issues are really workflow problems, and workflow problems can be fixed. Adding more people to the review team won’t address the underlying gaps; rethinking the process will.

The five components of a compliance-first architecture

A compliance-first content operation has five components. Together, they make compliance part of the process rather than something bolted on at the end.

• Review routing: Content is directed automatically to the right reviewers based on type, channel, and claims. A performance claim in a paid social ad follows a different path than an educational blog post. The system knows when a registered principal has to approve, when legal input is required, and when product verification is necessary. Independent reviews run in parallel; dependent ones run in sequence.
• Approval gates: Each required approval is a checkpoint everyone can see. Work can’t publish until every approval is in. Marketers always know an asset’s status and who currently holds it.
• Disclosure libraries: A managed repository holds pre-approved disclosures, standard claims, and templates. Writers pull approved language straight into drafts. When a disclosure changes, it updates in one place, which keeps wording consistent and cuts the volume of text that needs review.
• Audit trails: Every draft, comment, edit, and approval is logged with a timestamp and attribution. The result is a complete history that turns regulatory inquiries into a lookup. That record maps to FINRA Rule 2210, which in most cases requires principal approval of retail communications before first use, and requires retention of the approver’s name, the approval date, the dates of first and last use, and the source of any statistic or chart used in the piece.
• Retention and archiving: Communications are saved automatically in published form, meeting recordkeeping obligations like SEA Rule 17a-4 and FINRA Rule 4511. It happens at the publishing step, so nothing depends on someone remembering to file a copy later.

Compliance runs through the whole content journey instead of waiting at the end of it.

The legal and marketing operating model

Tools alone won’t fix collaboration if legal only sees the work at the end. The operating model has to change with them.

Move compliance to the start. When reviewers join at the brief and kickoff stages, their input shapes ideas while changes are still easy and cheap to make. Naming constraints early lets the team get creative without inviting costly revisions later.

Establish shared definitions. Legal and marketing should agree on the terms for content types and risk levels. When both teams define a performance claim or a tier-two asset the same way, the confusion disappears and reviewers can focus on what matters in each project.

Commit to clear SLAs. Marketing provides complete briefs with enough lead time; legal sets a review timeline for each risk tier. Those commitments give both teams a schedule they can count on.

Broaden the pool of pre-approved material. The more claims, disclosures, and templates that carry standing approval, the less new content each project puts in front of a reviewer. Routine work moves quickly on pre-approved elements, and reviewers spend their attention on what’s actually unique.

A maturity model: what good looks like

Most regulated content operations fall into one of four levels. Knowing yours helps you figure out the next move.

• Level 1 — Ad hoc. Reviews happen over email and Slack. Disclosures are made on the spot, and there’s no clear record. Delays and risk run high.
• Level 2 — Documented. Checklists and shared documents bring some consistency. Routing is still manual, though, and reconstructing records after the fact is a struggle.
• Level 3 — Systematized. Routing and approvals follow rules. A disclosure library exists, and the audit trail is captured automatically as work moves through the process.
• Level 4 — Compliance-first. All five components are built into the content platform. Compliance becomes part of the workflow, and the team works faster while governance remains in place.

Moving up is gradual. A Level 1 team gains the most from a disclosure library and a routing map. A Level 3 team gains the most from shifting manual steps onto a platform that captures the audit trail on its own. Wherever you stand, there’s a path to better speed and governance.

The payoff

Compliance-first design tackles the bottleneck head-on, streamlining cycle times through systematic routing and approvals. There can be a high cost of getting this wrong. FINRA fined M1 Finance $850,000 after influencers promoting the firm published posts that were not fair and balanced and made misleading claims. M1’s written supervisory procedures covered retail communications in general, but nothing routed influencer posts into that process, so no registered principal reviewed them and the firm kept no record of what was published or when. Roughly 1,700 influencers drove more than 39,400 funded accounts over three years. M1’s remediation was architectural: a registered principal now approves influencer posts before use, and the firm retains those communications systematically.

Begin by assessing your current workflow against five key components: review routing, approval gates, disclosure libraries, audit trails, and retention. Identify areas where email threads or individual memories fill gaps, revealing leaks in both governance and speed. A governed content platform like Contently integrates these components by design, enabling regulated brands to establish compliance as a foundation for confident publishing.

Frequently asked questions

What is compliance-first content architecture?

Compliance-first content architecture is a content operation that builds regulatory review into the workflow from the start. It combines five components — review routing, approval gates, disclosure libraries, audit trails, and retention — so compliance runs through every stage of content production.

How does FINRA Rule 2210 affect content marketing in financial services?

FINRA Rule 2210 governs communications with the public. It defines three categories — correspondence, retail communications, and institutional communications — and in most cases requires a registered principal to approve retail communications before first use. Firms must also retain specific records, including the approver’s name, the approval date, the dates of first and last use, and the source of any statistic or chart used. A workflow with built-in audit trails and retention helps firms meet these requirements.

Why do traditional content approval workflows break under regulatory load?

Traditional workflows treat review as a single, late approval step. Regulated finance calls for multi-party review, documented sign-off, and records firms can reproduce years later. When reviews run over email and Slack with improvised disclosures and no systematic archive, delays and compliance risk both climb.

How can regulated brands speed up content compliance review?

Speed comes from workflow design. Route content automatically by type and risk tier, move compliance into the brief stage, expand the library of pre-approved claims and disclosures, and capture audit trails as work progresses. These steps shrink the review surface and give both teams predictable SLAs.

The post Compliance-First Content Architecture appeared first on Contently.

Key Takeaways

• In finance, months and several stakeholders separate the content that influences a deal from the close, so last-touch attribution misreads what actually worked.
• Gartner finds B2B buying groups run 5 to 16 people, and 74% of buying teams hit unhealthy conflict—a single content touch cannot capture that.
• Early educational content is systematically under-credited because much of the committee’s research happens off-platform, before anyone fills out a form.
• Measure at the account and buying-group level with multi-touch attribution, not individual leads.
• Report in CFO terms: influenced pipeline, influenced revenue, cycle-time impact, and payback period.

The Measurement Gap

Imagine a finance buyer downloads a white paper in March, but the deal doesn’t close until November. During that time, a procurement lead, a risk officer, two analysts, and a CFO each weigh in, and that white paper may never even be mentioned in a sales call. When the revenue finally comes in, which piece of content played a role? For those marketing in financial services, this question often lacks a clear answer, and standard attribution tools can make it even trickier.

The issue is structural. Long cycles and large buying committees pull content engagement away from the closed deal. Last-touch reporting often credits whatever was open in the browser at signing. To measure content ROI effectively in finance, we need to shift from last-touch attribution to multi-stakeholder models that reflect how these buyers truly make decisions.

Why Finance Cycles Challenge Simple ROI Math

Let’s start with the committee. B2B buying groups can range from five to 16 people across as many as four functions, according to a Gartner survey. In finance, the decision often involves a CFO or controller, whose criteria may differ from those of other people in the buying group, say, an accountant or analyst. Each additional stakeholder consumes content on their own timeline and for different reasons.

These groups seldom move in harmony. According to the same Gartner survey, 74% of buying teams experience conflict during the decision-making process, with members often working from competing goals. Content that helps resolve these conflicts early can shape outcomes, but it often leaves little trace in traditional CRM systems focused on lead forms and demo requests.

As we stretch this process across the calendar, the math becomes complicated. Enterprise finance deals can take many months to close, and 57% of sales professionals say the sales cycle is getting longer. One piece of content can’t easily be linked to revenue when a buying group of five to 16 people takes many months to reach a decision.

Where Attribution Breaks Down

Last-touch attribution rewards the final steps in the funnel, since that’s closest to the close. First-touch attribution does the opposite, giving too much credit to what initially brought in the lead while ignoring what influenced the decision afterward. Over a lengthy multi-person journey, both methods can be misleading.

Early-stage content often suffers the most. The explainer that helped the committee understand a category, or the research shared with the CFO, plays a significant role long before anyone fills out a form. Yet a touch-based model tends to undervalue this content. Much of this research happens off-platform, with buyers conducting their own searches before engaging with marketing. Content that works during this self-directed phase remains invisible to any tracking tool.

A Framework for Full-Journey Measurement

To effectively measure a long, multi-stakeholder cycle, we need to implement a few key changes:

• Link content to buying stages, not just leads. Consider what role each piece plays, whether it’s educating the committee or addressing a risk concern, and measure its impact on advancing that stage instead of just capturing an email.
• Track metrics at the account and buying-group level rather than just focusing on individual leads. Since finance committees decide collectively, it makes sense to see how many committee functions the content reached.
• Use multi-touch or weighted attribution to credit the entire journey, ensuring early educational content receives its fair share rather than giving all the value to the last piece before signing.
• Combine leading indicators with lagging ones. Metrics like engagement depth, committee reach, and content-influenced pipeline can show early success, while influenced revenue and cycle-time reduction confirm it later on.

Metrics That Resonate with a CFO

Certain metrics carry more weight than just raw traffic. Content-influenced pipeline and influenced revenue connect content to actual dollars instead of mere page views. Buying-group reach indicates how many committee functions a body of content has touched, providing insight into whether it’s reaching decision-makers. Cycle-time impact assesses whether accounts that engage deeply close faster, which is crucial for a finance audience concerned with time and cost. Throughout this process, the quality of engagement is more important than the quantity. Ten meaningful minutes with a business-case calculator are far more valuable than a thousand anonymous page views.

Putting It Into Practice

Start by mapping the journey. Use CRM data, content analytics, and intent signals together to approximate the hidden parts of the cycle, as none of these tools is complete on its own.

Next, ensure alignment between sales and marketing on a single attribution model before reporting any numbers. This agreement up front helps avoid disputes about whose touch counted later.

Finally, present results in terms that resonate with a CFO. Influenced revenue and payback period make a stronger impact than lead counts. Frame content ROI in a way that reflects how the buyer’s finance team evaluates every other investment, and the measurement will carry more weight in budget discussions.

Agreeing the model matters is the easy part. Running it takes the workflow and analytics to track influence across the full journey. Book your strategy call to see how Contently helps regulated brands measure content value.

Frequently Asked Questions

Why is content ROI harder to measure in finance than in other industries?

Finance deals run long—often many months—and involve large buying committees. The content that shapes the decision is often consumed months before the close, sometimes by people who never appear in your CRM, so simple attribution misses it.

What attribution model works best for long finance sales cycles?

Multi-touch or weighted attribution tracked at the account or buying-group level. It credits the full journey, including early educational content, rather than handing all the value to the last touch before signing.

Which metrics matter most to a CFO?

Content-influenced pipeline, influenced revenue, cycle-time impact, and payback period. These tie content to dollars and time, the terms a finance team already uses to judge any investment.

How do I measure content that buyers consume off-platform?

You approximate it. Combine CRM data, content analytics, and intent signals, and watch leading indicators like engagement depth and buying-group reach to infer the parts of the journey no single tool captures.

The post Measuring Content ROI in Long Finance Sales Cycles appeared first on Contently.

Earn a spot in the top 10, close the tab satisfied, and head out to happy hour.

That’s how it used to be. If you appeared high on the search results page, you felt confident about your page’s performance.

In the past, pages in Google’s top 10 provided most citations in AI overviews. However, this share has changed significantly in less than a year due to a new challenge: the query fan-out. While it’s important to acknowledge that AI Overviews can sometimes include errors, focusing on being cited rather than just ranked will help your strategy thrive through these changes.

Key takeaways

• Ranking and citation have split apart. The share of AI Overview citations that also rank in Google’s top 10 fell from about 76% in July 2025 to roughly 38% by March 2026.
• Query fan-out is the cause. Google’s AI breaks one question into many sub-queries and builds its answer from the pages that surface most consistently across all of them—not just the page that ranks for the typed query.
• Top-10 ranking still matters. A strong organic position remains the most reliable feeder into AI Overviews and the clearest authority signal Google has—it gets you into the candidate pool.
• Citation takes depth and credibility. Answer engine optimization (AEO) rewards self-contained sections, topic-level coverage, and E-E-A-T signals that let a model lift a clean, quotable claim.
• Track citations, not just rankings. Position tracking alone now misses most of the picture of where your traffic comes from.

What is a query fan-out?

Query fan-out is how an AI search system breaks one user query into several sub-queries. It collects information for each, then combines the results into one response. LLMs rely on it to produce richer answers.

When you ask a question in Google’s AI experiences, the system expands it before it answers. Behind the scenes, an AI model breaks your question into a set of related sub-queries—equivalent phrasings, follow-ups, broader framings, narrower specifications—and runs them all at once.

The AI Overview is then built from the pages that surface most reliably across that whole set. A page can rank first for its headline query, but it might not show up in the fan-out. This happens because the model looks at several related searches where other pages provide more detailed information.

Here is what that looks like in practice:

“How do I measure the ROI of our B2B content marketing program to prove its value to executives?”

Instead of running that one query and stopping, the LLM pulls the question apart into shorter searches drawn from inside it:

• measure content marketing ROI
• B2B content marketing metrics
• content marketing value
• prove content ROI to executives
• content program performance

That shift—finding answers based on the most consistent pages, not just the typed question—is what separates ranking from citation.

Why ranking still matters

Roughly half of Google searches already surface an AI summary, and McKinsey projects that figure will pass 75% by 2028. In a McKinsey survey of 1,927 US consumers, half now actively seek out AI-powered search, and it has become the leading digital source they use for buying decisions. With most searches headed toward an AI answer, the pages that get cited decide most of your traffic.

In July 2025, about 76% of pages cited in Google’s AI Overviews also ranked in the top 10 for the same query. Ahrefs’ March 2026 study looked at 863,000 keywords and around 4 million AI Overview URLs. It found that figure had dropped to about 38%.

The rest of the citations moved elsewhere on the web. Ahrefs found them split almost evenly: roughly 31% from pages ranking 11 to 100, and another 31% from pages ranking past 100 or not ranking for the query at all. Ranking and getting cited no longer go together.

Don’t throw out your plans to rank just yet. A 38% overlap is still a large minority, and top-10 pages remain the most reliable feeder into AI Overviews. A strong organic position is still the clearest authority signal Google has. Ranking well gets you considered. Getting cited takes more.

Think of it as two gates. Traditional SEO gets you into the candidate pool, and fan-out decides which candidates get quoted. A page that ranks and covers its topic with real depth clears both. A page that ranks for one keyword and stops there clears the first and stalls at the second.

What AEO actually asks of your content

This is where answer engine optimization enters. Structure helps: clear headings, self-contained sections, schema, and a direct answer near the top all make content easier for a model to parse and extract.

What you shouldn’t ignore is coverage and credibility. If the AI samples sub-queries, your content has to answer the surrounding questions in addition to the main query. That means depth over keyword breadth: one resource that resolves the real question and its natural follow-ups, written with enough specificity that a model can lift a clean, citable claim from it. The same E-E-A-T (experience, expertise, authoritativeness, and trustworthiness) signals Google has always rewarded are what make a passage worth quoting.

AEO is the same demand for good content with the stakes raised, where every section now has to stand on its own.

Where to spend your effort now

Fan-out rewards content that anticipates the questions a reader actually has, and anticipating those questions is editorial judgment.

Knowing which sub-questions matter, which framings are honest, where to be specific and where to be brief, what claim is worth stating cleanly enough to be quoted—that is the work of an experienced editor or subject-matter expert.

The brands that get cited consistently share a trait: their content carries a clear point of view and the depth to support it across a topic. Volume of output has little to do with it.

Strategies include:

• Build around topics, not single keywords. Map the cluster of questions a reader asks before and after the headline one, and cover them in depth within a page or a tight set of linked pages.
• Make every section independently citable, since a model will lift passages out of context.
• Stay the course on earning the ranking, because top-10 placement is still your surest path into the candidate pool.
• Watch where you are cited, not only where you rank. Position tracking alone now misses most of the picture.
• Invest in editorial depth and expertise. The thing that makes content worth citing holds steady: depth, judgment, and a point of view a reader, or a model, can trust. That’s editorial work, and it’s exactly what Contently’s network of expert editors and subject-matter writers is built to do.

Frequently asked questions

What is a query fan-out in AI search?

Query fan-out is the technique an AI search system uses to break a single user query into several related sub-queries—equivalent phrasings, follow-ups, broader framings, and narrower specifications. It runs them all at once, then builds its answer from the pages that surface most consistently across the whole set rather than from the one page that ranks for the typed question.

What is the difference between SEO and AEO?

SEO (search engine optimization) works to earn a high ranking on the results page, which gets your page into the pool of candidates an AI can draw from. AEO (answer engine optimization) works to get your content quoted in the AI answer itself, which depends on self-contained sections, topic-level depth, and E-E-A-T signals a model can lift a clean claim from. SEO gets you considered; AEO gets you cited.

Does ranking in Google’s top 10 still matter for AI search?

Yes. Even though the overlap between top-10 rankings and AI Overview citations fell to about 38% by March 2026, top-10 pages remain the single most reliable feeder into AI Overviews and a strong organic position is still the clearest authority signal Google has. Ranking gets you into the candidate pool; citation takes additional depth and credibility.

How do I get my content cited in Google’s AI Overviews?

Cover a topic—not a single keyword—deeply enough to answer the surrounding sub-questions a reader and the fan-out will ask. Structure each section to stand on its own with clear headings, schema, and a direct answer near the top, and write with enough specificity and demonstrated expertise (E-E-A-T) that a model can extract a clean, quotable claim.

What is E-E-A-T and why does it matter for AEO?

E-E-A-T stands for experience, expertise, authoritativeness, and trustworthiness—the signals Google has long rewarded in rankings. For AEO they matter because the same qualities that make a passage credible to Google are what make it worth quoting to an AI model: specific, well-sourced, expert content is the kind a model is most willing to cite.

The post Your Best-Ranked Page Might Be Invisible to Google’s AI appeared first on Contently.

But your financial content isn’t making real progress. It isn’t doing enough to get AI engines like ChatGPT and Google’s AI Overviews to surface your work on the queries your target customers actually run. Then a senior buyer tells you they read three of your articles and still chose a competitor you should have beaten.

What’s going on? It’s about credible content—both AI engines and buyers trust the named expert.

AI engines choose financial content based on who wrote it and verified it. McKinsey reports that when AI engines answer, a brand’s own website supplies just 5 to 10 percent of the sources they draw on. Buyers are no different. And this is especially true in financial industries, where more than 65 percent of what AI engines cite comes from third parties, not your own site.

Last week, we discussed how an operating model can help your organization produce trustworthy content at scale. This week, we talk about the content itself and the role credibility plays in improving your results.

Below are five signs to look for to improve your content for AI answers and buyers, along with examples of brands that are winning trust at scale.

Why credibility is now the financial content metric

Content credibility determines which financial brands appear in AI answers and engage potential buyers. Regulated brands can pull further ahead when their content is credible. Large language models are built to defer to credentialed institutions on regulated topics, and their safety policies enforce it. A retirement-planning guide with no byline competes against the same guide published under a Certified Financial Planner with twenty years of experience. AI answers cite the second nearly every time.

Buyer behavior points in the same direction. Gartner surveyed 1,539 US consumers in October 2025 and found that half prefer brands that avoid generative AI in consumer-facing content. Another 68 percent wonder whether what they see is even real.

In financial services, that skepticism runs deeper. In early 2023, CNET ran AI-generated personal-finance explainers under the byline “CNET Money Staff.” After readers caught errors, it audited the batch. One explainer told readers a $10,000 deposit at 3 percent would grow to $10,300 in a year. The real figure is $300. CNET said every piece had been “reviewed, fact-checked and edited by an editor with topical expertise before we hit publish.” Somehow, this and other errors made it to published pieces anyway. It goes to show that a piece may sound authoritative, but if it’s wrong, it can impact the credibility of your organization.

Sign 1: Generalists produce your regulated content

Skimping on quality and expertise might initially save you money, but it will likely cost you in the end, financially and from a reputation standpoint.

A generalist producing a private-wealth guide might clear internal review. It will not earn a citation on buyer-stage queries, and it will not survive a reader who checks the byline. Google’s January 2025 Search Quality Rater Guidelines tell raters to give the lowest rating to pages whose main content is auto-generated with little to no added value (Section 4.6.6). The same logic catches a human writing outside their depth.

Match the writer to the subject before the first draft, name the credential in the byline, and link every author bio to verifiable prior work.

Sign 2: Legal sees the draft only after it’s written

Most financial programs treat compliance as quality assurance: legal gets the draft at the end, where review adds days per asset and stalls the calendar. So a reviewer who first sees a finished draft has no way to flag a problem except to send the whole piece back, which increases delays and wears down writers.

Moving review upstream while maintaining a strong audit trail helps resolve the bottleneck. Royal Bank of Canada routed every piece through one dedicated legal reviewer and a shared “watch-outs” document that set the guardrails before writers opened a draft. With a Managing Editor workflow on top, it compressed time-to-publish from weeks to a day or two across 22 divisions. When compliance reviews the brief, source list, and outline before drafting, it catches issues at each stage rather than all at once at the end.

Sign 3: AI citations go unmeasured

The metrics most financial programs track assume a web where Google sends traffic to publisher pages. That assumption is broken. Pew found that about one in five Google searches now returns an AI summary, and when one appears, searchers click a traditional result roughly half as often, 8 percent of the time, versus 15 (Pew Research Center, 2025). Traffic alone no longer tells you whether your content earned the buyer’s attention, but the answer engine citation rate does.

The sharper question is: What share of buyer queries in your category cite you in the AI answer? If you can answer that, you know where you stand. Tracking the following metrics can help you understand if your buyers are including you in their shortlist:

• Citation rate
• Share of voice in AI answers
• Brand-mention growth across ChatGPT, Google AI Overviews, Gemini, Copilot, and Perplexity

If you’re still watching pageviews, you’re tracking traffic that AI is busy siphoning off.

