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]]>October 2026
AI innovation continues at an extremely rapid pace regardless of regulation, bringing both enormous potential and risk to enterprises. The question businesses must ask is: how can they leverage AI to realize measurable productivity gains and a lasting reduction in operating expenses? Contact centers worldwide are considering their options as GenAI and Agentic AI are ideal for these people-intensive organizations. GenAI is useful for its ability to understand unstructured language, accurately identify “intents,” and apply this information to drive desired outcomes. Agentic AI is emerging as a powerful tool because it can apply automation and workflow logic, reducing the need for humans to handle routine customer inquiries and interactions. However, for AI to be viable in contact centers, it must cost less than the work it replaces and must also deliver sustained, measurable productivity gains. Additionally, leaders must factor in practical issues, such as the time, effort, and risk of implementing AI solutions.
DMG’s research has found that AI delivers real productivity benefits in specific and well-defined use cases, but the gains are smaller and slower than vendor claims, and they do not generally result in significant agent reduction. The applications delivering these benefits are:
1. Post-interaction summarization, also known as automated after-call summarization and wrap-up – this activity was not previously automated and therefore is an ideal task to address with AI. Companies have reported a 20% – 60% reduction in after-call wrap-up, depending on the contact center business function, complexity of topics discussed, and time allocated to after-call wrap up. Additional automation opportunities are extending the automated after-call wrap-up capability to include and automatically kick-off follow-up activities using agentic AI. (And there are potential benefits by using the after-call application to identify and document the follow-up activities, but to have them initiated by an agent or back-office employee.)
2. Conversational AI (CAI) – customer-facing self-service – first time users of CAI and companies that previously experienced a low automation rate (20% or less), such as many in health care, are experiencing a measurable increase in self-service utilization and call displacement when automating routine, high-volume inquiries. However, to realize full call displacement on routine calls, the organization must follow emerging CAI implementation best practices, which include starting with 1 or 2 high-volume use cases, actively involving the organization’s employees in the testing on an ongoing basis, creating and actively maintaining appropriate guardrails, and then scaling each use case. In most cases, the savings allowed organizations to scale and did not result in major agent layoffs.
3. Agent assist/agent augmentation, the agent-facing side of CAI – position these bots to identify and deliver appropriate case information, knowledge articles, policies and procedures, workflows, and sales and collections approaches directly to human agents as they interact with customers. This allows human agents to spend their time helping customers instead of searching for the information they need to resolve inquiries or sell. Sales and collections opportunities present a much larger upside potential than the savings coming from reducing agent average talk time in service organizations. However, to realize benefits, the solution must be highly customizable and easy to integrate and implement. Additionally, companies need a single source of data to ensure that customers receive the same answer whether they interact with a CAI solution of an agent. Organizations utilizing agent augmentation tools are benefiting by scaling their resources, rather than reducing their agents.
Final Thoughts
AI is delivering productivity gains in contact centers, but generally not in the form or at the size many enterprises were promised. The benefits are real for targeted and well-defined use cases, and organizations that implement them with discipline and best practices are realizing measurable results. In general, the gains are being used to absorb growth, improve the customer experience, and free agents to handle more complex and valuable work, not to drive large reductions in staff. However, for any AI initiative to be successful, it must cost less than the work it replaces and deliver gains that last.
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]]>The post AI Makes WFM More Essential appeared first on DMG Consulting.
]]>October 2026
Donna Fluss
View this article on the publisher’s website
The rapid rise of artificial intelligence has sparked a practical question across the customer experience industry: If AI is handling more interactions, do we still need workforce management (WFM)?
The assumption applied in the question is understandable; if automation reduces human workload, WFM should become less important. But the reality is that AI is not diminishing the need for WFM; AI is elevating it.
As organizations integrate AI into their CX ecosystems, WFM has become the operational intelligence layer that keeps hybrid human/AI workforces aligned, stable, and performing at scale. Work is changing, the workforce is changing, and WFM orchestrates both.
AI agents, virtual assistants, and multimodal conversational AI systems now handle a growing share of routine inquiries. But digital agents do not operate without constraints. They have concurrency limits, throughput ceilings, latency patterns, and escalation triggers. They behave like capacity-bearing resources, and they must be forecasted, monitored, and optimized like human agents. This is where WFM becomes indispensable. It anticipates when automation will reach its limits, when human expertise will be required, and how to align total capacity with demand. Instead of simply planning staffing levels, WFM becomes a real-time orchestration engine that shapes how work moves through the operation.