Sign 4: AI drafts ship without a credentialed editor in the loop

A review box on the org chart is not the same as a credentialed editor who can catch a domain error. CNET’s money desk had editors, and it still shipped the compound-interest piece above. The people in the loop could not catch what a finance expert would have flagged on sight. The fix is not to ban AI from the workflow. Use it for research synthesis, first-draft scaffolding, and metadata. Then route every output through a Managing Editor with subject-matter depth before publishing.

Then document the review in the audit trail with the reviewer’s name, date, and version. That record is exactly what an auditor asks for and what an AI engine’s safety layer rewards. Handle AI this way, and you publish content faster than the teams skipping the step, and you still clear compliance on the first pass.

Sign 5: Author credentials and review attribution are invisible

If an article is not attributed to a verifiable author, then AI engines and buyers don’t know who stands behind it. Buyers, and the AI agents shortlisting vendors for them, check the byline, scan for credentials, and look for review attribution. A piece missing any of the three won’t make the cut. Contently’s own analysis of AI search puts it plainly: credentials are not a compliance checkbox; they are the entry requirement for a channel that converts better than search.

So make the answer obvious on the page. Give every regulated piece a named author whose byline links to a credentialed bio, inline citations with live source URLs, and a visible “reviewed by” line. None of it slows you down when it is built in at intake. All of it disappears the moment you bolt it on at the end. Publish all three on every piece, and the advantage only grows the longer you hold it.

What to do next

Contently pairs a vetted network of credentialed financial writers with audit-ready editorial workflows, so your team earns trust and citations, without slowing down.

FAQs

How do I cut compliance review time without cutting controls?

Move compliance review upstream. The brands moving fastest have not eliminated review steps. They review the brief, source list, and outline before drafting begins, then flag issues at each stage. That removes the rework cycle, which is where most of the calendar drag lives. Expect measurable improvement in time-to-publish within the first two production cycles after restructuring intake.

What if I don’t have credentialed in-house experts for every financial topic I need to cover?

Most financial brands don’t, and they aren’t expected to. Sourcing credentialed external contributors (CFP, CFA, JD-banking, former CFO bylines) through a vetted creator network is now the default for enterprise financial services content programs. The key is matching credentials to topic at intake and locking in editorial review by a Managing Editor with regulated-industry experience and a contributor onboarding bar that screens for prior published work.

How long until I see citation rate and AI search visibility improve after fixing these gaps?

Brand mentions and citations compound over a 2- to 6-month window once the structural fixes are in place. AI engines reweight based on review-platform presence, brand mention growth, and content freshness. Programs that move credentialed bylines, third-party validation, and content refreshes inside a single quarter typically see their first measurable citation lift by month three.

Stop paying the credibility tax

Publishing volume is easy to match. Any competitor can outspend you on output. What they can’t copy is your credibility. Focus on ensuring every claim in your content traces back to a named expert and a review trail a machine can read. Build that, and you stop losing buyers you should have won.

The post 5 Signs Your Financial Content Program Has a Credibility Problem appeared first on Contently.

Here’s how to tell. Look for symptoms, such as competitors appearing in answer boxes above your content or compliance flagging a freelancer’s work. Another sign is the flood of requests to generate more and more content without the proper framework to ensure quality.

Quick-fix solutions can be tempting. You could try a new AI writer or an SEO tool. But those usually just hide the real problems, like taking painkillers for a chronic headache. Instead, your content system needs to clarify who produces the content, how it flows through the system, where AI fits, and which metrics are important. If one layer is weak, it can weaken the others.

Key takeaways

• Trustworthy content at scale is an operating-model problem—four interlocking layers (creators, workflows, AI guardrails, governance) reinforce each other, and weakness in any one caps the others.
• Named, vetted, subject-matched creators are a search and compliance requirement; latest search engine trends downgrade anonymous, low-effort AI content.
• Workflow rigor unlocks scale; a structured brief → source → draft → review → publish process with editorial checkpoints is the compliance backbone.
• AI is an accelerant on specific workflow steps; every AI-assisted step passes the same editor and audit checkpoints as human work.
• Governance ties output to brand voice, compliance, and a feedback loop measured in voice consistency, editorial pass rate, and AI overview citations.

Here’s a breakdown of four key connected layers of an effective operating model:

Layer 1. Vetted Creator Network

Anonymous content creates trust problems, and in regulated fields like healthcare, finance, and law, you risk getting flagged by the compliance team. A creator who does real work on a subject deserves a byline, and search engines have come around to the same view.

In January 2025, Google updated its Search Quality Rater Guidelines to instruct raters to assign the lowest quality rating to pages where most of the main content is AI-generated with little effort, originality, or added value. Google’s own Search Central documentation reinforces the same line, calling out the use of generative AI to produce many pages without adding value for users as a violation of its spam policy on scaled content abuse, and pointing publishers to the rater-guideline sections on scaled content abuse and minimal-effort main content.

Both anonymous freelance marketplaces and AI-only generation platforms run into the same wall here. Without a verifiable expert behind the work, the content doesn’t earn trust—both from humans and AI.

A strong creator network vets every contributor and matches them to the right assignment well before the review stage. You don’t want a writer with expertise in retirement planning to write a piece about cardiology. You put your reputation at risk. And even a strong retirement-planning writer needs extra time to come up to speed on cardiology, which might defeat one of the main reasons you’re trying to quickly scale content in the first place.

The contributor vetting process involves verifying identities, reviewing portfolios, testing subject knowledge when necessary, and continuously scoring performance based on editorial outcomes—something we’ve been refining at Contently for years. Our creator network ensures that every contributor is identified, vetted, and paired with their relevant subject area. This structure supports all aspects of our model, including workflow, AI, and governance.

Layer 2. Structured Workflow

Scaling content suggests that you’re moving. But which direction are you heading? If it’s not forward—because you suddenly have more Google Docs to juggle and more Slack threads to sift through—then you need to redirect your efforts.

As the volume of work grows, editors find themselves buried in project management and compliance checks. What should be their time to make a piece of content shine shrinks, shifting them into a frantic scramble.

Voice drift becomes noticeable, and drafts may require endless revisions, leading to missed deadlines. The inevitable blame game begins, fingers pointed at writers and tools, but the true culprit lurks in the workflow.

The remedy lies in five essential stages with mandatory editorial checkpoints. This transforms workflow into a seamless system. The pivotal stages requiring editor expertise include:

• Brief: Defining assignments rooted in genuine audience needs
• Source: Scrutinizing experts and verifying citations
• Draft: Aligning with voice and structural standards
• Review: Gaining legal, brand, and SME approvals
• Publish: Ensuring attribution remains intact

A structured workflow provides an audit trail that timestamps every brief, source, edit, approval, and publish action, linking them to specific team members and supporting your content compliance. In regulated industries, this may mean the difference between accountable content and an incident that can escalate into a mandatory fire drill meeting on a Friday afternoon.

Layer 3. AI Inside Guardrails

AI can’t operate entirely on autopilot. It should be used in specific steps of the workflow, with each step reviewed by a credentialed editor.

Map AI to the stages from Layer 2 and use AI for:

• Research synthesis and citation (surfacing for editor verification)
• First-draft scaffolding from a tight brief
• SEO and metadata work
• Structured-data generation

There are conditions. For example, style and structure suggestions during editing need editor approval. Example of where AI use should be off-limits: factual claims in regulated subject matter, the final byline voice, and anything that would ship without human review.

The principle is simple. AI output moves through the same checkpoints as human work. A credentialed editor reviews it. The audit trail attributes it. The same brand voice and compliance standards apply. No AI content goes live unedited under a real byline.

Programs that ignore these guardrails, and AI-only platforms, can result in voice drift and hallucinations, or worse, public failures. In the recent case of Hearst’s King Features, it distributed a syndicated summer supplement to the Chicago Sun-Times and the Philadelphia Inquirer. It included fictional books tied to real authors, including Isabel Allende, Rebecca Makkai, and Min Jin Lee. A freelancer (whose contract was later terminated) used AI, but skipped verification. There was also no editorial oversight between the AI’s output and publication. This incident has Sun-Times reevaluating its content-partner relationships.

The opposite is also a problem: programs with too many guardrails. It can produce content that sounds generic and disconnected, which is another reason the editor needs to be at every checkpoint.

Layer 4. Governance

Governance unites the first three layers into a cohesive system. It establishes brand-voice rules, compliance checks, and review SLAs for every piece of content, whether created by humans or AI. Without governance, even a strong creator network and a smooth workflow can lead to inconsistent results because there’s no shared standard for quality.

The measurement framework should cover:

• Voice-consistency scoring against a documented brand standard
• Time-to-publish by content type
• Editorial pass rate, or the percentage of drafts that pass review on the first attempt
• Share-of-voice in target SERPs and citation rate in AI Overviews
• For regulated industries, audit-readiness as a key metric, allowing you to reconstruct any published piece’s history in under an hour

Notice what’s absent from this list: raw traffic. In the AI Overview era, share-of-voice and AI Overview citations are often more important than clicks for many enterprises, as users find answers without clicking through. Programs that focus mainly on sessions are measuring the wrong outcome.

Governance also serves as the feedback loop for the entire system. Performance data informs creator scoring (who delivers voice and subject on time), workflow adjustments (which checkpoints catch defects and which add friction), and AI-prompt guidelines (where model output is strong and where it needs more constraints). VPs of Marketing and Brand leaders oversee this layer.

Map Your Gap, Then Build

If you want to map your current operation against the four layers and identify the highest-leverage gap to close, Contently will run a working session with you. The Contently creator network and editorial workflow platform are the reference implementation of this operating model. The value of the session is the diagnostic. A companion checklist of the maturity model is available alongside this piece.

Trustworthy content at scale is a system you build over time. The teams that build it first will own their categories in the AI-search era.

FAQs

How is a content operating model different from a content marketing strategy?

Strategy decides what to create and why. The operating model is the system that produces it—who creates, how work moves through editorial checkpoints, where AI is allowed, and how output is measured against brand and compliance standards. They work hand-in-hand to help ensure the right content is produced..

Where can AI safely be used in regulated content?

AI is appropriate for research synthesis, first-draft scaffolding, metadata, and SEO optimization, always reviewed by a credentialed editor before anything is shared publicly. Final byline voice, factual claims in regulated subject matter, and any output that would publish without human review are off-limits. The test is simple: would a regulator or General Counsel accept the audit trail behind this sentence?

What does “credentialed” actually mean for a creator?

Identity verified, portfolio reviewed, subject knowledge tested where the topic demands it, and performance scored against editorial outcomes on every assignment. A credentialed creator is a real person, a verifiable expert who can be cited in the byline and defended in a compliance review.

Which metric matters most in the AI Overview era?

Share-of-voice in target SERPs and citation rate in AI Overviews. Raw traffic is a lagging and increasingly unreliable indicator as zero-click answers rise; what matters is whether the answer engine cites your brand as a credible source on the topics that drive your category.

The post The Operating Model Behind Trustworthy Content at Scale appeared first on Contently.

Key Takeaways

Productivity matters inside your team. The people above you care about pipeline, margin, defensibility, and quality of work.

• The CMO wants to know if AI-assisted content is helping with pipeline and brand growth. Just producing more assets isn’t enough.
• The CFO is focused on the cost per asset and contribution margin. Simply saving hours doesn’t always mean saving money.
• Legal and Brand Safety teams want to see a clear review process and audit trail. Calling something ‘enterprise AI’ is just marketing language and won’t hold up if they ask about real controls.

The 3x Faster Trap

The presentation was ready by Tuesday. After three months of pilot work, the key slide said, “We’re 3x faster with AI.” But by Thursday’s executive review, the CMO was distracted, the CFO asked about cost per asset, and the General Counsel wanted to know who approved the outputs. Hidden from view, a senior writer quietly wondered if she’d be affected by future layoffs.

Meetings like this are common when the topic of conversation is AI adoption. The pilot might have succeeded: turnaround time dropped from a week to two days, and the editing backlog disappeared. But when the main metric was presented to executives with different priorities, it didn’t impress them.

Productivity is not always a strong argument for more budget. To get headcount approved for next quarter, you need to pitch the program differently to each audience, using the metrics they care about.

Why “Productivity Gains” Fails as a Universal Pitch

Duke University’s CMO Survey says AI now powers 17.2% of marketing activities, up 100% from 2022, and leaders expect it to reach 44.2% in three years. This means that speed ceases to be an advantage when everyone is using the same tools. Speed just isn’t enough to address the concern of the key decision-makers who have to justify budgets, defend headcount, or maintain quality.

There isn’t much proof yet. A recent Haus survey of 500 senior marketing and finance leaders found that only about half feel confident explaining AI-driven ROI to their board.

There’s a bigger issue in every executive review. The CMO talks about pipeline and brand to the CEO. The CFO focuses on margin and capital efficiency for the board. Legal is getting ready for rules that don’t exist yet. Meanwhile, your writers are discussing their future among themselves. Each group has its own priorities, and your real job is to explain AI work in terms that each one understands.

Tailoring your message for each group is a necessary step. Here’s how:

What the CMO Actually Buys

What CMOs care about the most is that content drives revenue. Let that sink in. Now consider a CMO’s other top aims: building brand authority and growing the organization’s share of voice.

A CMO buys revenue-attributable content, brand authority, and category share of voice. Forrester’s recent research on B2B marketing accountability finds that eight of the top 12 criteria used to judge B2B marketing performance are based on proof of engagement — metrics such as marketing-sourced pipeline, marketing-influenced revenue, and lead volume. Notice how asset volume doesn’t make the list. So instead of “we shipped 4x more posts” show how they actually moved the pipeline.

Before the meeting, revise your message to highlight results for the CMO to share with the CEO. Example bullet points, if you have the data to support it, can include:

• AI-assisted briefs boosted MQL conversion on key topics
• The team continued publishing during the hiring freeze without sacrificing quality
• Time-to-publish for news stories fell to under two days
• The team also gained share of voice on three competitive launches that would have been missed

The slides that get a CMO’s attention show how AI-assisted tools can enhance revenue at each stage of the funnel. Showcase the growth in branded and category searches from one quarter to the next. Ideally, you can tell the story of how the team published time-sensitive stories more quickly than competitors. And be sure to spotlight the opportunities created and closed through our content efforts.

Don’t include word counts, drafts per writer, or details about the prompt library. These details don’t matter to the CMO, and spending time on them takes away from defending your program in the next budget cycle.

What the CFO Actually Buys

A CFO might congratulate you for saving 200 editor hours and even applaud the effort. Saving hours on the job they oversee is a big deal for editors, and any content team would love to achieve this. But to get the CFO to invest in your AI initiative, you need to show the financial benefit. CFOs care about costs that get better as the business grows and a clear profit margin, whether the spending is classified as operating or capital, fixed or variable.

They may want to know: How do you turn those saved hours into dollars? What’s the business value of time saved? Show that the fully-loaded cost per published asset dropped from $X to $Y, while quality stayed the same or improved. The marginal cost for each new long-form piece is now low enough to make new channels worthwhile. Spending on freelancers and agencies for basic content is going down each quarter, and that money is now funding the campaigns the CMO cares about.

The CFO will also want to know:

• The contribution margin for each channel after using AI
• The marginal cost for the next 100 assets
• Trends in vendor, freelance, and agency spending over the past four quarters
• The payback period for your tools and licenses

CFOs love cost savings, and they remember promises of headcount cuts. If you don’t plan to make these cuts, don’t mention them. If you need to talk about the impact on resources, say you’re moving editors to more valuable work and give specific numbers on the impact. Only promise savings that will stand up to an audit.

What Legal and Brand Safety Actually Buy

There are times when content needs to be reviewed by legal, especially in larger organizations and those in regulated industries. What concerns legal the most are IP risks, AI errors, and brand-voice issues.

When discussing AI with legal, focus on controls, evidence, and audit trails that legal can easily share with regulators. For example, having a clear review process in place before publishing anything helps ease their concerns.

To address their concerns, back up your evidence that AI delivers benefits with the following:

• A documented review process, source attribution, and a named person on the byline for every AI-assisted asset
• Records of prompts, versions, and reviewer approvals for as long as your data policy requires
• Your vendor agreement should cover IP protection and limits on training-data reuse

Legal and brand safety teams will come to the meeting with questions. Be prepared to answer them. They may ask the following:

• What are the IP indemnification terms in your vendor contract?
• Where are training-data exclusions and customer-content protections documented?
• Are you keeping logs of prompts, versions, and reviews as required?
• Who approves sensitive content before it’s published?

Legal is interested in metrics such as the percentage of assets that pass review on the first try, quarterly citation accuracy rates, the number of brand-voice issues each quarter, and how quickly problems are resolved.

The Stakeholder Cheat Sheet

Translating your message for each audience is key. Keep this in mind for your next budget review:

• For the CMO, emphasize outcomes linked to revenue, not just volume. Highlight pipeline-influenced revenue and share of voice.
• For the CFO, discuss loaded cost per asset and contribution margin, not hours saved. Focus on payback period and marginal cost.
• For Legal and Brand Safety, swap ‘enterprise AI’ for proof of a documented review process and audit trail. Stress citation accuracy and pre-publish pass rates.

Start with one pitch, then adjust your main metric for the people in the room. Watch the conversation shift, and the senior writer who’d quietly worried about layoffs at Thursday’s review walks out with one less thing to worry about.

Frequently Asked Questions

What single metric should I lead with for each stakeholder?

For the CMO, lead with pipeline-influenced revenue from AI-assisted assets. For the CFO, lead with loaded cost-per-asset, holding quality scores flat or improving. For legal, the percentage of assets passing pre-publish review on first submission. For the writing team, named-writer bylines retained on hero pieces and editor-hours redirected from cleanup to original reporting.

How do I defend headcount when the CFO assumes AI means cuts?

Reframe the program as redeployment, not reduction, and put a number on the leverage. Show editor-hours moving from cleanup into reporting and original interviews. Show contribution margin lifting on the channels that matter. Show freelance and agency spend on commodity output trending down. If headcount cuts aren’t the plan, don’t pitch them.

What evidence does legal actually want to see?

A documented review chain with named approvers. Retained prompt and version logs per the data retention policy. Citation accuracy sampled quarterly. A vendor agreement that includes IP indemnification and training-data exclusions. Translate everything into controls and audit trails.

The post Why “AI Productivity Gains” Is the Wrong Pitch for Every Stakeholder Above You appeared first on Contently.

Your content program has never produced more output, and senior buyers have never been less impressed. The dashboards look healthy: impressions are up, downloads are tracking, the newsletter is growing. Then a sales leader joins the QBR and reports that none of it is showing up in deals. The economic buyer never mentions the whitepaper that took your team six weeks to produce, and the VP forwarded something a competitor published instead.

The gap comes down to the kind of attention your work is competing for. A director will spend a few minutes scanning your content to decide if it’s worth their time. If your piece sounds like every other vendor explainer, it won’t grab their attention — this guide covers how to write content that will.

Why B2B Content Fails With Senior Buyers

Volume metrics like traffic to your content are flattering. But to whom? If audiences skim and forget it, then there’s issue: the asset isn’t built for the reader who needs to act on it.

Three failure modes show up repeatedly in executive feedback on B2B content:

• Feature-led messaging dressed as insight. The piece reads like a thought leadership argument at first. Within two paragraphs, it’s a product capability tour, essentially a brochure. Executives will disengage.
• Generic trend recaps. Summaries of a market shift your reader has already lived through, padded with charts they’ve already seen. Nothing to learn, nothing to disagree with.
• “Educational” content pitched at the wrong altitude. A 101-level explainer aimed at someone who runs the function. Teaching a CFO what working capital is, at any length, can end your credibility before you’ve made an argument.

Feature-led messaging dressed as insight. The piece reads like a thought leadership argument at first. Within two paragraphs, it’s a product capability tour, essentially a brochure. Executives will disengage.

Generic trend recaps. Summaries of a market shift your reader has already lived through, padded with charts they’ve already seen. Nothing to learn, nothing to disagree with.

“Educational” content pitched at the wrong altitude. A 101-level explainer aimed at someone who runs the function. Teaching a CFO what working capital is, at any length, can end your credibility before you’ve made an argument.

Your reader is opening the piece for one of three reasons: to validate a hypothesis they’re already forming, to surface a risk they suspect exists, or to pressure-test a vendor they’re considering. Pieces that don’t fit one of those jobs end up competing against everything else in the inbox for attention they probably can’t win.

Start From a Decision

The highest-leverage change you can make happens upstream of the draft. Many briefs name a topic, like agentic AI in finance, and ask the writer to find an angle. What comes back is a competent survey of the subject that says nothing your reader could act on.

Reframe the brief around a decision instead. Before any draft begins, your brief should answer one question: what decision should this content help the reader make, defer, or defend? That single shift changes what gets written. “A piece about agentic AI in finance” becomes “a piece that helps a CFO decide whether to fund an agentic finance pilot in this budget cycle, or wait twelve months.” Same topic, but now you have an argument to make.

Many of the executive decisions you can influence fall into a small set of recurring questions:

• Budget defense. Why this line item survives the next planning cycle.
• Build vs. buy. Whether to staff an internal effort or bring in a vendor.
• Risk of inaction. What it costs to wait another quarter.
• Vendor differentiation. Why one approach in a crowded category is meaningfully different.

Budget defense. Why this line item survives the next planning cycle.

Build vs. buy. Whether to staff an internal effort or bring in a vendor.

Risk of inaction. What it costs to wait another quarter.

Vendor differentiation. Why one approach in a crowded category is meaningfully different.

Map every brief to one of those questions before writing. Then run the “so what” test: state your thesis in one sentence and ask whether a senior reader would respond with “obvious,” “wrong,” or “interesting.” Only the third response is worth the draft.

Translate Product Insight Into Executive-Relevant Point of View

Your subject-matter teams sit on the most valuable material you need to engage decision-makers: what your product actually changes about how customers operate. The translation problem is that this material almost always arrives in feature language, and “we added X capability” reads as a release note and gets treated like one.