At the same time, the human role is evolving. Today’s agents function more like customer success advisors or brand ambassadors, requiring AI fluency, critical thinking, and the ability to manage complex customer journeys. These higher-skill, higher-value resources need thoughtful scheduling, equitable workload distribution, and wellness safeguards. WFM ensures these specialists are deployed effectively, supported appropriately, and protected from concurrent interaction overload and sudden spikes in escalations or emotionally intense work.
Traditional skill-based routing assumed a relatively stable flow of work. AI-driven routing does not. Adaptive intelligent routing models can override planned schedules, shift work across channels, and trigger intraday changes in real time. This creates a new operational reality that requires continuous, real-time WFM intelligence. WFM must integrate seamlessly with routing engines, provide transparency into automated decisions, and continuously rebalance staffing as conditions change. It becomes the stabilizing force that protects service levels when automation behaves dynamically.
AI initiatives rely on massive volumes of clean data. Contact centers generate operational signals that fuel enterprise AI models, including capacity, skills, backlog, service-level agreement exposure, and resource availability. WFM provides much of this intelligence.
As human agents take on more complex responsibilities, tracking the full customer journey becomes even more critical because complex issues span multiple steps, channels, and systems, and agents need that end-to-end context to resolve them effectively. WFM supplies the operational visibility that makes this possible and ensures that staffing, routing, and resource allocation align with customer expectations and operational goals.
Today’s customer journey moves across channels, modalities, and departments, often shifting between AI and human agents. WFM becomes the connective tissue that ensures every transition, whether bot to human, simple to complex, or front office to back office, is predictable, staffed, and executed consistently. It ensures that the promises made at the front door are met and ensures follow-through across the entire journey.
AI is not replacing WFM. It is transforming WFM into a real-time strategic optimization engine. Modern WFM orchestrates hybrid human/AI workforces, predicts when automation will reach capacity, aligns skills with demand, stabilizes service levels in volatile conditions, safeguards employee well-being, and ensures customer outcomes are delivered reliably. In this era, WFM is no longer a support function. It is the control tower for CX operations; it is the system that ensures organizations can scale, adapt, and deliver consistently excellent service in an environment defined by rapid change. WFM is becoming a strategic enabler for future-proofing CX operations, providing the intelligence and operational discipline required to manage increasingly complex blended workforces.
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]]>It is AI-orchestrated, not AI-replaced.
Donna Fluss
October 2026
One of the most persistent and mistaken narratives in enterprise software is that AI will render today’s platforms obsolete, including customer experience (CX) solutions.
The argument sounds compelling on the surface. If AI can respond to customer interactions, predict behavior, personalize responses, and automate workflows, why do organizations need contact center-as-a-service (CCaaS), workforce engagement management (WEM), or customer relationship management (CRM) solutions?
The answer is straightforward because AI is an intelligent orchestration layer, not a replacement for the functionality these platforms provide. AI enhances CX platforms and makes them smarter, faster, and more adaptive, but it does not eliminate the need for those platforms.
Let’s look at the reasons why and how AI helps them.
CCaaS solutions provide the essential connectivity and communications management that powers every inbound and outbound customer interaction in a contact center.
This includes voice and digital interactions, cross-channel session management, and quality of service (QoS). These are the transport, routing, and queuing infrastructure upon which AI operates.
What AI should be doing is making CCaaS platforms more intelligent:
AI, then, enables CCaaS platforms to adapt in real time, continuously learning from interaction outcomes to refine and optimize routing decisions.
The result is not a replacement of the CCaaS communication and transport layer, but a transformation of it from a reactive switching system to an intelligent orchestration engine.
Enterprises still need a reliable, secure, and scalable omnichannel communications infrastructure. AI does not provide a dial tone, manage telecommunications protocols, or ensure 99.999% system uptime. CCaaS does.
AI…enables CCaaS platforms to adapt in real time, continuously learning from interaction outcomes to refine and optimize routing decisions.
What’s changing is that AI elevates CCaaS from a rules-based utility into an intelligent strategic platform that improves CX outcomes, operational efficiency, and competitive differentiation.