In Edelman and LinkedIn’s 2025 B2B Thought Leadership Impact Report, 73% of target decision-makers said thought leadership is more effective than traditional marketing or sales materials at demonstrating a vendor’s value. The translation work is what closes that gap.

Link the capability to what matters to executives. They care about the business impact. Take a new automation feature – it’s like a shiny new tool. But is it really better than what’s already out there? Is it more user-friendly? You need to show how it drives business results. For a CFO, you could say the finance team wraps up the books two days sooner. For a CMO, you might say the content quality stays high because a person is always involved. Pick the outcome that resonates with your audience, and make that clear in your content.

The same principle applies to evidence. Industry stats every competitor is also citing read as filler the moment a senior reader sees them. Internal benchmarks, anonymized customer outcomes, the patterns you see because of where you sit in the market: this is the material that builds trust, because no one else can publish it.

Take a position when the evidence supports one. The same Edelman-LinkedIn report found that 86% of hidden decision-makers, the internal influencers from finance, legal, operations and similar functions, favor perspectives that challenge their assumptions over content that validates their existing thinking. There are real cases where “it depends on your organization” is the honest answer, since some variables genuinely differ across companies. But if your evidence supports a verdict, lead with it and name the conditions that would change it.

Structure for Skim-First, Read-Second

The most valuable thing for decision-makers is time. Assume your reader doesn’t have any. They will skim before deciding whether to actually read. So, build the piece for the skim, and if you get the them to read it fully, then consider it a bonus.

A few structural moves carry most of the load:

• Lead with the conclusion. Your actual claim should live in the first 100 words. Setup, hook, and throat-clearing earn their place later or come out entirely. Long-form structure works when the argument is sharp.
• Use opinionated subheads. A heading like “Why B2B content fails with senior buyers” tells the skimmer what the section will argue. Vague placeholder titles like “Common content challenges” add nothing your reader can act on. Your bolded scaffolding should read as an outline of the argument the piece is making.
• Make pull quotes hold meaning on their own. If the highlighted line is a vague platitude, the visual weight is wasted. The pulled line should be the sentence your reader would underline.

Lead with the conclusion. Your actual claim should live in the first 100 words. Setup, hook, and throat-clearing earn their place later or come out entirely. Long-form structure works when the argument is sharp.

Use opinionated subheads. A heading like “Why B2B content fails with senior buyers” tells the skimmer what the section will argue. Vague placeholder titles like “Common content challenges” add nothing your reader can act on. Your bolded scaffolding should read as an outline of the argument the piece is making.

Make pull quotes hold meaning on their own. If the highlighted line is a vague platitude, the visual weight is wasted. The pulled line should be the sentence your reader would underline.

The cuts matter just as much. Definitions of terms your audience already runs, history-of-the-category preambles, and especially any sentence beginning with “in today’s fast-paced business environment” should come out before the draft goes anywhere. Senior-level decision-makers tend to read that prose as a signal that the rest of the piece won’t respect their time. Onto the next one.

Voice and Credibility Signals That Earn Executive Trust

Tone can quietly lose your reader. You aim for authoritative and land on aspirational, which leaves the piece sounding more like a lecture and less like a peer in the room. Senior readers can tell inside a paragraph. Peer-level voice assumes your reader already operates at the altitude you’re discussing; anything that explains that altitude back to them signals you’re reaching.

Credibility signals matter. Just be sure to choose the right ones. Being as specific as possible tends to work the best. According to the 2025 Edelman-LinkedIn report, 81% of target decision-makers say a hallmark of high-quality thought leadership is that it helps them uncover challenges or opportunities they hadn’t recognized. A named executive contributor offering a specific, uncomfortable opinion does work in your piece that no other element can replicate. Generic analyst citations every competitor is also running can read as filler. Specific numbers tied to named customer outcomes are what get the piece read; “customers see significant improvements” is what your reader has been trained to skip.

A short list of marketing tells will undo the rest of your work no matter how strong the argument is:

• Superlatives you can’t substantiate, like best-in-class, world-leading, or unparalleled.
• Vague positioning words like “leading” used without a reference.
• CTA language that breaks the editorial frame mid-argument (“and that’s why our platform…”).
• Too many qualifiers that distract and soften the main point of the piece.

Superlatives you can’t substantiate, like best-in-class, world-leading, or unparalleled.

Vague positioning words like “leading” used without a reference.

CTA language that breaks the editorial frame mid-argument (“and that’s why our platform…”).

Too many qualifiers that distract and soften the main point of the piece.

The Pre-Publish Executive Gut Check

Before any executive-targeted piece is published, run the draft against this checklist.

• Your thesis is extractable from the first 100 words and makes a claim a reader could disagree with.
• The piece answers a specific “so what” question for your buyer: budget defense, build vs. buy, risk of inaction, or vendor differentiation.
• At least one named contributor, customer, or first-party data point appears above the fold.
• Specific numbers replace vague claims wherever the evidence allows.
• The voice reads as peer-level, with no explanation of concepts your audience already runs.
• No superlatives, no “leading,” no in-today’s-fast-paced-world opening.
• A skimmer reading only the subheads and bolded lines walks away with your argument.

Your thesis is extractable from the first 100 words and makes a claim a reader could disagree with.

The piece answers a specific “so what” question for your buyer: budget defense, build vs. buy, risk of inaction, or vendor differentiation.

At least one named contributor, customer, or first-party data point appears above the fold.

Specific numbers replace vague claims wherever the evidence allows.

The voice reads as peer-level, with no explanation of concepts your audience already runs.

No superlatives, no “leading,” no in-today’s-fast-paced-world opening.

A skimmer reading only the subheads and bolded lines walks away with your argument.

Measuring Influence

Measurement is where many executive content programs lose the internal argument. Pageviews and time-on-page describe behavior on the page. What happens after your reader closes the tab can matter more for enterprise impact than anything you measure on the page itself, and those metrics leave that part unmeasured. The Content Marketing Institute’s 2025 B2B Content Marketing Benchmarks report found that 56% of B2B marketers cite difficulty attributing ROI to content as a top measurement challenge, with the same share saying they struggle to track customer journeys.

A more honest set of signals tracks how content moves through your buying process:

• Asset surfacing in deal cycles. Did the piece appear in a sales conversation, a discovery call, or a procurement review?
• Executive-level shares. Was it forwarded inside the buying account, especially upward?
• Sales-cited assets. Which pieces does your field team actively pull into outreach, and which do they avoid?
• Account engagement lift. Did engagement across the target account rise after the piece landed, even if the original reader stayed anonymous?

Asset surfacing in deal cycles. Did the piece appear in a sales conversation, a discovery call, or a procurement review?

Executive-level shares. Was it forwarded inside the buying account, especially upward?

Sales-cited assets. Which pieces does your field team actively pull into outreach, and which do they avoid?

Account engagement lift. Did engagement across the target account rise after the piece landed, even if the original reader stayed anonymous?

Instrumenting that view takes a real working relationship with sales. Build a habit of asking deal teams which assets showed up in won and lost cycles, and feed those answers back into your editorial calendar.

Content as a Boardroom Asset

Does the content move senior buyers focus on producing work that’s defensible in front of the specific person it was written for? Every piece should answer yes to that before it ships.

According to Forrester’s 2025 Buyers’ Journey Survey, 64% of business buyers at the manager level and above are now Millennials or Gen Z, a digital-native cohort Forrester describes as having less patience for generic outreach. The content that holds up tends to be the content that earns the first hundred words and rewards them for the rest. Everything else can keep generating impressions while losing deals.

FAQ

Why doesn’t my B2B content get traction with executives?

Many exec-targeted pieces miss because they’re built around topics rather than decisions, and they summarize information senior buyers already have. Reframe each brief around a specific decision your reader needs to make, defer, or defend, and lead with a defensible point of view in the first 100 words.

How long should thought leadership for executives be?

Density matters more than length. Your thesis should be extractable in the first 100 words regardless of total word count. Long-form or short-form can both land, as long as every section earns its place and the argument stays sharp. The failure mode tends to be medium-length pieces that hedge.

What’s the difference between executive content and standard B2B content?

Executive content takes a defensible position when the evidence allows, leans on first-party data and named contributors, and is structured to be skimmed before it’s read. Standard B2B content surveys a topic neutrally, cites recycled industry stats, and buries the argument under setup, which is why it can generate traffic without influencing pipeline.

How do you measure whether content actually influenced a decision maker?

Track deal-cycle signals beyond pageviews: was the asset shared internally inside the buying account, surfaced by sales in a live deal, or referenced in account engagement lift after it published? Build the habit of debriefing won and lost deals with sales to find out which pieces actually showed up, then use that input to shape your editorial calendar.

Key Takeaways

• Start from a decision. Executive-grade content earns attention by helping a buyer make, defer, or defend a specific call. When briefs specify a topic and ask for a particular angle, they usually end up being broad overviews of the subject.
• Lead with a defensible point of view when you have one. Readers in high-level positions tend to connect with content that makes a point. When you present both sides of an issue, it can sound like you’re sitting on the fence.
• Structure for the skimmer. Your thesis should be extractable from the first 100 words, even when the piece runs long.
• Use proprietary signal. First-party benchmarks and customer outcomes are more effective than industry surveys.
• Measure influence, not impressions. Pageviews can understate enterprise impact. Watch deal-cycle behavior: shares inside accounts, sales-cited assets, content that surfaces in pipeline conversations.

Start from a decision. Executive-grade content earns attention by helping a buyer make, defer, or defend a specific call. When briefs specify a topic and ask for a particular angle, they usually end up being broad overviews of the subject.

Lead with a defensible point of view when you have one. Readers in high-level positions tend to connect with content that makes a point. When you present both sides of an issue, it can sound like you’re sitting on the fence.

Structure for the skimmer. Your thesis should be extractable from the first 100 words, even when the piece runs long.

Use proprietary signal. First-party benchmarks and customer outcomes are more effective than industry surveys.

Measure influence, not impressions. Pageviews can understate enterprise impact. Watch deal-cycle behavior: shares inside accounts, sales-cited assets, content that surfaces in pipeline conversations.

The post How to Write Content That Lands With Decision Makers appeared first on Contently.

Writers, editors, and designers form the backbone of a content team. Content calendars get built around the team’s production capacity, and since time is always tight, AI has become the obvious path to faster output.

AI allows any marketing team with a credit card and a prompt library to fill next quarter’s calendar in just days. HubSpot’s 2026 State of Marketing report found that 86.4% of marketing teams use AI, with 42.5% reporting extensive use for content creation. AI can be used for tasks like drafting, outlining, summarizing, and editing—all in minutes.

The result is that teams have more drafts than they can review. More pieces are ready for approval. Teams now have more content than they can manage. But who has the time to make sure every piece doesn’t sound like every other AI-generated draft out there?

The person who decides what gets published and what stays hidden controls the entire process. This role is often called a content manager or editorial lead in many organizations. These roles have traditionally been hired to keep the calendar full, manage freelancers, and move pieces through review. These job descriptions focus on throughput: how much content is produced, how fast, and for which channel.

But many organizations still write the job descriptions for these roles as if it were 2016. What most teams need now is a managing editor—a role defined by quality and taste, not throughput.

Faster Work Still Needs Better Judgment

With AI, what once required a week from a team can now be accomplished in an afternoon. But there isn’t a simple plug-and-play solution for content creation because every organization does it differently. For example, Klarna successfully reduced sales and marketing agency expenses while boosting campaign output. But these improvements were not solely due to AI. They stemmed from revamping image production, copywriting, and agency workflows first. AI became effective only after the surrounding system was enhanced.

In other words, AI should be integrated into effective human processes, rather than the reverse. At Charter’s AI Summit, Microsoft’s Katy George noted a shift: “We used to pay attention to adoption, now we just pay attention to performance.” The perspective on AI adoption strategy is relevant for content operations, as increased speed leads to higher volume. But this shift creates additional pressure on those responsible for quality. With each additional draft, risk is introduced. And every piece that falls short of the standard expected by consumers can lower how a brand performs or is perceived.

The fundamental questions behind each piece remain unchanged.

• Is it worth publishing? Does it reflect our voice?
• Does it add value or detract from our work?
• Is the argument compelling or merely easy to digest?
• Will we be proud of it a year from now?

For content teams, AI is being deployed faster than it’s being governed. EY’s latest survey found that more than half of AI projects in departments are happening without proper supervision, and almost four out of five leaders say they can’t keep up with the business risks that come from using AI too quickly. What often results is an inconsistent voice and weakened editorial judgment and brand standards.

At Contently, the managing editor role is what closes that gap for our clients, keeping work on-brand and on-standard as output scales. Six functions define the role. The managing editor:

• Sets the publishing bar. Defines what “good” looks like for each asset type, channel, and audience, establishing the rubric against which everything else is measured.
• Stewards the brand voice. Voice—the one thing a competitor can’t copy—is often one of the first things to drift with AI. That drift has to be caught early, before it becomes a pattern.
• Draws the authorship line. Decides where and when AI drafts are appropriate and where it never touches the page. Those decisions are live judgment calls that evolve alongside the tools, rather than a static policy filed away in a wiki.
• Carries institutional memory. Knows what’s been said, what didn’t land, what’s already been tried. Only a human can hold that kind of knowledge over the years.
• Translates standards into briefs that writers and tools can actually execute. This is the operational bridge between taste and production.

What You Don’t Publish Is Doing the Real Work

Here’s a lesson many teams have learned as AI adoption continues in content operations: when production is cheap, pieces that never see the light of day do the real work. Why? Because it gives those pieces that are most on-brand the spotlight. A publication that ships less but with a clear point of view builds a strong readership over time. In contrast, a publication that releases more to fill a calendar loses trust with every forgettable post. Readers quickly notice the difference.

Voice consistency is a valuable asset. What a brand shares defines it, multiplied across many touchpoints. Teams that feel this most are those who’ve seen a strong voice fade due to high volume. Over a year or two, readers may stop recognizing it.

The managing editor focuses on decision-making, not just production. They choose what the publication will endorse and, equally important, what it won’t.

What to Hire For

Seven traits to look for:

• Voice stewardship. Hears drift early, before it becomes a pattern.
• AI fluency for triage. Knows which drafts to trust, which to send back, and which to scrap, and can explain why in each case.
• Brief translation. Turns editorial standards into operational criteria that writers and tools can run against.
• Institutional patience. Treats the publication as a long-term project rather than a quarterly campaign.
• Cross-functional authority. Holds the line with marketing, product, legal, and leadership without relitigating every piece.
• A reader’s ear. Can tell when a sentence is fluent but hollow, or technically correct but off-key.
• Willingness to be unpopular internally. The job requires saying no to people with more organizational weight than the editor has, so hire someone who’s done it before.

What This Looks Like in Practice

Contently has been serving clients for years, even before the current volume issue. Managing editors work closely with in-house teams. They ask for pitches, assign briefs, and edit each piece to fit the brand’s voice and strategy.

The effectiveness of this setup lies in its structure. One person makes the final call, ensuring each piece aligns with the client’s strategy.

Today, anyone can create content. What will define a brand in five years is a unique point of view that endures through the AI era. That endurance will separate one publication from another as volume becomes free and quality remains rare.

However, survival isn’t guaranteed. It relies on someone in the organization who is paid, trusted, and empowered to decide what gets published. Most content teams have writers and tools. What they lack is a dedicated decision-maker, because judgment will be the key constraint in 2026 and beyond.

Frequently Asked Questions

What does a managing editor actually do that a content manager doesn’t?

A content manager is usually measured by throughput—pieces shipped, deadlines hit, calendar filled. A managing editor is measured by judgment: what made the cut, what didn’t, and whether the publication still sounds like itself a year in. The two roles overlap in operations but diverge in authority.

Why does this role matter more now than it did five years ago?

Because production is no longer the bottleneck. When any team can generate a month of drafts in an afternoon, the constraint shifts to deciding what’s worth publishing. That decision is where brand voice lives or dies.

Can AI replace a managing editor?

No. AI can draft, outline, and summarize, but it can’t hold years of context about what a publication has said, what’s landed, and what sounds off-brand. That kind of institutional memory is still a human job.

What’s the single most important trait to hire for?

A reader’s ear—the ability to tell when a sentence is fluent but hollow or technically correct but off-key. Most of the other traits can be taught.

The post The #1 Role Your Content Team Needs in 2026 Is a Managing Editor appeared first on Contently.

Then, somewhere around the 18-month mark, quality dips. Deadlines become aspirational, at best. The aims that felt so clear at launch become harder to articulate. And eventually, the whole effort stalls.

According to the Content Marketing Institute, only 22% of marketers rate their B2B content marketing as extremely or very successful, while 58% report only moderate results. A key differentiator: 62% of organizations that do succeed have a documented content strategy aligned with business objectives.

The drop-off in content marketing happens because sustaining quality, voice, and output over years is challenging — through leadership changes, budget cycles, and platform shifts. Content culture is what separates successful programs from those that fade: one puts the human element at the center of everything.

Here are three pillars to building an effective content culture:

Pillar #1: A Mission Everyone Can Feel

A content team might have a strategy, which describes what you will make and when.

But does it have a mission?

A mission is a shared north star. It is part of your strategy that explains why you create content. It includes answers around what the brand believes, what the audience genuinely needs, and where those two things meet. Teams that articulate that “why” clearly enough that every person on the team, from senior strategists to occasional freelancers, can feel it in their work are the ones that stay coherent across hundreds of pieces and dozens of contributors.

Without a mission, content tends to drift. Individual pieces may be well-executed, but they start to feel like disconnected campaigns rather than a point of view. Over time, this erodes trust. CMI found that 97% of content marketers have a documented content marketing strategy. But 42% of marketers point to a lack of clear goals as the root cause of underperformance.

A mission requires human judgment about what the brand stands for, what the audience is actually trying to figure out, and what the brand has earned the right to say. It is built into the culture.

Pillar #2: Content Belongs to Everyone

Content programs are often tied exclusively to the marketing team, which produces good work and publishes consistently. But then it watches nearly helplessly as the content underperforms. The reason is that content should be a shared responsibility across the organization.

Product teams consider content implications when planning new features. Sales teams surface the questions that should be driving editorial. Customer success teams flag the moments when content actually changes a customer’s behavior. Leadership talks about content the way it talks about other strategic assets.

According to Forrester, many executives (82%) think their teams are aligned, but based on the feedback from B2B sales and marketing professionals in the trenches, only 8% of organizations actually have strong alignment between sales and marketing.

Building a cross-functional content program requires people who can translate content value into the language of finance, product, and sales — and who can do it repeatedly, in the rooms where decisions actually get made.

Pillar #3: Sustainable Process Over Heroic Sprints

There’s a sense of urgency in some content cultures, where every deadline is a sprint and every major piece is a scramble. This approach can produce great work in bursts. But is it the mark of a great content culture?

When the process consistently asks more than it gives back, then it’s the process that’s the problem. A 2025 study found that 52% of content creators have experienced career burnout, and 37% have considered leaving the industry because of it. Among full-time creators, the top drivers were creative fatigue (40%) and demanding workloads (31%).

Lasting content programs build something more deliberate: editorial calendars that provide genuine lead time, workflows with clear handoffs, feedback loops that actually close, and enough breathing room that creative work can be creative.

Sustainable content practices offer the best options for talent. It allows teams to publish reliably, at a quality standard everyone can meet. Content leaders who implement sustainable creative processes respect the people doing the work and acknowledge that creativity needs space to flourish.

How To Bring It All Together

A shared editorial mission requires human judgment. Cross-functional buy-in requires human relationships. A sustainable creative process requires human empathy. Each of the pillars that makes content culture durable depends on something that cannot be outsourced to a platform or automated away.

That is where Contently’s investment has always been — not in replacing those human elements, but in making them work better. The network of creators Contently has built is a community grounded in real relationships between brands and the writers, designers, and strategists who know their audiences. Strategic services pair brands with editorial experts who bring genuine judgment to content planning. The technology is built to serve the people using it, not the other way around.

The brands building content cultures that last are not the ones chasing the newest tool or the highest volume. They are the ones investing in the people who keep the mission alive, who build belief across the organization, and who treat creators as collaborators rather than production resources.

Before you evaluate your next platform or revisit your content calendar, consider the three pillars.

Does your team have a shared mission that goes beyond what you are publishing and gets at why?

Do you have genuine buy-in from outside marketing?

Do you have a process that respects the creativity it is asking for?

If any of those answers is no, that is where to start.

Frequently Asked Questions

What is a content culture, and why does mission matter?

A content culture is the shared set of values, processes, and commitments that keep a content program producing meaningful work over time. While a content strategy focuses on what to publish and when, a content culture with a mission addresses the human infrastructure, which helps retain talent, maintain editorial consistency, and build lasting audience trust.

How do you get buy-in for content marketing from teams outside of marketing?

Build relationships in the rooms where decisions get made and speak the language of these outside teams. For example, show sales teams how content shortens deal cycles. Product teams will respond to how editorial feedback surfaces feature requests. Leadership wants to see how content drives measurable pipelines and retention metrics. The key is making content a shared capability rather than a marketing-only function.

How can content teams avoid burnout while maintaining a consistent publishing schedule?

Build editorial calendars with genuine lead time, establish workflows with clear handoffs, and create feedback loops that actually close. A reliable cadence at a quality standard the whole team can sustain will always outperform occasional brilliance followed by missed deadlines. Give creative work the breathing room it needs, and treat your editorial calendar as a support system rather than a pressure mechanism.

Most content programs stall within 18 months, but the ones that last share three pillars. Explore the importance of the human element in your content program.

The post The Content Cultures That Last Have One Thing in Common appeared first on Contently.

Polished copy is easy now with AI. You can quickly write blog posts, social campaigns, video scripts, thought leadership essays, white papers, and podcasts at scale across every imaginable format and channel. And yet, after the content is published, it’s quickly forgotten.

What now separates authentic, smart content from forgettable (and sometimes regrettable) non-strategic content is taste.

When every piece of content imaginable is easy to make, deciding what not to make becomes the real work. The brands pulling ahead of everyone else are the ones making taste a core element of their content creation process.

Taste describes the ability to consistently distinguish what fits from what doesn’t. It’s an exercise in judgment about what deserves to exist in the first place. Taste is a skill that enables content teams to determine what’s worth an audience’s time from what merely fills a content calendar.

The Judgment Call

It used to be that content teams, measured by their ability to produce faster, more efficiently, at higher volume, had the advantage. But this edge has dulled as content has become a commodity. It’s simply not good enough to have “good enough” content.

Content that can be easily produced by tools and systems is competent and fluent by default. What’s often missing is judgment.

Judgement can’t be commoditized. Judgement is thinking. It’s like when a content team takes a dozen viable ideas and chooses only the three worth pursuing. When a person instinctively reframes a piece and trims it down so that what’s being communicated is genuine and advances the message, they’re making a judgment call.

Editors have always known what’s worth making and what’s best left out. The sharpest content teams are taking their cue from editors and gaining a competitive edge.

More Content Isn’t The Same as More Impact

Most organizations default to pursuing more content. More blog posts. More thought leadership. But publishing everything without taste doesn’t necessarily lead to better results.

Brands also risk diluting their message when they overload their audience with content. According to Accenture, 74% of empowered consumers walked away from purchases simply because they felt overwhelmed. Content overload works the same way. What readers want is clarity. If they get that from the content they read, they stay and reward brands with their trust. Bore or bombard them with content, and they often leave quietly.

The trap of producing more content is seductive because the metrics lag behind the damage. Publishing more can keep the pageviews and open rates looking fine for months, even as readers slowly lose interest. By the time the decline shows up in the numbers, the problem has been compounding for a long time—because nobody was asking whether any of it was worth making.

What “Taste” Actually Means

Taste sounds inherently subjective. You either have, or you don’t. But in practice, it’s far more concrete than its reputation suggests.

Content guardrails tell you what to do or not to do. For example, brand guidelines tell brands how to sound. Taste takes on a harder question: What’s actually worth making?

Creative taste involves a clear sense of what fits and what doesn’t. Organizations that have it know their own voice well enough that they don’t need to watch what other brands are doing (though your content is also competing for a spot in AI-generated answers).

Brands using taste to their advantage accept that not every audience segment will be served by every piece. They also know that there’s a payoff to being opinionated when it serves the strategy, because the safest content is often the least memorable.

Codifying Taste Without Killing Creativity

Taste can be scalable when shared, but avoid the temptation of turning “taste” into a checklist or formula. How can you define taste in a structured way so that creativity flourishes?

First: Show, don’t tell. Nothing communicates taste faster than showing people what good looks like and what it doesn’t. Collections of the brand’s best work, annotated with notes on why it works, give teams a reference point far more useful than abstract principles alone.

Second: Set clear principles. Principles can help lock in content teams to what taste is, as long as the principles are clear. An example, “We explain, we don’t lecture,” sets a standard while allowing for interpretation. Principles point content teams in a direction. But they also need freedom to experiment and adapt messaging without going off-brand.

The balance that works is shared standards plus human discretion. The system provides the framework. The people provide the judgment.

Editors Were Right All Along

As the volume of potential content grows, the need for experienced judgment grows with it. Senior editors and creative directors are filters. They’re the members of the team who look at a week’s worth of planned output and ask whether it actually says anything new.

Senior editorial leaders don’t just catch errors or enforce style guides; they decide whether content is worth sharing with the world. They set the standard for what makes sense while serving as a bridge between strategy and creative execution.

From a business standpoint, investing in strong editorial leadership helps manage risk. Any piece of content that falls short costs the company something, such as audience attention, brand reputation, or internal resources. Leaders who prevent mediocre work from being published help protect the value that’s hard to recover once it’s lost.

Taste Offers A Real Creative Advantage

The future of content belongs to teams who can say, with confidence, this is us, this isn’t, and this is worth your time.

Content creation will get easier as tools get better. Taste remains the throughline that keeps brands coherent, credible, and distinct.

The volume of content will keep increasing. But the organizations that treat editorial judgment as a strategic asset will be the ones whose content still matters five years from now.

Building that kind of editorial capability doesn’t happen by accident. It takes experienced leadership, shared systems, and a commitment to quality over quantity. Connect with Contently to work with expert managing editors who can help your team develop the taste and judgment that turns content from output into advantage.

Frequently Asked Questions (FAQs):

How do I build “taste” into my team if we don’t have a senior editor?

You may not have a senior editor yet, but you can still take key steps to establish “taste” guidelines for your team. First, gather five to ten pieces that your team thinks are their best work and note why each one succeeded. This will be your “taste” reference set. Next, create two or three clear editorial principles to guide decisions, but flexible enough to encourage creativity. Keep updating the reference set and refining the principles over time, revisiting them every quarter.

How do I convince leadership that publishing less content is the right move?

Leadership will likely want more. So offer a new perspective—too much content can weaken the brand and reduce trust. Also, producing too much can stretch resources thin, resulting in team burnout. Then connect the idea of less content to real results, such as the pipeline, engagement, or earned media generated in the last two quarters. Compare that data to the total output. Usually, a small portion of content drives most of the results. This data helps make your case.

How long does it take to see results after shifting from volume to judgment?

Plan for one full quarter. In month one, review past work and set standards. The team uses them on new projects in month two. By month three, expect results: better engagement, fewer revisions, and clearer priorities. This information will give your team a stronger understanding of what’s worth creating. Be sure to agree on this timeline with leadership before starting.

The post The Future of Content Belongs to the Tastemakers appeared first on Contently.

That model is breaking down.

In AI-driven discovery environments, your content is no longer competing with other brands in the traditional sense. Instead of vying for attention and eyeballs, now you’re competing to show up in the language, examples, and assumptions AI systems use in their answers.

The first step is to survive the summarization process. Here are some tips on how to write for the “idea ecosystem.”

The New Model

When someone asks a system like ChatGPT, Perplexity, or Google’s AI Overviews a question, the system constructs an answer assembled from many sources at once. Your content enters that system as raw material, and exits recomposed alongside other inputs.

What matters, then, is whether any part of your brand’s messaging shapes the response the system generates. The pinnacle of success is making such an impression on one of the major LLMs that you do get cited by name. A second-best outcome is seeing your terminology or logic show up consistently in AI-generated answers, even if your brand doesn’t.

While on its face, “no attribution” sounds like a raw deal, being cited by AI, even tangentially, can make a difference in multiple stages of the sales funnel. If AI repeatedly explains a category using your logic, buyers may later:

• recognize your language on your site
• hear your pitch as familiar rather than promotional
• perceive alignment instead of persuasion

When it comes time to make a decision, this familiarity can make your product or service feel like the obvious fit.

What Actually Survives AI Compression (and What Doesn’t)

Ideas that survive compression tend to function as anchors; they give the system something stable to organize around. Examples might include a clear model for thinking about a problem, or an original benchmark that gives the system a reference point. Content that introduces structure or, better yet, new and valuable data is a boon. (This is one of the reasons we’re seeing a rise in branded benchmark reports and flagship research these days.)

Generic content rarely provides that. Familiar advice and widely repeated tips dissolve into the background because they don’t change how the system understands the topic.

A sharply argued position, on the other hand, gives the system something to work with. Instead of blending seamlessly into everything else, it helps organize other inputs. This is why original language matters—but not as ornamentation. Distinct terminology can make an idea easier for AI to find and surface.

How Marketers Need to Rethink Content Strategy

Content can no longer be treated as an asset that drives traffic; it needs to function as a source of durable ideas that persist across platforms and summarization layers. That means prioritizing clarity over cleverness. A clear definition or straightforward, compelling original data point will travel farther than a witty headline.

It also means investing in strong framing. If you can name a concept, structure it, and make it easy to restate accurately, you increase the odds it will persist.

It means using memorable language: Not buzzwords or jargon, but precise, specific phrasing that’s hard to replace with a generic equivalent.

And it means recognizing that safe, consensus-driven content is the most vulnerable to erasure. If your article says what everyone else is saying, it contributes nothing distinct to the compression process. It becomes filler.

This is uncomfortable for brands that have built content strategies around avoiding risk. But in an environment where AI systems blend dozens of voices into one, the riskiest move is to have no distinct voice at all.

The New Competitive Set: Ideas

AI doesn’t care about brand equity the way human readers do. A Reddit comment with a sharp insight can outcompete a polished whitepaper if the insight is more distinct and easier to compress; an academic study with clear findings can overshadow your thought leadership if the findings are more specific.

This levels the playing field in some ways, but it also raises the bar.

If your content strategy was built for the old model, now’s the time to audit. Here are a series of questions to ask when evaluating existing and planned content for AI search:

• If this article were compressed into a single sentence, would our core idea survive? Would our framing survive? Would our name?
• Is this content safe or generic? How can we make it stand out?
• What can we say about this topic, product, or sector that nobody else is saying? What language can we use that’s distinct, or what point of view can we “own”?
• If a buyer encountered this idea elsewhere later, would they recognize it as ours?

Idea persistence is the new metric. It’s time to start measuring for it.

Learn how Contently helps brands build content strategies designed for clarity, resilience, and long-term impact. Get in touch.

Frequently Asked Questions (FAQs):

Does this mean SEO no longer matters?

No. SEO still plays a role, especially for discovery and authority signals. But it’s no longer sufficient on its own. Ranking well doesn’t guarantee influence if your ideas disappear during summarization.

How can we tell if our ideas are influencing AI answers?

You won’t see a single metric. Signals tend to be indirect: recurring language in AI-generated responses, familiar framing appearing across tools, or prospects repeating your terminology in conversations. Influence shows up over time, not in dashboards.

Is AI attribution realistic for most brands?

It depends on the category and the role your content plays in the buying journey. Direct citation does happen, especially in product-led or comparison-driven searches, but it’s inconsistent and difficult to control. For most brands—particularly those operating in crowded or concept-driven categories—the more reliable goal is idea adoption. Attribution should be treated as an upside, not the baseline measure of success.

The post Your Content Isn’t Just Competing With Other Brands Anymore appeared first on Contently.

So when a customer asks your support chatbot a routine question and the bot confidently cites that guide as current policy, the advice is wrong. Your support team now has to explain why an official brand answer is outdated.

It’s a scenario that’s becoming more and more common as AI makes its way into customer service, e-commerce, and search. Since LLMs pull from published brand materials to answer user questions and shape buying decisions, outdated or incomplete content can carry severe consequences. According to The Conference Board’s October 2025 analysis, 72% of S&P 500 companies now identify AI as a material business risk, up from just 12% in 2023.

Content teams are feeling the pressure. Marketing collateral that used to be about engagement and reach now carries far more responsibility.

Why This Shift Is Happening Now

AI systems don’t distinguish between your latest product update and a blog post from 2019; they treat all indexed content as equally valid source material.

This creates a compounding problem. When ChatGPT, Perplexity, or Google’s AI Overviews pull from your content library, disclaimers disappear, dates vanish, and nuance evaporates.

This is what leads to scenarios like the one described at the top of this piece. Here are a few other examples of how content can go awry:

• A 2023 pricing page informs a sales conversation with a chatbot, and the customer pushes back when it becomes clear the quoted numbers no longer apply.
• A deprecated feature appears as a live offering in Google’s AI Mode, leading to confusion during customer onboarding.
• An old compliance explainer is surfaced on ChatGPT as guidance, even though the underlying regulation has changed. The company is forced into a reactive audit.

For regulated industries, the exposure carries profound risk: Financial services firms might face SEC scrutiny, and healthcare organizations that have to navigate HIPAA implications could find themselves correcting patient-facing guidance after the fact.

The New Risks Content Teams Are Absorbing

Content teams didn’t sign up to be compliance officers, but the risks have arrived anyway.

Consider what happened to Air Canada a couple of years ago: In a 2024 ruling, a British Columbia civil tribunal found the airline liable after its website chatbot cited incorrect information about bereavement fares, promising a discount that did not exist under current policy. When Air Canada refused to honor the discount, the customer pursued a claim and won. The tribunal ruled that the company was responsible for the chatbot’s statements, regardless of how or where the information was generated. What began as outdated guidance surfaced through AI ended as a legal and public accountability issue.

There are a few buckets that AI-related content risk tends to fall into. Here are some common failure modes to be wary of:

• Outdated information as “current” fact. AI systems resurface archived content without timestamps, so policies, pricing, or product details that no longer apply are delivered as if they were up to date.
• Inconsistent messaging across content types. Your blog says one thing, your help docs another, and your landing page a third. AI systems amalgamate those contradictions into confident answers that may be completely off base.
• Nuance and disclaimers stripped away. Legal caveats and contextual qualifiers rarely survive AI summarization. The careful language your legal team approved gets compressed into declarative statements.

McKinsey’s 2025 State of AI survey found that 51% of AI-using organizations have already experienced at least one negative consequence from AI deployment, with inaccuracy the most commonly cited issue. This represents structural exposure that content teams now own, whether they planned to or not.

Why Most Teams Aren’t Set Up for This Role

Content teams evolved to optimize for different metrics: speed, volume, engagement, traffic. But in many cases, the established workflows that serve those goals actively work against accuracy governance: Publishing calendars prioritize velocity, and editorial reviews tend to focus on voice and clarity. Legal approval processes that were designed for campaigns (discrete, time-bound assets) might not extend to evergreen content libraries that AI systems mine indefinitely.

And ownership gets murky fast. Who’s responsible for updating a three-year-old blog post when regulations change? Who audits help documentation when product features evolve? In most organizations, that accountability doesn’t exist.

Content teams sit at the center of this vacuum, creating the assets AI systems consume, without the mandate, tools, or headcount to manage the downstream risk.

How Teams Are Adapting Without Slowing Down

The organizations getting this right are building what we call the Content Risk Triage System — four interlocking practices that maintain velocity while managing exposure.

1. Tiered review models. Not every piece of content carries equal risk. A best practice is to classify content by exposure: high-stakes claims (pricing, compliance, capabilities) route through legal review, standard editorial content moves faster with SME sign-off, and low-risk assets publish with editorial approval alone.
2. Content risk scoring. Assign risk classifications at the brief stage. Content touching regulated topics, making quantifiable claims, or likely to be cited by AI systems should get flagged for additional verification before drafting begins.
3. Clear ownership for content lifecycle. Designate owners not just for creation but for ongoing accuracy, e.g., one person who owns the quarterly audit of evergreen content and another team member who manages the sunset process for outdated assets.
4. Treating content as living systems. Instead of “publish and forget,” treat your content libraries like software: versioned, maintained, and regularly patched. When policies change, content updates follow within defined SLAs.

What Content Leaders Should Do Next

Content leaders need practical systems that reduce risk without bringing publishing to a halt. These three steps are a reasonable jumping-off point:

1. Start with an audit. Identify your highest-exposure content: pages making specific claims, documents AI systems frequently cite, assets in regulated topic areas. These are your first candidates for accuracy review.
2. Set realistic standards. You can’t fact-check everything quarterly. But you can establish clear thresholds for what triggers review: regulatory changes, product updates, specified time intervals for high-risk content.
3. Make risk management part of content strategy, not a bolt-on. Build verification into your editorial workflow. Include accuracy checkpoints in your content calendar. Staff appropriately for the governance work that now falls to content teams.

For organizations needing additional support, Contently’s Managing Editors can serve as an embedded layer of editorial governance, helping teams maintain accuracy standards without sacrificing publishing velocity.

The cost of fixing content after it spreads is far higher than the cost of managing it upfront. Don’t spend your next quarter doing damage control; put proactive systems in place today. It’s the resolution that will give back all year long.

For more on building content operations that scale responsibly, explore Contently’s enterprise content solutions.

Frequently Asked Questions (FAQs):

How do I know if my content library has risk exposure?

Start by auditing content that makes specific claims: pricing, capabilities, compliance statements, health or financial guidance, etc. Then identify assets that AI systems frequently cite by testing queries in ChatGPT, Perplexity, and Google AI Overviews. Content appearing in AI responses carries the highest exposure and should be prioritized for accuracy verification.

What do I need if I’m on a small content team with no dedicated compliance support?

At a minimum, assign clear ownership for content accuracy reviews on a quarterly cadence. Create a simple risk classification system that routes high-stakes content through additional review before publishing. Document your verification process so you can demonstrate due diligence if questions arise. These basics don’t require additional headcount, just intentional workflow design.

How do I get legal and compliance teams to participate without slowing everything down?

Build tiered review into your process from the start. Define what content types require legal sign-off versus what moves with editorial approval only. Create templates and pre-approved language for recurring claim types so legal reviews become faster over time. The goal is appropriate oversight, not universal bottlenecks.

The post Why Content Teams Are Quietly Becoming Risk Managers appeared first on Contently.

Behind those millions of searches, clicks, saves, and sign-ups are real signals that show exactly what audiences care about and how they engage. For example, retail sites saw a 693% surge in traffic tied to AI-powered shopping assistants and chatbots this year. That kind of growth suggests shoppers are becoming more comfortable letting AI do the comparison shopping for them. Buy now, pay later also became an even more popular option, implying that shoppers were looking for ways to make bigger purchases feel manageable.

January is your opportunity to harness those insights or let them languish. This guide looks back at what worked during the holidays and how to use those insights to plan more effectively for 2026 and beyond.

What People Searched for in December (and Why It Matters Now)

December queries are shaped by the year people just lived through. Sometimes, they signal indulgence; other times, restraint.

Google’s Holiday 100 trends, for instance, made a few patterns clear. In 2025, search interest clustered around practical gift categories: things like movie projectors, weighted vests, kids’ scooters, and backpacks. At scale, that mix suggests steady demand for items that solve everyday needs and feel worth the spend.

In addition to category interest shifts, broader consumer behavior illuminated how people actually made decisions across the season:

• AI-driven sales in the U.S. topped $3 billion on Black Friday alone
• Conversion rates from AI referrals outpaced traditional social channels by a wide margin
• Value-seeking and sharply price-conscious shoppers favored smaller-ticket gifts
• A majority of consumers said rising prices would influence their holiday spending, especially among younger generations facing tighter budgets

Taken together, these signals point to shoppers who were deliberate, price-aware, and increasingly influenced by tools that helped them feel confident about their choices.

In terms of actionable insights here that can carry over to 2026, focus on what reduced friction for people when decisions got complicated. Look at Google Trends and see how searches like “budget gifts” stack up against “luxury gifts” in your market. Then pull last Q4’s Search Console data to see what actually brought people in, not just what you assumed would. Saves on social and interactions with short-form or AI-generated clips tend to spike when people are narrowing choices.

The throughline: Context beats cleverness. When money feels tight, “under $25 gifts” will outperform premium roundups almost every time. For 2026 content planning purposes, marketers should prioritize formats that answer real questions and make next steps obvious.

Where Specificity Wins

Holiday SEO moved fast. January is when you can finally see what held up in search and what didn’t. Rankings have settled, traffic has normalized, and it’s clearer which pages earned their visibility versus which ones were buried.

Looking back, many Q4 search wins came from specificity. Gift-giving phrases, problem-driven queries, and local intent tended to outperform broad holiday terms. Pages that spoke directly to last-minute or highly specific needs earned traction, while generic “Christmas” pages faced steeper competition and more mixed intent.

Long-tail targeting is likely to become even more useful as more discovery happens through conversational queries, whether people type them, speak them, or ask an assistant. In many categories, those behaviors create whitespace brands can capture with clearer, more specific pages.

There’s a particular opportunity with voice search that most businesses are still missing, as we can see below:

Before deciding what to update or reuse next year, check how competitive your keywords were and whether your site was realistically positioned to rank. SEO checkers are useful for validating where effort paid off and where it probably never had a chance.

A post-holiday SEO review usually surfaces takeaways like:

• Holiday URLs that performed well are worth keeping live and updating each season
• Structured data helped certain pages stand out in crowded results
• Updated pages outperformed brand-new ones
• Page speed and simple layouts mattered during high-intent searches
• Basic accessibility improvements supported engagement

Use what December showed you to make cleaner, more realistic SEO decisions going forward.

Building a Content Calendar That Works in January

If December reveals which content holds up under pressure, January is the time to translate those signals into structure. Use the month to reset your publishing rhythm around the pieces that consistently supported real decisions.

A few best practices:

• Publish anchor content early so it can build momentum over time (guides, evergreen explainers, core resources).
• Create decision-support content that aligns with key moments when people are choosing quickly.
• Craft audience-specific pieces tailored to distinct segments instead of broad, one-size-fits-all topics.
• Make space for short-cycle content that moves from idea to publish quickly during spikes.
• Focus on low-friction formats that reduce cognitive load and help people progress without extra steps.

Leaving roughly 20% of the schedule open creates space to respond to demand as it appears, while keeping the rest of the plan stable.

Turning Holiday Insights Into Your Next Plan

The holiday season has passed, and what remains is the record: what people clicked, saved, returned to, and ignored when their attention was stretched thin.

Start with what you already know. Pull the last two years of Q4 data and identify five things that consistently worked. Build around those wins. Add one new experiment to keep learning and to give yourself room to improve.

Momentum comes from simple steps taken in order. Choose one tactic from this guide and implement it today. Tomorrow, choose another. Progress stacks quickly when the next step is always clear.

Audiences respond to clarity. Content that helps them decide, solve something practical, or move forward with less friction earns trust over time. Keep doing that consistently, and your strategy keeps paying dividends, season after season.

Ready to see which stories actually move people through the funnel? Contently’s platform surfaces performance signals across search, social, and conversions — all in one place. See how it works.

Frequently Asked Questions (FAQs):

What’s the biggest lesson marketers should take from the 2025 holiday season?

That audiences reward clarity. Content that helps people compare options, feel confident, and move forward tends to outperform splashy, generic pieces — especially when budgets feel tight.

What metrics matter most when analyzing post-holiday performance?

Look beyond traffic. Prioritize assisted conversions, time on key decision pages, return visits, saves, and email sign-ups. These signals reveal which pieces reduced friction and moved people closer to a decision.