If there is one area where the “AI replaces everything” narrative most misses the mark, it is WEM. The more AI an organization deploys, the greater its need for data, structure, governance, quality oversight, performance analytics, and continuous improvement, which are core disciplines of WEM.
Just like their human counterparts, AI agents need to be monitored, measured, coached, and optimized. Their knowledge bases must be accurate and kept current. Their outputs must meet compliance and quality standards and be structured to meet the needs of each audience they support.
Moreover, the front- and back-office operating environments WEM platforms support require adaptive and orchestrated workforce management (WFM) solutions that support hybrid environments with human and AI employees that handle voice, digital, and asynchronous interactions.
WEM is evolving from a set of internal- and external-oriented analytics solutions designed to optimize a human workforce into an operational backbone for managing hybrid human-AI contact centers and back-office operations.
Just like their human counterparts, AI agents need to be monitored, measured, coached, and optimized.
Modern WEM platforms deliver unified analytics that evaluate performance across all resources, AI agents, human agents, and self-service, providing managers with visibility into what is and isn’t working across channels and functions.
AI is transforming how WEM capabilities are delivered:
The functions that WEM platforms traditionally provide, including QM, recording, WFM, conversation analytics, performance management, coaching, and more, are not going away. What is changing is how these functions are performed and the outcomes they deliver, both of which are being enhanced by AI.
The need for these solutions is increasing as CX operations become more complex and dependent on AI technology, which requires a great deal of oversight, along with guardrails. AI agents must be continuously monitored for accuracy, compliance, and quality, just as human employees are.
CRM systems keep track of each customer’s relationship with an enterprise. This includes account details, along with their purchases, preferences, service inquiries, and cases.
Like CCaaS and WEM, AI enhances performance, flexibility, intelligence, and outputs from CRM solutions, but it does not eliminate the need for these capabilities.
AI automates data capture, surfaces customer signals, identifies actionable insights from structured and unstructured communications, proactively identifies customer needs, and detects at-risk customers.
Consequently, it enables companies to build effective customer engagement strategies that enhance loyalty and profitability.
AI-powered CRM transforms static customer records into dynamic relationship data and intelligence. But the underlying need for a system of record that captures, organizes, and makes accessible the full history of each customer relationship remains foundational for both front- and back-office operations.
But there is more to the AI story. Organizations attempting to deploy generative AI (Gen AI), large language models (LLMs), and agentic AI without a robust CRM platform discover that even the most sophisticated models are ineffective when operating on incomplete or fragmented customer data.
AI can generate personalized responses, predict behavior, automate decisions, and do much of the fulfillment, but only if it has access to accurate, comprehensive, and well-structured customer data.
For without a reliable system of record that captures the full history of each customer relationship, AI lacks the memory and context it needs to personalize interactions, anticipate needs, and deliver consistent experiences across channels and touchpoints.
AI does not make CRM obsolete; it makes it more essential. Like CCaaS and WEM, CRM is a foundational layer that gives AI structure and data.
The pattern across CCaaS, WEM, and CRM platforms is consistent: AI is not eliminating these platforms, but it is fundamentally enhancing what they can do.
AI changes the intelligence, precision, and speed at which a company operates.
Organizations should focus on the platforms’ integration and interoperability. In an AI-enabled CX environment, the value of CCaaS, WEM, and CRM increases when they share customer data, signals, and insights.
AI serves as the orchestration layer that provides the intelligence that ties these systems together, enabling companies to make real-time decisions that vastly improve both the CX and employee experience.
The debate over whether AI will replace CCaaS, WEM, and CRM reflects a fundamental misunderstanding of what these systems do and the role of AI.
AI is a transformative technology that enhances decision-making, automates routine tasks, and enables real-time adaptation. It is an intelligent orchestration layer that operates on top of the infrastructure and data layers to improve all aspects of the customer journey.
AI does not make CRM obsolete; it makes it more essential. Like CCaaS and WEM, CRM is a foundational layer that gives AI structure and data.
AI’s contribution to CX organizations depends on the volume and quality of the data moving through and across these platforms. However, contact centers still need communications platforms, operational management and analytics frameworks, and a system that tracks the entire customer relationship.