What should I prioritize in January when planning my calendar?

Build around what worked. Anchor evergreen guides early, schedule decision-support content around key moments, leave ~20% of your calendar open for flexibility, and use short-cycle formats when urgency spikes.

The post What’s Working in Content for 2026? What the Holiday Season Taught Us appeared first on Contently.

That’s because there’s still a gap between AI’s promise and its practical value — you know, all those “AI best practices” that no one can quite trace back to real outcomes. Meanwhile, clicks and organic traffic are in freefall.

Of course, at Contently we firmly believe in the value of AI as a force multiplier for great teams. Used thoughtfully, it can streamline research, tighten workflows, and help people ship higher-quality content faster.

But we also recognize that there are some persistent “marketing myths” about what AI can realistically do for content programs and how to use it effectively. These myths tend to take root because AI marketing advice swings between extremes: Hype merchants promise transformation without effort, while skeptics dismiss everything as a fad. Neither helps the marketing director trying to figure out what actually works on Monday morning.

This is the year to get that clarity. Here are five myths that deserve to stay in 2025.

Myth 1: More AI Tools Automatically Mean More Efficiency

On paper, it sounds logical: Add more AI, get more done. In practice, it often works the other way around: Instead of replacing manual steps, many teams end up layering tools on top of one another.

The takeaway isn’t “use fewer tools,” but rather that true efficiency comes from connected workflows. When AI lives inside the places work already happens — your briefs, your CMS, your editorial calendars — the gains start to show up. Good training and clear guidelines can also do more for productivity than chasing the newest feature set.

What works: Before adding anything new, map your current process end to end. Look for bottlenecks AI can realistically remove, consolidate where possible, and invest in helping your team use the tools they already have with confidence. Some basic guardrails also keep everyone from experimenting in five different directions at once.

Myth 2: AI Content Performs Just as Well on Its Own

Thanks to AI, we’re no longer short on content. Most teams can publish more than ever. The real challenge is creating work that actually sounds like you — and earns more trust than the nearly identical post your audience saw five minutes earlier.

Performance now hinges on expertise and perspective, not volume. Search engines and readers both look for signals that someone who knows the topic is actually behind the keyboard, but generic AI text often lacks the lived experience and perspective that makes content persuasive. In other words, grammatically correct copy isn’t the same thing as a compelling narrative.

What’s more, left to its own devices, AI tends to default to the safest version of an idea, which is rarely memorable (and probably won’t drive conversions).

The teams seeing results are treating the AI content creation process as a collaboration. They layer in examples from real customers, clarify claims, tighten arguments, fact-check (!!!), and make sure every piece serves a clear business goal.

What works: Use AI to speed research, outlines, and first passes. Then layer in human editing for accuracy, voice, story, and differentiation.

Myth 3: AI Will Solve Bad Strategy

AI optimizes execution. But it cannot fix fuzzy positioning or off-base business goals. Speed amplifies direction, including the wrong direction.

We see this play out all the time. Teams use AI to publish more, faster… and the metrics that matter don’t budge. Traffic goes up, but conversions stall. The content ranks for keywords, but it doesn’t speak to real buyer pain. Without clear positioning or a path to conversion, all that new visibility simply evaporates before it reaches pipeline.

What works: Get crisp on messaging and conversion paths before you scale production. Then let AI help you execute a strategy that’s already pointed in the right direction.

Myth 4: Everyone Needs to Adopt AI for Everything Immediately

FOMO drives bad technology decisions. Teams adopt tools because competitors are using them, not because they actually solve identified problems. Those wrong-fit tools then create cost, confusion, and cynicism that makes future adoption harder.

The teams that make AI work may not move the fastest, but they do make those moves deliberately. They start by identifying a problem worth solving, define what success should look like, and only then pick the technology.

Readiness also matters. A team still ironing out basic content workflows won’t get much leverage from advanced optimization features. A team without clear governance can accidentally multiply brand, legal, and data-privacy risks as soon as AI scales production.

What works: Look for a single, high-impact use case where AI can remove friction or cost. Run a contained pilot. Document what improved (and what didn’t). Expand from there.

Myth 5: AI Search Is Basically the Same as SEO

Marketers understand visibility through rankings. So it’s easy to assume AI-powered answers are just another extension of Google’s algorithm. They aren’t.

Traditional SEO metrics like site structure and performance remain foundational. But AI Search works differently. Instead of ranking pages, language models compress and rewrite information across multiple sources. According to Ahrefs’ 2025 research, AI Overviews reduce clicks to top-ranking pages by 34.5%. In short, ranking well no longer guarantees visibility.

Visibility in AI Search depends on whether your content is structured clearly and rich with credible context. Two articles might rank identically on page one. The one with clear structure, schema markup, and direct answers gets cited repeatedly by AI assistants. The other rarely appears in AI-generated responses.

What works: Maintain traditional SEO foundations while adding practices designed for AI visibility — clear entity definitions, structured data, and question-driven content formats.

If the last few years were about experimentation, the next one should be about discipline. Use AI where it helps, skip it where it doesn’t, and focus on outcomes instead of promises.

Here’s to a 2026 with fewer breathless predictions and more proof that the work is actually working.

Ready to build AI workflows that actually help your team accomplish real work? Contently’s AI-assisted content platform combines generative AI efficiency with editorial oversight — so your team accelerates without sacrificing quality or brand safety.

Frequently Asked Questions (FAQs):

How do I know if my team is ready for AI adoption?

Assess your current content operations first. If your team has documented workflows, clear brand guidelines, and consistent publishing processes, you’re ready to pilot AI tools. If basic operations still feel chaotic, strengthen those foundations before adding AI complexity.

What’s the minimum investment needed to see results from AI?

Most teams can start with existing tools. Many content platforms now include AI features at no additional cost. The real investment is time: Expect to spend two to four weeks training your team on effective prompting and editing workflows before seeing consistent productivity gains. Budget for those learning curves.

How should I balance traditional SEO with AI Search optimization?

Treat them as complementary. Continue building topical authority, improving site performance, and earning quality backlinks — these fundamentals still matter. Layer AI-specific practices on top: structured data markup, clear entity definitions, and content formats that answer questions directly.

The post 5 AI Marketing Myths to Leave Behind in 2025 appeared first on Contently.

For marketers, this means the old SEO playbook won’t cut it anymore. We’re in a whole new ballgame.

Here are a few predictions on how marketing teams will need to operate in 2026, as this shift in discovery becomes more deeply embedded in everyday search behavior.

Prediction 1: AI Answer Engines Will Become the Default Search Experience

In 2026, traditional search (the “ten blue links”) will still exist, but it’ll play a secondary role as tools like ChatGPT, Gemini, Perplexity, and Google’s AI Overviews increasingly handle the first pass at information discovery. We’ll be dealing with more of a search ecosystem than a single gateway controlled by one dominant engine, even as Google continues to set the tone.

The real shift here is the fact that answers are now assembled from a bunch of different, disparate sources. AI systems pull from publisher content, brand-owned assets, and third-party reference material; weigh their credibility; and synthesize responses. This means that content across all these channels can influence outcomes without ever earning a click.

That fundamentally redefines what both SEO and content marketing entail. Visibility is no longer about ranking first on a results page. It’s about being retrievable and trusted enough to be used as input. Structured data, clear sourcing, and explicit signals of expertise move from best practice to table stakes. Breadth, i.e., how many places you’re published consistently and recognized as an authority, matters.

By 2026, content that isn’t designed to be cited simply won’t show up where decisions are being made.

Prediction 2: Search and Recommendation Will Collapse Into a Single Discovery System

By 2026, the distinction between “search” and “recommendation” will be mostly academic.

This convergence is already visible across platforms. AI systems routinely infer what users want before they articulate it: YouTube queues up explainers you didn’t explicitly search for, LinkedIn surfaces posts aligned to your role and interests, TikTok predicts what will hold your attention within seconds, and Amazon anticipates needs before they become queries.

For marketers, that changes both the opportunity and the risk. Content can now reach the right audience without a single keyword ever being typed. A sharp industry analysis or a well-designed explainer can travel far beyond traditional search results. But content that isn’t legible to these systems—or doesn’t fit the platform’s native signals—won’t travel at all.

In 2026, marketers will need to start designing for moments of “inferred need,” not just explicit demand. That means understanding how different platforms evaluate relevance, creating content that fits their native formats, and accepting that discovery is increasingly driven by systems deciding for users.

Prediction 3: Personalization Will Get a Memory

Persistent conversational history and user-level memory are becoming standard features across major AI platforms. ChatGPT, Gemini, and Perplexity now remember past interactions, saved preferences, and accumulated context. More and more, this memory shapes what content gets recommended to users.

The consequences for discovery are profound. Somebody who has previously explored a topic at an advanced level will receive different results than someone encountering it for the first time. Past clicks and conversational patterns all influence what AI presents in its outputs.

This creates audience fragmentation at an unprecedented scale. The same query from two different users may surface entirely different content based on their individual memory profiles. Repeat searchers see increasingly tailored results that reflect their established preferences and expertise levels.

Marketers must respond with more modular content strategies. They’ll need to create content that serves different knowledge levels (e.g., beginner, intermediate, expert). That means designing content as a progression with clear entry points, deeper follow-ons, and signals that help systems understand who each piece is for.

Prediction 4: Attribution Models Will Break, but New KPIs Will Emerge

With the rise of AI search, brands are losing insight into the traditional click-based path from search to conversion. It’s getting harder to determine how content influences decisions.

This breakdown forces a rethinking of measurement. Clickthrough rates (CTRs), long the bedrock of search performance analysis, become less reliable as primary KPIs as more conversions happen through pathways that bypass traditional tracking.

New metrics will emerge to fill the gap. Citation frequency—how often AI systems reference your content—is becoming a meaningful signal. Model recall rates, excerpt usage patterns, structured data adoption, and dwell time within AI-generated summaries all offer insight into content performance in the new environment.

Perhaps most significantly, “share of answers” will emerge as a competitive benchmark. Just as share of voice became a standard PR metric, share of answers will measure how often your brand appears in AI-generated responses relative to competitors. Performance teams and forecasting models will need to incorporate these new signals, developing frameworks that capture influence even when direct attribution proves impossible.

Prediction 5: Authority Signals Will Become the New Ranking Factors

As LLMs grow more cautious about sourcing and citation quality, authority signals are displacing traditional SEO factors as the primary determinants of visibility. Trust, accuracy, and demonstrable expertise have become the currency that determines whether a brand’s content gets surfaced at all.

This shift reflects how AI systems evaluate content. They increasingly emphasize verifiable claims, named experts, publication transparency, and clear information provenance. High-signal pages—those rich in facts, specificity, structure, and consensus alignment—receive preference over high-volume content that lacks depth or originality.

Model training updates, retrieval layers, and safety guardrails all push the system toward what might be called “safe precision.” AI systems reward brands that back up their claims with evidence and penalize those that don’t. The era of thin aggregation and SEO filler content is ending.

For marketers, this means substance will beat scale more often than not. Original research, subject matter expert quotes, and first-party insights are already gaining substantial value. Brands must invest in credentials like detailed author bios, proper citations, disclosure statements, and expert review processes.

In other words: Human expertise is becoming a competitive advantage again. (There’s a reason the recent Wall Street Journal article on brands hiring “storytellers” went so viral.)

Preparing for the Search Landscape Ahead

The transformation of search represents both a challenge and an opportunity. Marketers who cling to legacy approaches will find their strategies increasingly ineffective as AI reshapes discovery, but those who adapt will position their brands for sustained organic growth.

The time to prepare is now. Audit your content for answer-readiness. Invest in structured data and expertise signals. Build measurement frameworks that capture influence beyond clicks. The search landscape of 2026 is taking shape today, and the foundations you lay now will determine your visibility in the AI-driven discovery era ahead.

Frequently Asked Questions (FAQs):

If clicks are declining, how do we prove content is working?

Measurement is shifting from traffic to influence. Metrics like citation frequency, excerpt reuse, and “share of answers” are becoming more meaningful indicators of performance than CTR alone. While these signals aren’t as clean as last-click attribution, they offer a clearer picture of how content shapes decisions upstream — even when traditional analytics can’t see it.

What kinds of content perform best in AI-driven discovery?

Content that is clear, specific, and defensible tends to travel farther than broad or generic material. AI systems favor structured explanations, verifiable claims, named experts, and well-defined scopes. Original research, expert commentary, and tightly framed explainers consistently outperform thin aggregation or keyword-driven filler.

How should teams adapt their content strategy for personalization and memory?

Teams should think in terms of progression rather than one-size-fits-all assets. That means creating modular content that serves different knowledge levels and clearly signals who each piece is for. Entry-level explainers, deeper technical breakdowns, and advanced perspectives should connect logically, allowing systems to surface the right material based on a user’s history and expertise.

The post What’s in Store for the Future of Search in 2026? 5 Predictions appeared first on Contently.

Take, for instance, a fintech team that publishes a Know Your Customer (KYC) explainer, only to learn later that a single phrasing choice misaligned with Anti-Money Laundering (AML) requirements. What seemed like a harmless line can trigger formal compliance reviews, takedown notices, or even fines.

When producing content for regulated industries, the question needs to shift from “How do we create high-performing content?” to “How do we create content that performs without crossing compliance lines?” That’s where a compliance-guided content strategy comes in.

Let’s discuss how you can create one.

1. Know the Rules

Start with identifying the legalities surrounding sensitive topics and mapping out regulatory boundaries in your industry. Consider areas like:

• Data protection and privacy
• AI use
• Advertisements
• Sociocultural nuances
• Influencer and sponsored content disclosures

Data protection laws differ by region, even for the same industry. For example, California has its own data privacy law called the California Consumer Privacy Act. Likewise, the United Kingdom enforces the Data Protection Act.

If your service reaches Europe, Canada, Asia, or African markets, expect to deal with additional policies like:

• GDPR
• PIPEDA
• POPIA
• DPDP

Each region has different rules governing the types of ad content your marketing team can produce. For instance, a health tech platform can’t market a symptom checker as a diagnostic tool in the U.S. without treating it as a regulated medical device and backing its claims with clinical validation. And if it collects or uses patient data, it also has to comply with HIPAA‘s privacy and security rules. In Europe, regulators such as France’s CNIL require clear, explicit consent and transparent disclosures before using personal data for targeted or AI-powered personalized marketing.

Study and compile the laws that apply to your operations, and use them to streamline your content framework for compliance. To stay up to date, you can track relevant regulations using legal monitoring tools such as Securiti, OneTrust, or DataGuidance.

2. Build a Compliance-First Framework

Wang Dong, founder at Vanswe Fitness, notes that a common mistake many content teams make is treating compliance as an afterthought. “You need to do the opposite. Compliance has to be built into your content skeleton, also known as the framework. That means your team needs to shift from the typical idea first, draft next, and legal review last to something more structured.”

To operationalize compliance, begin by:

• Formulating an idea and outline review process that includes compliance guardrails
• Creating compliant content briefs complete with bulleted “do’s and don’ts”
• Drafting within approved terminology
• Setting up processes for legal and expert verification before publishing
• Ensuring final content is aligned with both brand and regulatory language

Besides including legal or compliance review in each stage, you also need to:

• Use approval workflows and audit trails to track what was reviewed, by whom, and when
• Keep documentation for every claim, quote, statistic, or regulatory reference so you can defend your messaging if questioned

Anna Zhang, head of marketing at U7BUY, offers a piece of advice: “Create an internal reference sheet for your content team that summarizes what they can say, what they must avoid, what requires legal review, and what needs source citations or disclaimers,” she says. “This includes word choices permitted in your industry; pronouns in DEI-inclined regions; cultural intonations that can trigger public outrage; and overly assertive, non-permissible terms.”

3. Define Clear Messaging Boundaries for Health and Financial Claims

The health and financial sectors can be more sensitive and reactive to non-compliance due to the high stakes involved. When you’re dealing with people’s lives and financial well-being, the margin for error narrows quickly. So, it’s essential to establish crystal clear boundaries around gray areas like health promises or investment guarantees.

For instance, you shouldn’t use ambiguous phrases or absolute language like:

• “Guaranteed returns on investment”
• “Zero returns on investment”
• “Stops fraud completely”
• “Eliminates stroke risk”

Instead, consider more transparent and compliant framing like:

• “Returns tied to market performance with defined risk controls”
• “Designed to limit downside exposure under specific conditions”
• “Helps reduce fraud incidents through multi-layer verification and monitoring”
• “Supports cardiovascular health outcomes when combined with licensed medical care”

In a nutshell, assertiveness and overtly promotional phrases might violate marketing regulations in your industry. A more suggestive approach backed by verifiable data can save you legal trouble down the line.

4. Use Technology Wisely

“Manually reviewing each piece of content for compliance can be a tough nut to crack, especially if you churn out [dozens of pieces of content each week],” says Paul McKee, founder of ReadingDuck.com. He suggests that AI-powered writing tools like Grammarly and editing assistants such as Hemingway can help surface unclear phrasing, overly bold claims, or ambiguous language for compliance review and editing.

Grammarly screenshot by Author

Compliance platforms like Vanta can also automate evidence collection and help teams achieve and maintain compliance with frameworks such as SOC 2, HIPAA, and ISO 27001. Others, such as Riskonnect, centralize policies, compliance requirements, audit tracking, and risk reporting in a single system.

Such platforms often include features like:

• Content tagging for regulatory risk, such that each content piece is flagged for required disclosures, claims, and region-specific rules
• Audit trails to track who reviewed what, when, and what decision was made
• Alerts and changes to monitor regulation updates and notify you so you can revisit content that may now be non-compliant
• Compliance status to help you know which content pieces are safe, which need review, and which are blocked

5. Focus on Transparency and Credibility When Using AI

Morgan Taylor, co-founder of Jolly SEO, notes that regulated industries may also require more transparency when it comes to the use of AI. “Each content piece should also disclose AI involvement, and to what extent, as required by the regional and global regulations,” he says.

Stipulations may include:

• Mandating citation of verified data sources
• Disclosing sponsorships and affiliations
• Keeping messaging consistent across regions with varying regulations

AI disclosure isn’t just necessary due to industry regulations, but also because your audience demands it. According to Dentsu, about 75% of consumers said brands should disclose if branded content was created with AI.

6. Train and Align Your Internal Teams

Studying compliance laws, building a framework, and defining messaging boundaries is just half the job of developing an effective content strategy. The other half is equipping your team with this strategy. You can do that by:

• Organizing regular workshops that focus on current operational laws and your brand’s ethics
• Sharing a short compliance checklist that your writers and editors can seamlessly reference

Emily Ruby, owner of Abogada De Lesiones, suggests building a feedback loop between your legal and marketing teams to streamline compliance review. “This involves integrating your legal team into the final content review process just before any piece goes live and helping them communicate directly with your editors,” she says.

7. Measure What Matters

After implementing the above best practices, start tracking metrics that show whether your compliance process is actually working. This includes:

• Turnaround time for compliance review
• Approval rate across content themes and formats
• Number of revisions caused by regulatory language
• Content trust KPIs, such as authority signals, citation depth, and expert references

Additionally, conduct quarterly audits on published content to identify outdated claims, expired data sources, and language that no longer aligns with current industry regulations.

In highly regulated industries, credibility is fragile and the repercussions for compliance missteps can be severe. The more your team embeds compliance into everyday workflows, the safer and more effective your content becomes.

Contently’s team of expert Managing Editors and professional creators can help strengthen your workflows and ensure every piece meets your industry’s standards. Reach out to get started.

Frequently Asked Questions (FAQs):

What counts as a “regulated industry” for content teams?

Industries like healthcare, finance, insurance, cybersecurity, legal services, and fintech operate under strict compliance frameworks. If your content touches personal data, medical advice, financial guidance, or AI-driven personalization, you’re likely subject to additional oversight.

How often should content be reviewed for compliance?

A good baseline is quarterly, but high-risk industries may require monthly reviews, especially for evergreen pages making health, financial, or legal claims. Any regulatory update or product change should also trigger a review cycle.

How can small teams manage compliance without slowing down production?

Start with guardrails: approved terminology lists, claim libraries, and compliance-aligned content briefs. Then use workflows, audit trails, and documentation templates to reduce back-and-forth and make reviews predictable.

The post Developing a Content Strategy for Regulated Industries in 2026 appeared first on Contently.

Schema markup sounds like something that belongs in a developer’s basement lab, right next to the blinking server rack and a stack of vintage Linux manuals. Most marketers treat it that way too: vaguely intimidating and probably dangerous to poke without supervision.

But you don’t need to write code or summon an engineer to make sense of it. And if your content is getting outranked or out-cited by inferior articles in AI Search, this is likely the one of the missing links.

Schema isn’t magic. It’s simply the structured vocabulary that tells search engines and AI tools exactly what your page is about and whether it’s trustworthy enough to cite.

Here’s a marketer-friendly overview of this increasingly important component of your content.

The Problem: Your Content Is Invisible to AI

Schema markup is structured data you add to your website’s HTML that tells machines exactly what your content is about. Think of it as labels on a filing cabinet: Without identification, someone rifling through your files has to guess what’s inside; with clear labels, they know instantly.

Search engines and AI models face the same ambiguity problem. Your page might include a product name, price, author bio, and publication date — but without schema, machines have to infer what each piece represents. Schema removes the guesswork by marking up entities: “This is a product. This is its price. This is the author. This is when it was published.”

The payoff is twofold: in traditional search, schema powers rich results like star ratings, FAQ dropdowns, or recipe cards. In AI Search, schema helps language models identify entities, reduce ambiguity, verify facts, and cite sources. Whether someone searches on Google or asks ChatGPT, schema makes your content easier to parse and surface.

But implementation mistakes are costly. Sites that mark up content invisible to users or use schema that doesn’t match visible page content risk manual penalties from Google, which can remove rich-snippet eligibility entirely. In other words, schema only works when it accurately reflects what’s on the page.

The Three Schema Types Marketers Need First

Most marketers don’t need every schema type under the sun. These three schema types cover 80% of content marketing use cases and deliver the fastest visibility wins:

Article schema

This schema type marks up blog posts, news articles, and longform content. It tells search engines the headline, author, publication date, and featured image. LLMs rely on Article schema to disambiguate entities and verify publication dates when fact-checking claims — and without it, your “Apple” could be a fruit, a tech company, or a record label.

Use Article schema on every piece of editorial content you publish; it’s the baseline for getting your articles indexed properly and cited in AI answers.

Organization schema

This establishes your company as a verified entity; without it, AI tools may cite your content without attributing it to your company. Organization schema includes your business name, logo, contact info, and social profiles. Add this schema type to your homepage and About page to help search engines and AI models connect your brand to your content across the web.

Person schema

This marks up author bios, executive profiles, and contributor pages. It connects individuals to their credentials and organizational affiliations, and it’s critical for building expert authority. When AI tools cite content, they often cite people by name, and Person schema makes those connections explicit. This becomes particularly important as AI systems prioritize content from verified experts over anonymous sources.

According to Backlinko research, 72.6% of first-page Google results already use schema markup, meaning the majority of companies who do well with traditional SEO have implemented it, whether intentionally or through CMS defaults. With schema rapidly becoming even more important for landing in AI Search results, the window for competitive advantage is closing.

How to Implement Schema This Week

You don’t need to write JSON-LD by hand or understand HTML to implement schema. Multiple no-code pathways exist, including:

• CMS plugins. WordPress users can install Yoast SEO or Rank Math, both of which add schema automatically to posts and pages and let you fine-tune the type per template. On platforms like Shopify, Squarespace, and Webflow, many modern themes and built-in features (or apps) output structured data for products and articles. If your CMS offers any schema or “structured data” functionality, enable and configure that first. It’ll be the fastest path to broad coverage.
• Schema generators. If your CMS doesn’t do enough out of the box, use a visual generator (like Google’s older Structured Data Markup Helper or a third-party tool) to tag elements on your page and export JSON-LD. Just highlight the headline and click “headline” (or highlight the author name and click “author”), and the tool creates the markup. Paste it into your page’sand you’re done.
• Pro tip: Validation is non-negotiable. After adding schema, validate it. Google’s official tools (e.g., the Rich Results Test and Google Search Console) check highlight missing fields and flag incorrect formats. Fix what’s broken, re-test, and then publish.

To get traction fast, start with quick wins: Add Article schema to your top 10 blog posts this week, Organization schema to your homepage, and Person schema to author bio pages. Track which pages show up in AI-generated answers over the next quarter. Measure the shift.

The Bottom Line

Schema markup is a quiet layer of infrastructure that grows alongside your content. And while everyone is arguing about whether it’s “too technical,” the brands shipping it are quietly becoming the sources machines trust first.

You don’t need to overhaul your entire site this week. Start with the pages that drive the most value and build outward from there. Momentum is what matters, and the longer you wait, the more entrenched everyone else’s signals become.

Ready to level up your content operations? Explore how Contently helps brands turn strategy into measurable results.

Frequently Asked Questions (FAQs)

Do I need schema if my content already ranks well on Google?

Traditional rankings don’t guarantee visibility in AI-generated answers. Schema helps AI models understand and cite your content even when users never click through to your site. If you want to show up in ChatGPT, Perplexity, or Google AI Overviews, schema provides the structured context those systems rely on.

How long does it take to see results from schema implementation?

Google typically recrawls and reindexes pages within a few weeks of adding schema. Rich results can appear as soon as your updated markup is indexed. For AI Search visibility, expect a longer timeline (months, not weeks), but the benefits compound over time. Most brands see initial rich results within 2-4 weeks, while AI citation improvements take 2-3 months as models refresh their retrieval systems.

Can schema hurt my SEO if I implement it incorrectly?

Incorrect schema won’t tank your rankings, but it won’t help either. Google ignores malformed markup or schema that doesn’t match your page content. The bigger risk is missing out on rich results and AI citations. Use validation tools to catch errors before they go live.

The post WTF Is Schema? A Primer for Marketers appeared first on Contently.

That’s changing quickly. AI-driven video indexing, powered by large language models (LLMs), computer vision, and automatic speech recognition, now treats video content like readable text. Search engines and recommendation systems can now see everything from your captions to the text on your slides.

As a result, video is becoming SEO 2.0, a fully discoverable format that can rank and surface answers just like a blog post.

For content teams, this demands a new approach. If video is now as indexable as written content, you need a “video retrievability” strategy that ensures your clips show up when people search for the problems your product or service solves.

Why Video Is Now SEO-Relevant

The mechanics of search are evolving quickly. AI-powered systems like Google’s AI Overviews, Perplexity, and ChatGPT can now parse the actual content inside your videos, not just the title or description. With advances in automatic speech recognition, computer vision, and language modeling, search engines can extract meaning from multiple layers at once:

• Spoken dialogue transcribed and analyzed word by word
• Auto-captions and SRT files providing structured, timestamped text
• On-screen text detected through computer vision, from slide titles to product labels

This is a major shift from the old world of video SEO, where discoverability hinged on thumbnails, tags, and a few surface-level signals. Now, every meaningful moment, from your initial overview of a framework to your example at minute 3:42 to the term typed on a screen, can be read and indexed.

That’s the foundation of retrievability: a search engine’s ability to find, understand, and surface specific insights from within your video content.

Beyond SEO: How Generative Search Engines Use Video

Retrievability is only the starting point. Generative search engines go a step further by blending insights from text, video, audio, and images into a single synthesized answer. In these environments, video isn’t treated as a standalone format. It’s just one source among many that an LLM uses to construct the most authoritative response.

That’s why video citations are showing up in AI-driven answers. A YouTube clip may appear inside a Google AI Overview as supporting material, or TikTok’s “Search Highlights” might pair a trending query with a short, highly relevant clip. ChatGPT and Perplexity increasingly pull structured insights from videos that are properly indexed and easy to parse.

For brands, visibility now depends on multi-format coverage. If your expertise exists only in blog posts, you have a gap. If your videos aren’t optimized for retrieval, they won’t appear in the generative answers shaping consumer decisions.

How to Optimize Video for AI Search

If video is now discoverable at the dialogue level, your optimization strategy needs to go deeper than metadata. Here’s how to make your videos work like high-performing content.

Think of your script as both narrative and index.

Write your video scripts the way you’d compose an optimized blog post. That means clear phrasing, natural long-tail questions, and front-loading key terms in a way that feels conversational.

That “conversational” element is important because LLM-powered search engines prioritize natural language. Instead of saying “Today we’ll discuss customer acquisition strategies,” try, “How do you acquire customers without spending a fortune on ads?” The second phrasing mirrors how people actually search, and gives AI systems a clearer signal about the problem you’re solving.

If you’re explaining a concept, state it plainly early in the video. Ambiguity might work for storytelling, but it doesn’t work for retrievability.

Get serious about metadata hygiene.

Your title, description, and tags should accurately reflect the problem your video solves, not just the topic it covers. Avoid keyword dumping. Instead, prioritize clarity and user intent.

For example, in lieu of a title like “Content Marketing Tips | SEO | Video Strategy | 2025,” go with something like “How to Make Your Marketing Videos Discoverable in AI Search.” The latter is more specific and clearly describes the content’s value.

This approach applies to platforms ranging from YouTube to TikTok to LinkedIn.

Make your transcript the most accurate version of your video.

Always upload full transcripts or SRT files, which are now critical ranking signals. Well-formatted transcripts help AI systems disambiguate topics and identify key takeaways, as well as match your content to nuanced or niche queries.

Transcripts also capture long-tail queries that don’t fit neatly into titles or descriptions. Someone searching “how to handle objections in sales calls with technical buyers” might find your video because that exact phrase appears at minute 12 in your transcript, even if your title is more general.

Keep your transcripts clean. Remove filler words if they obscure meaning, but don’t over-edit. Natural phrasing is what LLMs are trained on.

Think of on-screen text as a secondary layer of indexable content that reinforces spoken points.

Everything you put on screen — callouts, lower thirds, slide text, product labels — is now crawlable. That’s a huge opportunity, but it also means you need to be intentional. If you’re introducing a framework, make sure the name of that framework appears visually. If you’re citing a stat, put it on screen in readable text.

Avoid “text spam,” i.e., cluttering your video with keywords just for the sake of crawlability. But do ensure that key terms, takeaways, and concepts appear both verbally and visually when relevant.

Practical Checklist: Your Video Retrievability Toolkit

Here’s a quick implementation guide to make your video content discoverable in AI-powered search:

• Write scripts with clear takeaways and natural phrasing that mirror how people search
• Add clean titles, accurate descriptions, and high-quality tags that reflect user intent
• Include full transcripts or SRT files with proper formatting and minimal filler
• Use intentional on-screen text for key concepts, stats, and frameworks
• Maintain consistent naming conventions across platforms to build topical authority
• Repurpose transcripts into blog posts to reinforce your expertise and capture text-based search traffic

Treat this as an evolving practice. As AI Search tools become more sophisticated, the ways they index and cite video will continue to shift. The core principle, though, remains making your content easy to find, understand, and reference.

Search engines are learning to see, hear, and cite everything. The black box is open. What you do with that power is up to you.

Learn how Contently can help you turn video into discoverable, high-performing content.

Frequently Asked Questions (FAQs)

How long should my video be for optimal discoverability?

There’s no universal “best length,” but clarity and structure matter more than duration. Shorter videos work well for intent-matching on TikTok and YouTube Shorts, while longer explainers provide deeper material for generative answers to pull from.

Do I need special tools to make my videos indexable by AI Search?

No. Most of what matters — clean scripting, accurate transcripts, readable on-screen text, and clear metadata — can be handled during production and upload. AI search engines handle the indexing automatically if the signals are there.

How quickly will I see results from video retrievability efforts?

Indexing timelines vary by platform, but many brands see improvements within weeks. The bigger gains come from consistency: using unified naming conventions, publishing across multiple formats, and reinforcing your expertise with supporting written content.

The post Your Brand Needs a Searchable Video Strategy appeared first on Contently.

Not KPIs, not personas—entities. We know it sounds vaguely like the plot of a sci-fi film about sentient databases. But entities are real, and if AI models don’t recognize you (or your brand) as one, you may as well not exist to the millions of users currently asking AI tools for answers instead of typing searches into Google.

Somewhere between “thought leader” and “structured data,” entities are how AI search engines recognize and categorize information sources. That means your brand needs to show up as an entity and your products as their own connected entities. Beyond making your brand and flagship content machine-readable, you can tap the people within your organization who already embody that expertise—and elevate them as recognized entities, too.

So if you’ve got a CTO who wows the crowd on stage with her cutting analysis of AI ethics, or a chief economist whose byline shows up in every industry trade mag, you’re halfway there. But you still need to figure out how to turn these living, breathing experts into machine-legible profiles complete with context, connections, and citations that LLMs can actually read.

Why Internal Experts Matter in AI Search

As AI-driven search tools evolve, they’re often rewarding recognizable human expertise over anonymous brand content. Research from BrightEdge identifies author expertise as one of the key quality signals AI algorithms use to evaluate trustworthiness and relevance. In other words, an article bylined “Marketing Team” carries less authority than one attributed to a real person with verifiable experience and a digital footprint to match.

This ties into a larger shift in how credibility is gauged online. Search Engine Land notes that “verifiable authorship makes your content stand out as trustworthy in a sea of generic AI material,” recommending brands use structured data to help AI systems understand who is behind the content (more on this in a sec). When search engines and AI models can connect a name to reputable publications and other professional activity, they’re more likely to surface that expert as a reliable source.

This matters because buyers trust people more than logos. The 2024 Edelman-LinkedIn B2B Thought Leadership Impact Report found that nearly three-quarters (73%) of decision-makers say an organization’s thought‐leadership content is a more trustworthy basis for assessing its capabilities than its marketing materials.

Put simply: Both algorithms and audiences are looking for the same thing, and that’s credibility. When brands elevate internal experts with visible, verifiable identities, they improve their odds of being cited in AI-generated answers and influencing real-world buying decisions.

Three Implementation Layers

Turning experts into search entities requires three systems working together.

1. Optimizing authorship metadata

Think of your expert pages as digital passports for your people. If AI systems can’t read the name or credentials on that passport, your content risks rejection.

This first layer is about definition, i.e., making sure every expert within your organization has a clear, consistent identity that algorithms can recognize. Maybe your head of compliance appears as “J.R. Martinez” on your blog, “John Martinez, JD” on LinkedIn, and “John Martinez” on a conference agenda. To a human, it’s obviously the same person; to an algorithm, it may be three separate entities (there’s that fun term again).

Likewise, specificity matters. The same rules that make a resume effective apply here: A vague bio like “20 years in B2B SaaS” tells a weaker story than “former VP of Product at Salesforce, led three launches generating $50M ARR, published in Harvard Business Review.” This layer is about getting the foundational data right so AI systems know who your experts are.

Action items for marketers:

• Add structured data: Use Schema.org/Person markup on every author bio to make expertise machine-readable, and link to LinkedIn and external publications.
• Standardize bylines: Keep author names, titles, and bios consistent across all platforms, and maintain a canonical author page as the single source of truth. Update on a consistent basis (quarterly or every six months) to reflect new achievements or expertise.
• Show concrete credentials: Use specific, verifiable achievements (e.g., awards, results, publications) instead of vague experience statements.

2. Building cross-platform credibility

If your experts only exist on your blog, they might as well be whispering into the void. Once identity is defined, visibility is the next layer. AI engines (and human audiences alike… those still matter too) take cues from signals across the web. A CTO who posts on LinkedIn, appears on a podcast, receives invites to CES and SXSW every year, and gets quoted in TechCrunch looks a lot more “real” to both humans and machines than one who lives exclusively on a company site.

This layer is about amplification: showing up in trusted spaces where expertise carries weight. Each verified appearance helps algorithms cross-reference your experts and build confidence in their authority.

Action items for marketers:

• Show up beyond your own domain: Encourage experts to share insights on LinkedIn, contribute guest articles, join panels, or appear on podcasts. Each mention reinforces their authority signal.
• Keep bios consistent: Use the same headshot, job title, and expertise descriptors across platforms so AI sees one cohesive identity.
• Prioritize trusted venues: Focus your experts’ visibility in the channels and publications your audience already trusts. Quality beats quantity.

3. Connecting human voices to structured data

Your VP of Product might publish a brilliant post on API security, but unless that article links her name to the subject in structured data, those insights will disappear into the algorithmic abyss. This third layer closes the loop and linking who your experts are and where they appear to what they know.

This is where human knowledge becomes data that machines can understand and reuse. By embedding structured tags and capturing expert insights in standard formats, you make it easy for AI systems to retrieve and cite that expertise again and again.

Action items for marketers:

• Connect people to topics: Use internal knowledge graphs or structured tagging to link each expert to their focus areas within your content taxonomy.
• Use Q&A formats strategically: Create FAQs or explainers where experts answer common questions, then mark up with FAQPage schema to give AI clean, citable quotes.
• Close the feedback loop: When experts share new insights or answer customer questions, capture that information in structured formats so AI systems can find and surface it.

Common Barriers to Expert Participation

Getting insights out of busy SMEs or execs is messy, political, and often lands low on their priority list. Here are the five roadblocks that show up again and again:

1. Time (and attention) scarcity. Experts are underwater. Billable work and internal projects always come first, leaving “content” to fight for scraps.
2. The curse of knowledge. The more experienced someone is, the harder it is for them to explain what they know. SMEs often skip context or assume everyone understands their shorthand, which makes it tough to extract content that’s clear and usable.
3. Legal and brand risk aversion. Some organizations hesitate to spotlight individuals, fearing off-brand messaging or intellectual property leaks.
4. Internal competition. In fields where credibility equals career capital, multiple people may want to “own” the same topic. Without guidelines for who speaks on what, thought leadership can turn into a turf war.
5. No infrastructure for knowledge capture. Most teams lack the systems to document, tag, and reuse insights efficiently. Without templates, structured interviews, or AI-assisted content extraction, valuable expertise slips through the cracks.

Extraction Tactics That Work

Most content programs stall not because experts lack ideas, but because teams lack infrastructure. When you fix the process, expert participation scales naturally.

1. Make participation low-friction. Stop asking experts to write. Instead, schedule 30-minute interviews where content teams extract insights. One conversation can fuel three blog posts, five LinkedIn updates, and a dozen quotable soundbites. Layer in micro-content opportunities, e.g., quick takes on breaking news or short Slack replies that can be repurposed later. Even better, host “office hours” where content teams drop in with questions.
2. Level up your content team. Train writers to think like interviewers. Teach them to draw out “atomic insights”—the smallest, most original nuggets of expertise that make content stand out. Close the loop by showing experts how their words evolve into polished stories.
3. Partner early with legal and comms. Bring them into the process instead of treating them as gatekeepers. Create simple review workflows and clear guardrails, e.g., what experts can and can’t comment on, how approvals work, and where quotes will appear.
4. Frame it as career growth. Recast participation as professional development. Show how visible experts land conference invites or grow their LinkedIn following. The more your people see real outcomes, the easier it is to get them on board.
5. Create repeatable extraction systems. Build interview templates by content type, e.g., thought-leadership sessions, tactical how-tos, or case-study debriefs. Run monthly roundtables where three to five SMEs discuss one topic; use AI transcription to surface quotes instantly (but have a human double-check for accuracy, of course).

The Long Game

Building expert authority takes time; you probably won’t see results in 30 days. AI systems need consistent, credible signals across platforms before they cite your experts by name in generated answers.

But bit by bit, those signals create a map of expertise that algorithms rely on. Over time, AI builds its own understanding of who knows what. The organizations that keep contributing credible information will shape how their fields are defined in the years ahead.

We can’t change the jargon, but we can make it useful. If “entities” are what the algorithms respect, your experts deserve to be recognized as some of the best.

Learn more about how Contently can help your brand build lasting visibility through expert-driven content.

Frequently Asked Questions (FAQs):

Why should marketers care about entities?

If your experts aren’t recognized as entities, their insights are harder for AI to associate with your brand. Your competitors’ names might even show up in generated answers, even if they’re referencing ideas you originated.

How can I tell if my experts are already “recognized” by AI?

Search for their names alongside key topics on Google and emerging AI search tools like Perplexity or ChatGPT’s search mode. If their profiles or quotes appear consistently, they’re already surfacing as credible entities. If not, you’ve got an opportunity to strengthen their visibility through structured data, authorship pages, and off-site presence.

What’s the fastest way to start building entity recognition, and how long does it take for results to show up?

Start small. Add Schema.org/Person markup to your expert bio pages, link those bios to LinkedIn and other verified sources, and make sure bylines and job titles are consistent across platforms. Then, publish or syndicate content where the algorithms and your audience already look for expertise.

As for how long it takes, this depends. In most cases, consistent, well-structured authorship data starts showing traction in a few months. Over time, as AI models absorb more signals, that visibility compounds.

The post How to Turn Your Internal Experts Into Search Entities appeared first on Contently.

If your brand is lucky enough to be mentioned, it’s usually one line, stripped of style. But even then, the story isn’t yours anymore. The headline your managing editor spent hours crafting has been rewritten, all nuance flattened. Your once-differentiated point of view now reads like it came from a committee.

This is the new reality for marketers: Humans still read content, but increasingly, machines decide what they read first. The job now entails speaking to both audiences — human customers with distinct motivations and mercurial emotions, and robotic algorithms that extract, rewrite, and rank your ideas — without turning your content into boring slop.

The marketers who win in this new era will be the ones whose ideas survive translation. Here’s how to pair standout storytelling with extraction-ready structure.

The Two Audiences Problem

AI Overviews, ChatGPT Search, Perplexity, and voice assistants now read, compress, and represent your content, often before a human sees your page. They snip, condense, and reword, meaning fewer clicks, more paraphrase risk, and new rules for how credit and context travel.

This distribution shift has two practical implications for brands: serve humans with memorable narrative and serve machines with cleanly extractable facts.

Creating content for humans

We may be marketing in the age of machines, but people are still the ones who share (and ultimately buy from) your brand. Ipsos finds that even in marketing content, audiences have a strong preference for human-created content. So even if you’re using AI in your content marketing (which, let’s face it, in 2025 you should be), your message shouldn’t sound mechanical.

What moves people:

• Story arcs with specificity: real scenes, anecdotes, tension, stakes, resolution, and a clear POV that says something new (or says the obvious better).
• Line-level craft: vivid verbs, concrete examples, first-person takes, judicious humor, and sensory detail that earns attention.
• Useful originality: facts or information they can use today, e.g., frameworks, checklists, decision trees, before/after examples.
• Social proof with texture: quotes, screenshots, data points, and customer language that sounds like a person, not a press release.

The challenge:

• Attention math: As attention spans shrink, especially for text-based content, if the first 150 words don’t land, you’ve lost the scroll.
• Audience sophistication: Readers have seen a thousand AI-polished articles; they can spot recycled or over-optimized copy instantly. Every line needs a reason to exist.
• Trust deficit: Audiences crave authenticity; their spidey senses tingle when everything you publish sounds the same. Marketers must balance polish with personality and stay clear without sounding canned.

The takeaway for marketers: Algorithms can summarize information, but only humans can be moved by it. The best human-centric content earns attention by saying something that feels both familiar and fresh, useful and relatable. It draws readers in because it sounds like it was written by someone who understands them.

Even as generative AI reshapes how content is discovered and distributed, you can’t afford to forget these fundamentals.

Creating content for machines

AI engines and LLMs tokenize, extract, and rank. They don’t care how lyrical your prose is or how many hours your writing team struggled to find the exact right turn of phrase for that tagline. They want the claim, evidence, and context mapped to recognizable entities so they can answer a question confidently.

Machines tend to prioritize:

• Clarity & consistency: canonical terms, stable naming, unambiguous definitions, scannable H2s phrased like questions people ask.
• Structure & metadata: JSON-LD schema (Article, FAQ, Product, HowTo), bulleted summaries, glossaries, datelines, author credentials, organization details, canonical URLs.
• Credible citations: first-party data published on your site, outbound links to authoritative sources, methods sections for studies, and consistent cross-site alignment (docs, product pages, partner listings, and PR all using the same names and numbers).

The challenge:

• Zero-click reality: Assistants render answers inline; influence depends on how they summarize and cite you.
• Voice flattening: Witty lines or flowery language get lost in translation; only unambiguous phrasing gets reused.
• Attribution drift: The most parsable source often wins credit, even if they learned it from you.

The takeaway for marketers: Write with the model in mind. Label your answers, standardize your terms, and publish receipts. When writing for AI, clarity — not cleverness — is what earns citations. It’s also becoming clear that freshness is another factor that counts.

How Do Brands Create Content that Speaks to Both Humans and Machines?

To succeed in today’s search-and-summary landscape, you need a dual-pronged content strategy designed for both people and AI parsers. The art is in creating something that reads beautifully to humans while feeding machines the clean signals they need to understand and amplify your story.

Here are five moves to master both:

1. Lead with a scene; label with structure.

Start every piece with a hook that drops readers into a moment by opening with a question, conflict, or vivid visual. Then make sure your subheads, schema, and summaries clearly outline the main takeaways so machines can interpret them. Humans remember stories; machines remember scaffolding.

2. Make every claim quotable and parsable.

When you state an insight, back it with data, name sources explicitly, and phrase it cleanly enough for AI to lift. Think of it as writing for citation: a line that resonates with readers and a sentence that can stand on its own in an AI Overview.

3. Design visuals that speak in two languages.

For humans, visuals should tell a story complete with emotion and context. Machines need text alternatives, descriptive filenames, and clear captions. Whether it’s a chart or a product demo video, metadata is your friend.

4. Use video to teach twice — once to viewers, once to models.

In video or short-form content, open strong; the first three seconds are your headline. Speak keywords naturally in voiceovers, add captions with consistent terminology, and include a structured description when uploading. That helps algorithms surface you, and gives humans a reason to stick with your video until the end.

5. Keep your message stable across every touchpoint.

Machines learn from repetition and alignment. Humans learn from consistency and tone. Use the same product names, taglines, and phrasing everywhere, from blog copy to YouTube titles, so both audiences recognize and recall you.

Measuring Success in a Zero-Click Era

As AI summaries become the new first impressions, traditional traffic metrics no longer tell the whole story. A spike in visibility may not show up as a click, but it can still shape perception, recall, and buying behavior.

The new KPIs live at the intersection of influence and alignment:

• Share of summary: What percentage of AI answers use your phrasing, cite your brand, or reference your data?
• Assisted influence: Does AI visibility correlate with downstream impact, i.e., more branded searches, higher demo requests, stronger sales enablement conversations?
• Funnel impact: Measure the halo; influenced opportunities, demo-to-trial conversions, or ABM coverage lift tied to AI answer visibility.
• Recall tests: Prompt ChatGPT, Gemini, or Perplexity with category questions. Do they echo your terminology, your frameworks, your stats? That’s narrative imprint, not chance.
• Update velocity: How quickly and consistently can you update facts, numbers, and names across every owned channel? Alignment beats speed in a world of retrained models.

We’ve spent years optimizing for people and platforms. Now we’re optimizing for people and parsers. That doesn’t mean stripping the soul from your stories, but it does involve teaching machines how to carry them forward.

The marketers who can do both will own the next era of visibility.

Your stories deserve to be seen and cited. Discover how Contently’s platform helps brands build AI-ready content.

Frequently Asked Questions (FAQs):

What does it mean to create “machine-readable” content?

Machine-readable content is structured in a way that AI systems, search engines, and voice assistants can easily interpret and summarize. That means clear headers, consistent terminology, schema markup, and unambiguous claims so your ideas are easy to extract without losing their meaning.

Should marketers still care about SEO if AI Overviews and chatbots dominate search?

Yes, but SEO now means structuring for understanding, not just ranking for keywords. Schema, entity alignment, and first-party credibility matter more than ever. Traditional keyword tactics may fade, but semantic clarity and topical authority remain critical.

Does this shift change how we approach video and visual content?

Definitely. Treat every visual as both a story and a signal. Use descriptive titles, captions, and metadata so algorithms can understand the context, but still lead with human emotion and pacing that hooks a viewer in seconds.

The post Are We Still Marketing to Humans? appeared first on Contently.

Okay, so maybe that’s an overstatement. But if you’re a human who scrolls or swipes on the semi-regular, you’ve surely noticed the TikTokification of just about everything. And as a B2B brand, you can’t ignore this shift in how people consume and share ideas.

Scroll through any feed and you’ll see the power of this now-ubiquitous format. A sharp, 20-second video clip can extend the half-life of your best ideas; it can pull a key takeaway out of your latest report, give it visual and emotional context, and send it rippling through executive feeds within hours. It can turn depth into reach, and thought leadership into momentum. And in 2025, the brands mastering this balance between insight and immediacy are the ones shaping the conversation.

This playbook lays out a practical framework for scaling short-form production without sacrificing your sanity (or your brand voice).

Why Invest in Short-Form Video Now?

In recent years, three converging forces have made the format indispensable.

1. Platform algorithms reward native video content. LinkedIn’s algorithm favors native uploads and visible engagement (likes, comments, and reshares) over external links. That means a short video posted directly to the feed will almost always travel farther than a link to your blog or YouTube page. YouTube itself is doubling down on Shorts as a discovery engine, logging over 70 billion daily views and driving new traffic to longer videos on the same channels.
2. Buyer behavior has fundamentally shifted. Short-form videos work because they fit into micro-moments: the scrolls between calls, inbox breaks, or quick research before a pitch. A single, well-edited clip can become both an external thought-leadership post and an internal enablement asset.
3. The ROI proof is in. HubSpot’s annual State of Marketing report notes that short-form leads in ROI, engagement, and lead generation compared to other video formats.

Here’s an example of why this format is so critical in 2025: Imagine your team hosts an insightful webinar that draws a few hundred live attendees. The response is positive, but small scale and contained. But a day later, your marketing team clips a 30-second highlight from the event, and suddenly, the insight is everywhere on LinkedIn — it’s even picking up traction on TikTok. Same idea. Same audience. Different velocity.

Formats That Work in B2B in 2025

Successful B2B video strategies rely on repeatable formats that teams can batch-produce efficiently.

These might include:

• Expert snippets and micro-takes (30–45 seconds) can work well for sharing perspectives on industry statistics/trends/reports or highlighting customer insights. Tap into your organization’s own subject-matter experts or internal data storytellers to surface fresh insights that customers or peers actually care about (e.g., a surprising trend from your latest benchmark report or a question your sales team keeps hearing).
• Explainer videos cut into digestible nuggets (30–60 seconds) break down complex frameworks, demonstrate before-and-after scenarios, or define emerging trends in three clear beats. The winning structure follows a simple pattern: Hook (identify the problem) → Core insight → Actionable step → Clear CTA.
• Behind-the-scenes content humanizes expertise while strengthening employer branding. For instance, show how customer success managers solve real client issues or how research teams uncover insights. Clips like these remind audiences that your company is made up of real people solving tangible problems.
• Series formats create viewing habits through familiar cadences like “60-Second Whiteboard,” “One Metric Monday,” or “3 Slides in 30 Seconds.” Consistent naming and timing can lower the cognitive load for viewers while simplifying planning and batch production for content teams.
• Strategic thought starters grab and maintain attention through provocative openings: “hot take” cold opens, “We were wrong about…” admissions, or direct challenges like, “If you only change one thing this quarter, make it this.”

Think of these formats as your highlight reel templates — they make it easier to share what your brand already knows, one clip at a time.

Production Techniques to Prioritize

In social feeds, clarity and pacing matter far more than cinematic production value. The most effective short-form clips hook viewers within the first second or two.

Smart editors also build in “pattern interrupts” every few seconds, swapping angles, adding B-roll, or flashing quick on-screen stats to keep attention from drifting. Because most platforms autoplay videos without sound, captions are critical. Burn them in, highlight key words for emphasis, and use visual cues like progress bars to nudge viewers toward completion.

Remember that you’re not striving for perfection; rather, you should aim to keep up momentum. An “80%-there” version published within 72 hours of a webinar or interview will outperform the flawless cut that ships a month late.

Finally, keep in mind that authenticity almost always beats polish. A quick, well-lit phone recording that feels human will connect better than a high-production shoot that feels staged.

To keep your process sustainable, treat short-form production like a feedback loop: Publish quickly, learn from watch-through data and comments, and adjust pacing or framing as you go. With accessible tools like Descript, CapCut, Adobe Premiere Rush, or VEED for editing — and Riverside, Zoom, or Loom for capture — teams no longer need full studio setups. Even AI-assisted repurposing tools such as OpusClip can help jump-start edits (though a human pass for quality and tone is still essential before anything goes live).

Platform-Specific Distribution and Optimization

Each platform has distinct engagement patterns and optimization requirements. To get the most out of every clip, tailor how you publish and frame it to match where your audience actually consumes content.

For instance:

• LinkedIn optimization centers on native uploads with strong opening lines and specific questions that encourage comments. Pin top comments with resource links and encourage authentic internal engagement within the first hour of posting to boost algorithmic distribution.
• YouTube Shorts require keyword-rich titles, series naming conventions, and dedicated Shorts playlists that encourage binge-watching while connecting to relevant long-form content on the same channel.
• Website integration through dedicated “Video Briefings” archives improves SEO through schema markup and interlinking with related guides and resources.
• Sales enablement packages should compile the top five performing clips monthly with specific use case guidance for prospecting, objection handling, and deal progression conversations.

No matter the platform, consistency beats complexity; the brands that show up regularly stay more visible.

From Long-Form to Shareable Short-Form: A Step-by-Step Guide

The most efficient B2B teams start with a single, insight-dense “anchor” asset, then break it into smaller, platform-ready pieces that keep the conversation going long after the original launch.

Here’s an example of what this process looks like step by step:

1. Choose the right anchor.

Start with something that already carries weight: a webinar, research report, executive interview, or customer roundtable. The best anchor content offers a clear point of view and connects directly to your broader marketing themes. Think: “What’s our take on this trend?” not “What can we summarize?”

2. Map out the moments worth sharing.

Before you ever hit record, list 8–15 potential short-form clips (“video atoms”) you could create from the anchor. These might include:

• A single strong stat or takeaway
• A myth your expert can debunk in 30 seconds
• A customer soundbite that illustrates impact
• A quick “how-we-did-it” tip from your team
• A question your audience asks again and again

Each one should have a rough script skeleton: a hook, a core insight (two or three lines max), a visual cue, and a clear call-to-action (CTA).

3. Batch record and assign clear roles.

Get everyone involved on the same page early. Strategists should identify anchor assets and tie them to upcoming campaigns. Subject-matter experts can block a short monthly recording session to capture multiple takes at once. Producers will handle editing, captioning, and versioning by platform. Social leads can write titles, schedule uploads, and engage in the first-hour comment window.

4. Build guardrails that let you move fast.

Nothing kills momentum faster than a 17-step approval chain. To avoid the death-by-approvals spiral, set up pre-approved brand templates for all the components you can. Maintain a short “greenlight list” of safe, recurring topics that can skip full legal review, and agree internally on a 48-hour turnaround standard from clip completion to publish.

5. Distribute and track smartly.

From one anchor asset, aim to create 10–15 video clips, a handful of static visuals, one short newsletter embed, and a quick sales-enablement reel. Assign each piece to a specific channel and goal (awareness, engagement, lead generation, or internal enablement) and monitor how each performs to refine the next round.

Turn Big Ideas into Bite-Sized Impact

The next time you publish a major report or host a webinar, keep the momentum going. Find the 30 seconds that say the most, put it in motion, and give your audience a reason to stop scrolling.

Attention may be fleeting, but influence compounds. Each short-form clip is a small opportunity to reinforce what your brand stands for — in your voice, on your timeline, and in front of the audiences that matter. When those moments stack up, they start to shape perception long after the video ends.

Learn how Contently helps B2B marketers turn depth into reach, and reach into measurable ROI.

Frequently Asked Questions (FAQs):

Q: What if my subject-matter experts hate being on camera?

Remind them that realness often performs better anyway. Try audio-over-PPT, screen recordings with voiceover, or micro-shorts where the expert speaks one idea directly. Over time, confidence follows repetition.

Q: Do I have to publish across all platforms at once?

Nope. It’s smarter to start where your audience already is (LinkedIn, Slack communities, internal channels) and scale gradually. Use your top-performing formats there before branching into Shorts, newsletters, or website archives.

Q: How do I make sure short-form video doesn’t become a siloed half-effort?

Embed it into the bigger content strategy. Map each clip to themes, campaigns or buyer stages. Use the same language, link back to related content, and integrate clips into newsletters, sales decks, or blog posts so they reinforce—not distract from—your core narrative.

The post The B2B Brand’s Guide to Short-Form Video in 2025 appeared first on Contently.

For brands accustomed to agonizing over campaign messaging for weeks, this speed is punishing. The challenge associated with keeping pace is twofold: Brands must learn to implement faster, lighter approval cycles, while ensuring their messaging still meets brand safety and compliance standards.

Luckily, social listening tools and strategies can help brands hone their radar and ride cultural momentum without becoming the corporate version of the Steve Buscemi “fellow kids” meme.

Here’s a guide on how to move from passive monitoring to active cultural intelligence.

Turning Sentiment into Strategy

Sentiment analysis has its place in reputation management, but on its own it misses social listening’s broader strategic potential. The most effective content teams go beyond reacting to brand mentions and use listening to surface cultural signals that can shape their next move.

Consider how this shift plays out in practice: A spike in negative comments may look like a reputational crisis at first glance, but viewed through a wider lens, the chatter could point to an emerging consumer need or even momentum around a competitor. What looks like a fire to put out may actually be an early market signal — and a chance to lead the conversation rather than chase it.

To leap from sentiment analysis to true strategic advantage, brands need to reframe listening as an input to planning, not just reporting. The teams that excel track core indicators like:

• Conversation velocity (how quickly discussions accelerate)
• Community reach (which groups drive the narrative)
• Emotional resonance (the intensity of engagement)

When these signals pinpoint a cultural insight, marketing teams should try to respond while the discussion is still gaining traction, ideally within a 48- to 72-hour window. Later than that and they risk looking reactive or irrelevant.

Spotting Micro-Virality Early

“Micro-virality” is a recent shift in how cultural trends emerge and spread. Unlike traditional viral content that explodes across demographics simultaneously, micro-viral moments ignite within specific communities before potentially crossing into mainstream consciousness. What’s more, the earliest ripples often start where brands aren’t looking: A meme circulating on a 20,000-member Discord server or a LinkedIn post gaining unusual traction among B2B marketers can signal tomorrow’s broader trend.

Detecting such early sparks is a challenge for even the most culturally clued-in brands. Standard analytics dashboards prioritize volume over velocity, missing these signals entirely.

Effective micro-virality detection demands monitoring beyond obvious channels. Brands should consider branching out beyond big, public platforms to digital subcultures like:

• Reddit threads
• Discord conversations
• Slack community reactions
• Twitch chat patterns

Such spaces can provide invaluable early warnings, but breaking in can be tricky if you show up heavy-handed. To earn trust, brands need to listen first and contribute in ways that feel native to the community rather than bolted on from the outside. They might also consider partnering with credible voices inside those spaces to amplify their presence organically and avoid looking like outsiders trying to hijack the conversation.

Trendjacking with Finesse

For every successful trendjacking moment, there are dozens of tone-deaf attempts that ultimately breed mockery and backlash instead of engagement and authentic connection. Brand missteps share common DNA: rushed execution, misunderstood tone, and forced brand insertion.

For instance, when the “#GirlDinner” trend exploded on TikTok, Popeyes tried to capitalize by launching a “Girl Dinner” menu made up of its side dishes. But instead of eliciting delight, the move was widely panned as lazy and off-base. What could have been an opportunity to align with Gen Z humor ended up highlighting the risks of jumping in without adding genuine value.

Brands that take the time to understand nuances are far more likely to show up in ways that feel relevant rather than opportunistic. That’s the difference social listening makes. HelloFresh, for example, actively tracks not just brand mentions but larger conversations around cooking habits, recipe trends, and packaging feedback. By analyzing these signals, the company adapts its product offerings and content strategy in real time.

Shaping the Content Machine from the Inside Out

Social listening’s greatest impact emerges when insights flow directly into content operations. Leading organizations are moving beyond surface metrics to let real-time audience intelligence inform four critical functions:

Editorial Calendar Evolution

Streaming platforms like Netflix have shown how closely tracking audience chatter can shape promotional priorities. Conversations around genres, moods, or cultural touchpoints often guide what gets emphasized in marketing campaigns — think highlighting “comfort viewing” during moments of collective stress.

Language and Tone Optimization

Ryanair has become a case study in how brands can use listening to inform voice and tone. Their cheeky, self-deprecating voice (“yes, our legroom is terrible, but our fares are cheap”) is a direct reflection of what they know people are already saying. Posts that mirror the humor of its audience consistently drive higher engagement, showing how listening can shape not just what a brand says, but how it says it.

Executive Positioning

Enterprise brands like Salesforce lean on trend monitoring to inform thought leadership. By paying attention to emerging business discussions (whether about AI, customer data, or sustainability), they position their executives to weigh in early and credibly.

Message Testing

Technology companies regularly validate their positioning by tracking how potential narratives land in the market. Slack’s evolution from “be less busy” to “digital HQ” reflects this kind of feedback loop, where conversation analysis helps sharpen the language before a campaign scales.

Practical Takeaways and Best Practices

Turning social listening from a passive tool into a performance driver requires discipline and integration. Here are three best practices to follow:

• Choose tools you’ll actually use. Start simple with native analytics (Twitter/X, TikTok, LinkedIn dashboards) to get comfortable tracking mentions and trends. As your needs grow, layer in dedicated tools like Brandwatch or Talkwalker for sentiment and community analysis. Larger enterprises may graduate to suites like Sprinklr or Sprout Social, but only when the scope and scale of insights demand it. The best tool is the one your team can use consistently, not the flashiest platform.
• Embed listening into existing workflows. Instead of creating extra steps, fold listening into routines you already run. Add a 10-minute trend scan to daily standups. Set up a Slack or Teams channel for real-time cultural alerts. Summarize key insights in weekly performance reviews so listening is always linked back to outcomes. Teams that systematize these habits are the ones that actually act on what they hear.
• Measure what matters. Don’t stop at tracking “mentions.” Tie listening to business outcomes. Measure speed-to-publish on trend-informed content (is your team able to turn ideas around within 24 hours?). Compare engagement lift between listening-driven posts and pre-planned content. Track how early your brand enters cultural conversations — and whether that timing translates into more relevance, share of voice, or even conversion.

The brands thriving in today’s compressed attention economy anticipate conversations, shape them, and build durable competitive advantage through cultural intelligence.

That shift requires reframing budgets and mindsets. A social listening line item is an investment in performance. In a landscape where cultural moments flare and fade faster than you can say “Barbenheimer,” the ability to detect, interpret, and respond in hours — not weeks — is a differentiator.

Don’t be the brand that shows up after the party’s already over. Social listening helps you arrive on time, and join the conversation in a way that feels welcome.

Social listening is only as powerful as the stories it informs. Learn how Contently can help your team build a content engine around real-time insights.

Frequently Asked Questions (FAQs)

What’s the biggest mistake brands make when they start?

Treating listening as a reporting function rather than an action driver. It’s easy to produce dashboards that look impressive but never inform a decision. The real value comes when insights directly change how you plan, create, or publish content.

Can social listening replace customer research?

Not entirely. Social listening shows you how people talk in public, often in real time. It complements surveys, focus groups, and user testing by surfacing unfiltered opinions and emerging behaviors — but it shouldn’t replace those methods.

How do I balance speed with brand safety?

Build lightweight guardrails: a pre-approved “do/don’t” list for language, topics, and tone; a short approval chain for rapid responses; and clear escalation paths for sensitive issues. This way, you can move quickly without exposing the brand to unnecessary risk.

The post Social Listening as Your Brand’s Secret Performance Tool appeared first on Contently.

Remember when the “I’m Feeling Lucky” button was Google’s biggest gamble? Now, it’s their entire business model, and your traffic is the casino’s take.

Your best-performing article still ranks #1, but traffic’s down 30%. Search your primary keyword and there it is: a Google AI Overview perfectly summarizing your content. No click required.

Welcome to the age of zero-click search, a blunt term that means exactly what it sounds like: searches where users get their answers without ever visiting your site. Industry veterans are calling the phenomenon “Google Zero” (less self-explanatory but just as ominous-sounding).

This new era means rankings alone no longer guarantee engagement. Your audience absorbs AI-generated answers directly on Google’s platform, bypassing your site entirely. This is the defining challenge of content marketing today. In an era dominated by AI-powered SERP previews, winning means creating digital destinations worth visiting, not just pages that pull rank.

Here’s how brands can adapt.

Understanding the Zero-Click Search Landscape

Google’s AI Mode represents a fundamental shift in how users source information online, compressing entire articles into punchy, AI-powered summaries. Users love the instant gratification. Brands and media companies, on the other hand, are in panic mode.SparkToro analysis from 2024 found that for every 1,000 Google searches in the US, only 360 clicks went to the open web.

Let that implication set in: Out of every 1,000 queries, 640 now lead to no clicked results.

Some publishers are impacted more than others

This trend has only ballooned in 2025, and click-through rates (CTRs) are plummeting. Publishers in reference verticals that have historically relied on search visibility report devastating traffic losses; some witness double-digit drops in referral visits, even as their keyword rankings hold steady. Semrush data finds that science, health, people & society, and law & government are the industries seeing the largest share of AI Overview growth.

The erosion is surgical, with Google’s AI scalpel removing the meat and leaving only bones:

• Recipe sites see their content distilled into lists and cook times.
• Long product reviews shrink to bite-sized bullets.
• Communities and dev sites have detailed Q&As sliced into decontextualized code snippets.

Zero-click search also strips away narrative, perspective, and experience, leaving only commoditized fragments that serve Google’s ecosystem.

The upside for marketers

But here’s what many panicked marketers miss: This isn’t a content apocalypse, but a process of natural selection. Commodity content is the dinosaur.

Most of the formats endangered by zero-click search were already oversaturated (all those “10 Best Tools for X” listicles you’ve been banging your head against a wall writing for the past decade). Many were competing on efficiency, completeness, and SEO tricks rather than on real innovation or brand distinction. Google Zero simply accelerates a reckoning that was always coming.

And here’s the twist: AI Search often surfaces sources that live well beyond the first page of Google’s traditional rankings. In other words, content that was once invisible in the old SEO hierarchy can suddenly become citable and top-of-mind in AI summaries. For brands willing to invest in distinctive, authoritative insights, the playing field may actually be more open than before.

Building a Destination Content Strategy

There are a few tactics for thriving in the Google Zero era. “Destination content,” for instance, inverts the classic SEO playbook. Forget adjusting for every last query and optimizing around keyword density; instead, focus on building branded content experiences that users actively seek out. These are digital destinations that drive interaction, build habit, and deliver value AI cannot compress.

Here are a few examples of what these strategies look like in practice:

1. Utility and interactivity

Example: Tools, assessments, and calculators. Google’s AI Overviews can summarize general best practices, frameworks, or even steps to use a tool, but they can’t generate dynamic, personalized outcomes tied to an individual user’s inputs, data, or context. ChatGPT can mimic personalization if you paste in content or data, but without integrations it can’t apply the proprietary scoring logic, benchmarks, or datasets that make branded tools defensible.

That unique value — rooted in owned IP and interactivity — is what keeps tools like HubSpot’s Website Grader a step ahead of zero-click answers. Users enter their site to get their specific recommendations, a direct exchange of effort for individualized insight that no AI summary can replicate.

2. Memorable Narrative and Voice

Example: Serialized storytelling and editorial franchises. Readers return for evolving narratives, strong opinions, and a distinct voice beyond just facts. (Think of the difference between Wikipedia and a respected analyst’s ongoing columns.) AI can summarize the facts, but not the evolving insight, context, or strategic nuance. For instance, Rare Beauty’s Substack leans into longform, behind-the-scenes storytelling that blends personal anecdotes, mental-health reflections, and candid product development updates. It stands out by offering authenticity tied deeply to the brand, giving readers a reason to subscribe rather than passively consume.

3. Deep, Engaging Experience

Example: Interactive flipbooks, quizzes, and content hubs. Build content networks that reward deeper exploration. Think of an immersive guide that walks a user through a complex topic using clickable flows, rich visuals, and progressive disclosure, instead of flattening content into a one-and-done summary. According to industry guides, formats like flipbooks, quizzes, polls, and interactive infographics are trending as tools for deeper engagement, boosting dwell time and even delivering audience insights.

4. Unmatched Credibility

Example: Subject matter expertise and original research. Every year, Edelman publishes its Trust Barometer, surveying more than 30,000 people across 28 countries on trust in business, media, government, and NGOs. The findings are widely cited by media outlets and executives, and the report’s methodology and charts compel readers to click through for detail.

Such research-driven content stands apart because it offers proprietary insights users can’t get anywhere else. It positions the brand as a trusted authority, fuels citations and coverage, and compels readers to click through for methodology and nuance.

Diversifying Discovery and Distribution

Smart brands aren’t putting all their chips on Google anymore. Instead, they’re engineering multiple discovery paths that are immune to AI summarization and constantly shifting SERP formats. The most resilient strategies balance owned channels, native participation, and interactive experiences — each reinforcing brand visibility outside of Google’s walls.

A few ways to do this include:

1. Email Newsletters

Email newsletters remain the gold standard of owned distribution. Immune to zero-click harvesting, newsletters deliver content directly to your audience on your own terms.

The strongest programs build around a clear editorial promise, tailored segmentation, and actionable next steps. Engagement metrics also look different here: unique opens,real click-to-open rates, and organic subscriber growth matter more than sheer volume. A newsletter welcome in a crowded inbox is a stronger signal of affinity than any search ranking.

2. Native Social Discovery

Native social discovery offers another durable channel. On Reddit, credibility comes from contributing expertise before dropping links — a strategy with extra upside, since Reddit is currently the single-largest source feeding AI search results. On LinkedIn, brands are finding traction with shareable carousels and concise insights designed for in-platform engagement. And in private communities like Slack groups, Discord servers, or other niche forums, value comes from participation, not promotion. Brands that show up with utility and authenticity win trust; those that push content for clicks don’t.

3. Content-Driven Events and Interactive Experiences

Webinars anchored by actionable tools, workshops, or playbooks create live value, while the content generated during those events (clips, FAQs, templates, case studies, etc.) can be repurposed across other touchpoints. The most effective teams go a step further, building “distribution kits” for every major asset. That means automated email sequences, platform-specific social adaptations, community prompts, and even snippets for sales enablement and internal knowledge transfer.

Redefining Performance Metrics in the Google Zero Era

Organic sessions, once the bedrock KPI for SEO success, are no longer reliable on their own. In the age of zero-click search, when Google’s AI Overviews siphon answers directly from your content, traffic becomes unpredictable. To future-proof a destination content strategy, brands need to shift from measuring visits to measuring value.

That involves monitoring a new set of metrics.

Relationship Metrics

Email signups, subscriber growth, retention, and community participation are now among the strongest signals that your content is worth returning to. Unlike a fleeting pageview, these metrics reflect ongoing trust and affinity.

Engagement Signals

These signals reveal depth of impact. Look beyond clicks to measures such as engaged reading time, scroll depth, recirculation into related articles, and direct repeat visits. Even the ratio of direct or bookmarked traffic to organic search traffic tells a story: Audiences are coming back because they want to, not because an algorithm sent them.

Utility and Habit Metrics

These indicators capture how your content integrates into users’ workflows. Tool completion rates, repeat usage of assessments, template downloads, calculator sessions, and resource revisits are strong indicators of content that delivers enduring value. A user who saves and reuses your template is worth far more than one who skims a single article.

Contextualized Traditional Metrics

Traditional SEO metrics like rankings and organic sessions still matter, but only in context. When search is one of many pipelines — not the only one — fluctuations lose their power to derail your growth.

Escaping the SERP

The rise of zero-click search doesn’t signal the death of content marketing, but it just might be the end of lazy content tactics. Google Zero is forcing brands to confront a truth long in the making: Visibility is meaningless without engagement. Traffic is volatile. Relationships endure.

Winning in this era means rethinking what you measure, how you distribute, and why your audience should care. It means building destinations worth seeking out, not just pages that happen to rank. You don’t have to fight AI or abandon SEO entirely — these remain important parts of the mix (and we’ll be covering tactics for LLM optimization in future articles).

But survival in the Google Zero era isn’t about winning clicks; it’s about winning commitment. Brands that build trusted relationships, deliver irreplaceable utility, and foster genuine communities will discover something liberating: when audiences choose to seek you out, no algorithm can make you disappear.

Ready to future-proof your content strategy? Partner with Contently to build destination experiences your audience can’t ignore.

Frequently Asked Questions (FAQs):

1. What exactly counts as “destination content”?Destination content is any experience your audience seeks out directly, rather than stumbling across through search. That could mean an interactive tool, a trusted newsletter, or a content hub with resources they bookmark and revisit. The key is habit and value: It has to be worth returning to even if Google never sends them.

2. Should we stop investing in SEO altogether?SEO is still important, but it shouldn’t be your only strategy. Think of it as one pipeline among many. Rankings and search traffic should be contextualized alongside relationship, engagement, and utility metrics. The real hedge against zero-click search is diversification.

3. How can smaller teams compete if they can’t build tools like HubSpot’s Website Grader?Interactivity doesn’t have to mean a massive engineering lift. Simple calculators, quizzes, or even well-structured templates can deliver personalized value. The goal is to create something useful enough that your audience wants to return.

The post ‘Destination Content’ Is a Lifeboat In the Google Zero Era appeared first on Contently.

It’s a cautionary tale replayed endlessly across one of the world’s most influential community-driven platforms.

But for brands, Reddit can no longer be dismissed as a marketing minefield to be avoided. The platform has around 108 million daily unique visitors worldwide, and users spend an average of around 16 minutes consuming content per session — far more time than on many other social platforms.

Perhaps most importantly, the site’s sprawling archive of authentic conversations now serves as one of the primary gatekeepers for AI Search. Google’s $60 million-per-year agreement to license Reddit content signals that this influence is now entrenched at the highest levels of SEO and GEO.

The message for marketers is unambiguous: The rules of digital influence are being drafted on Reddit, whether you’re participating or not.

Reddit Has Traditionally Been Thorny Territory for Brands

Historically, Reddit has been hostile to overt marketing efforts. The graveyard of brand blunders is filled with failed AMAs and cringey misfires: Nissan, REI, and travel ticketing site Skiplagged have been dragged for clumsy attempts at engagement. Electronic Arts’ now-infamous 2017 defense of “pay-to-win” mechanics in Star Wars Battlefront II earned the most downvoted comment in Reddit history.

The platform’s persistent hostility to brands is tied to three deeply structural and cultural dynamics:

1. Authenticity above all. Reddit’s entire ethos centers around authentic, user-first contributions rather than top-down brand messaging.
2. Community-driven scrutiny. Every subreddit has its own culture, rules, and moderators, which means outsiders — especially brands — are expected to adapt seamlessly to the community.
3. Anonymity breeds candor (and crass comments). Under the cloak of anonymity, Redditors can be brutally honest. They won’t hesitate to tell you exactly what they think of your brand, and they have a keen nose for sniffing out inauthenticity.

As a result of all of the above, traditional marketing tactics that may work elsewhere are swiftly rejected here. Marketing-speak is mocked, subtle self-promotion is quickly exposed, and contrived campaigns are dismantled within minutes. (If you want a vivid illustration of this, just head on over to r/HailCorporate, a subreddit dedicated to unmasking brand intrusion.)

Reddit’s upvote/downvote mechanics also impose real-time accountability on content. Public comment and post histories are visible by default — though since June 2025, users can hide it from their profiles. (Moderators, however, retain 28-day access.)

Finally, moderation can be a rude awakening for brands accustomed to sanitized feedback loops. Volunteer moderators enforce each subreddit’s rules publicly and quickly. Missteps can result in instant removal or bans. And unlike platforms where content disappears, Reddit has a long memory: Deleted posts often persist via archives and mirrors, which means that one ill-conceived campaign can haunt a company for years.

2025 Reddit: New Rules, New Tools, New Stakes

All that said, Reddit in 2025 is simply not the same beast it was in 2015. The platform is evolving, both in how it equips brands and in how its culture is shifting under the spotlight of AI search.

New Tools for Marketers

Recently, Reddit itself has signaled openness to brand partnerships and data licensing deals — a perhaps not-unrelated response to the widely publicized revenue struggles leading up to its 2024 IPO.

Whatever the motivation, over the past five years, the platform has rolled out a slew of products that signal a new posture toward brand participation, including:

• Reddit Pro: A native suite of analytics, post scheduling, and community insights to help brands engage more effectively.
• KarmaLab: Reddit’s in-house creative team, built to help brands craft content that won’t instantly get flamed.
• AMA Ads: Launched in 2025, these let brands promote upcoming Ask Me Anythings in relatively “safer spaces” than past free-for-alls.

These tools make it clear that Reddit is building out infrastructure to help brands participate without breaking community norms.

AI Search: Raising the Stakes for Authenticity

Despite the hurdles involved, there’s real urgency for brands to engage with Reddit. If you’re not active on the platform, you’re forfeiting control of how your brand is represented in AI-generated answers. Competitors or critics will happily fill the void.

A few clear indicators of Reddit’s growing influence in digital discovery include:

• AI systems cite Reddit constantly. After OpenAI’s July 2025 update, Reddit citations surged 87% and now account for over 10% of ChatGPT’s references.
• Search engines elevate Reddit threads. Google increasingly surfaces Reddit discussions when users want lived experiences, not polished marketing copy.
• Meritocracy rules. In Reddit’s culture, genuinely helpful contributions — not ad spend or brand size — determine visibility. Smaller, scrappy brands can punch above their weight if they provide genuine value.

The TL;DR: The world’s toughest focus group is now also the training ground for AI, and brands can’t afford to sit it out.

Subtle Cultural Shifts

The culture is also softening, at least in pockets. In certain subreddits, more specialized experts — engineers, academics, clinicians, etc. — are welcomed when they contribute genuine expertise. The implicit bargain is simple: Show up as a person first, a brand rep second.

How Brands Are Experimenting Successfully

Even with the tailwinds created by new tools and shifting community norms, it’s no excuse for brands to fall back on lazy campaigns. Success on Reddit requires a radically different playbook that centers patience, humility, relatability, empathy, and a focus on providing value.

A few brands getting it right:

• The Economist has run thoughtful AMAs with its editors, leaning into expertise rather than pushing subscriptions.
• Mint Mobile earned credibility by having employees (including Ryan Reynolds himself at times) participate directly in r/mintmobile, answering questions and cracking jokes rather than shilling.
• Purple Mattress launched r/LifeOnPurple, a community dedicated to sleep health. Instead of spamming product links, it became a global focus group where users traded advice.

There can be real results tied to these efforts. Mint Mobile, for instance, has seen over 44% of its social media referrals (more than 101,000 visits) come from Reddit.

On the other hand, there are real risks. Brands have very little real control over even the most branded of subreddits; a recent comment on the Purple community, r/LifeOnPurple (headline: “Purple has no moral fiber”) highlights how quickly conversations can turn critical.

Technical Brands and Radical Helpfulness

Technical audiences reward brands that bring real resources to the table. Sharing a GitHub repo, being candid about a failed migration, or troubleshooting alongside users builds more trust than a dozen blog posts.

Imagine for a moment a parallel universe to the scenario at the top of this article. In this alternative outcome, the same company’s lead engineer joins a thread about database performance concerns. She candidly shares the team’s journey migrating 50 million records, drops a link to their GitHub tool, and highlights both successes and setbacks. The community responds positively; screenshots begin circulating on X. Months later, her answer resurfaces when developers search for scaling advice.

This example showcases the real value of Reddit for brands: credibility meets connection at scale. In a world in which AI slop dominates feeds, people are flocking to Reddit presumably because of the very human, messy, and unfiltered exchanges that happen there. By showing up authentically — not aggressively — brands stand to win trust and gain relevance.

Contently helps the world’s top brands create stories that resonate with real people — and stand out to both audiences and AI.

Frequently Asked Questions (FAQs):

How do you measure success for brand activity on Reddit?

Engagement looks different on Reddit than on other platforms. Metrics include upvotes/downvotes, comment sentiment, referral traffic, and whether brand posts are organically referenced in other threads. Increasingly, success also means being cited frequently in AI Search results.

Can paid ads work on Reddit, or is organic participation the only path?

Reddit Ads can be effective, but they perform best when paired with authentic community engagement. A promoted AMA or native-style post without organic credibility often falls flat. Brands that invest in both paid reach plus ongoing community presence may see the strongest results.

What types of subreddits are most open to brand participation?

Smaller, niche, interest-driven communities (tech, health, hobbies) tend to be more receptive when brands bring expertise. Large default subreddits like r/funny or r/pics are usually hostile to overt marketing. The key is finding communities where your brand can add value to conversations that are already happening.

The post Reddit’s Resurgence: How the Internet’s Toughest Crowd Became AI’s Favorite Source appeared first on Contently.

Most of these assets lived squarely in marketing’s owned-and-operated channels. But sometime over the past decade, “content” stopped fitting neatly inside the marketing department. It has now spilled into every corner of the customer experience: product UI copy, customer support scripts, help-center articles, checkout flows, push notifications, and content to live on whatever buzzy new platform will inevitably debut next quarter.

The rise of AI Search represents another turning point. LLM and AI Search experiences often pull from authoritative and widely corroborated sources; brands with consistent, high-quality coverage tend to be cited more. It stands to reason that the more unified a message your brand delivers across every element of the digital ecosystem, the more likely it is that message will make it into AI-generated outputs.

As a result of all of the above, we’re seeing content career opportunities evolve. More and more companies are hiring roles like “Head of Content Experience” and “Director of Content Design,” marking a shift in how organizations think about the choreography of brand storytelling across multiple channels. In the past, marketing teams focused on what to say and where to publish it — landing pages, campaign assets, maybe a few gated PDFs. Today, the mandate is more ambitious: Design the entire content journey so that every touchpoint feels frictionless.

Why Content Experience Matters

With so many platforms and content formats competing for customer attention, brands face a real consistency challenge. People want to feel like the same company that reeled them in during a short-form video ad is also the one answering their questions clearly in a help article or walking them through a checkout process.

While a cohesive brand voice isn’t necessarily a silver bullet for sales, it can make your brand feel more professional and trustworthy. Salesforce research has found that 69% of customers expect consistent interactions across departments. At the same time, trust in corporations is reaching all-time lows; nearly three-quarters (72%) of consumers trust brands less than they did a year ago. 

In this climate, inconsistency can further chip away at confidence. Content experience is one of the levers brands can pull to counteract that.

Content Experience, Design, and Strategy: How Are They Different, and Where Do They Overlap?

Unlike content marketing, which often treats messaging as standalone assets, content experience treats content as infrastructure. It involves building the scaffolding that makes every interaction feel connected, from first click to task completion.

Here’s how the different roles tend to break down:

• Content Strategist: Sets the big-picture plan for what content to create, for whom, and why. They define voice/tone guidelines, editorial calendars, governance rules, and KPIs. A strategist might determine that the brand needs a library of onboarding tutorials, but they aren’t usually the ones crafting the microcopy inside the product.
• Content Designer: Works closely with UX and product teams to shape in-product copy and flows. They focus on clarity, accessibility, and task completion, writing for things like error messages, navigation labels, onboarding prompts, and help center articles — typically in the context of the interface.
• Content Experience Lead: Operates between strategy and design, with a systems lens. They ensure that content is consistent, discoverable, and adaptive across channels. This can include building modular content systems, implementing personalization logic, managing taxonomies, and coordinating delivery across web, app, email, and emerging platforms.

Unlike with traditional content marketing roles, content design and experience are not so much about producing more assets, but orchestrating existing ones into a coherent, user-friendly whole. The goal is to make sure that no matter where a customer encounters your brand — in an AI Search snippet, a push notification, or a complex product workflow — it feels like part of the same conversation.

These roles aren’t meant to work in silos; their real value shows when they collaborate across the full content lifecycle. A content strategist might partner with a content experience lead to ensure the high-level editorial vision translates into modular, reusable components that can live across multiple platforms. 

That same experience lead might work side by side with content designers to embed those components into product flows and ensure they’re consistent with voice, tone, and accessibility standards. In mature teams, these roles often sit in a shared content or UX organization, but they also act as liaisons to marketing, product, and customer support. The collaboration is cyclical: Strategy informs experience, experience informs design, and design feedback helps refine strategy.

Applying the Mindset Without a Dedicated Hire

You don’t need a Head of Content Experience to start thinking like one. Even without a specialized team, small shifts can move your organization toward a more cohesive, user-first content experience.

Here’s a quick-start playbook:

1. Audit your most important journeys

Map your top user tasks — whether that’s signing up for a trial, upgrading a plan, or finding help — across your site, docs, product UI, and support channels. Look for language gaps, redundant steps, or tonal mismatches that create friction or confusion.

2. Treat content as a design component

Work with your design system or dev team to bake voice, tone, terminology, and content patterns into the same place you keep visual components. If those standards live in your CMS and design files, they’re easier to apply consistently.

3. Create space for cross-functional reviews

Bring marketing, UX, and product teams into the same (virtual) room to critique real user flows. A quick “ad → landing page → trial → help doc” run-through can surface tone shifts and clarity issues that siloed reviews miss.

4. Pilot fixes in high-impact areas

You don’t have to revamp everything at once. Try a small, visible project like:

• • Launching a unified glossary so marketing, product, and support all use the same terms.
  • Applying progressive disclosure in onboarding copy to reduce overwhelm and speed up activation.

5. Give teams a cheat sheet

A single-page “language patterns” guide covering voice, tone, and terminology gives everyone a quick reference. When in doubt, they’ll have a shared source of truth.

While there’s a lot up in the air right now about the future of content (and the careers in this space), there’s one consistency we can count on: New channels will keep emerging. AI will keep reshaping how people discover and evaluate brands. The best way to future-proof your message is to make sure it already works everywhere — and that’s exactly what content experience thinking delivers.

At Contently, we help brands put these principles into practice, from developing voice and tone guides to creating modular, multi-channel content systems that keep messaging consistent everywhere your audience meets you. Learn more about our services, including our AI Studio, here.

Frequently Asked Questions (FAQs):

1. Do I need to hire all three roles — content strategist, content designer, and content experience lead?

Not necessarily. Many companies start by layering content experience thinking into existing roles. If you can’t staff all three, focus on cross-functional collaboration between marketing, UX, and product, and look for people who can work across silos.

2. How is “content experience” different from just good UX writing?

UX writing focuses on the clarity and usefulness of in-product copy. Content experience zooms out to orchestrate how all content — in product, marketing, and support — works together, so it feels like one cohesive brand conversation.

3. What’s the first step if my organization isn’t ready for a full content design or experience hire?

Start with an audit of your most important customer journeys and create a shared “language patterns” guide for all teams. Even small steps toward consistency can pay off quickly in trust, usability, and discoverability.

The post Strategy, Experience, Design: The Roles Redefining Content in 2025 appeared first on Contently.