The real risk for enterprises is not that AI will make their CX platforms obsolete, but instead that they won’t move quickly to modernize those core platforms to take full advantage of what AI offers.
Meanwhile, the real risk for vendors that provide these platforms is that competitors will deliver AI-native or enhanced versions of the same capabilities faster and more effectively.
The future of CX technology is not about replacement; it is about transformation, integration, and intelligent orchestration of the foundational platforms that enterprises depend on to serve their customers.
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]]>The post What’s in a Name? More Than You Think appeared first on DMG Consulting.
]]>September 2026
There are many kinds of agents in the world. Real estate agents who help you find a home. Travel agents who help you book a vacation. Sports agents who negotiate million-dollar deals. Literary agents who discover the next New York Times bestseller. Insurance agents who help you prepare for the unexpected. Secret agents… well, who save the world.
And then there’s the person on the front line of the customer relationship and brand.
The one who handles the most emotionally charged, complex, high‑stakes “make or break” moments in the customer relationship. They’re the person who must solve problems that automation can’t touch (and occasionally creates): issues that are messy, ambiguous, and unscripted. The person who steps in when something derails, when something really matters, when someone is frustrated, confused, or worried.
And the industry decided to call that role… “agent.”
Oh, sure, other titles have been used over the years: representative, customer service rep (CSR), customer service associate, support specialist, call center rep, even the dehumanized “Tier 1.” But none of them stuck. Not because they were wrong, but because “agent” was, well, easy and generic and fit many types of contact centers and support organizations. It matched the perception of the role at the time: transactional, scripted, routine, low-complexity, low-authority. A name for a job that was assumed to be simple. A name for work that was assumed to be basic. A name for a role that was perceived to be entry-level, dispensable, and inexpensive.
And now? Now that the work has evolved into something that requires judgment, empathy, contextual reasoning, ethical decision making, and hybrid human/AI orchestration? The name feels like a relic, a fossil from another time. Because today’s frontline professionals are no longer performing routine tasks. They are:
This is knowledge work. This is cognitive work. This is emotional work. This is strategic work. This is CX specialist work. And with that evolution should come:
And here’s where the naming conversation gets even more interesting.
We now have AI agents: tireless, unflappable, programmatic. They never get overwhelmed. They never get upset. They never need PTO or a wellness break. They never have to talk someone down from an emotional cliff. Because when a situation requires empathy, nuance, or judgment, they escalate the customer to their human agent counterpart, the one who steps in to take the heat when things get complicated. So ironically, the human agent, the one doing the hardest work, now shares a title with a bot.
If that doesn’t tell us it’s time for a name upgrade, nothing will. Because names matter. Names shape perception. Names shape value. Names shape how organizations invest in a role. Names shape how leaders think about a role. Names shape how people feel about the work they do. And when the work evolves, the title should too.
So maybe it’s finally time to retire “agent” for the frontline role that has outgrown it. Maybe it’s time to give these professionals a name worthy of the complexity, judgment, and emotional intelligence they bring to every customer interaction.
We’re suggesting “CX specialist,” but we’re open to something even better. Tell us what you think.
Because what’s in a name? In this case — everything.
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]]>The post Will AI Help or Hurt Contact Center BPOs? appeared first on DMG Consulting.
]]>August 2026
CX and contact center industry leaders have discussed the demise of business process outsourcers (BPOs) for decades. Although the sector has experienced periodic downturns, it has proven to be resilient, and evidence suggests it will remain a strong and relevant vertical in the AI era. The current argument is that AI will significantly reduce the need for many human agents, leading some to assume that enterprise demand for BPO‑provided first‑ and second‑tier support will decline.
BPO leaders have shared with us that enterprise customers expected significant reductions in their human agent requirements throughout 2026 and 2027. However, these projections are not materializing. While there have been agent reductions attributed to AI initiatives such as conversational AI (CAI) self-service solutions, most enterprises are not realizing the staff reductions they expected. In some cases, enterprises that downsized their contact center workforce in anticipation of increased automation are now quietly rehiring to replace the agents they released. This is where contact center BPOs play a critical role, as they excel at rapidly supplying human agents to fill resource gaps when enterprises need to scale quickly.
The Data Tells a Different Story
The gap between AI expectations and reality continues to widen. A December 2025 Gartner survey found that only 20% of customer service leaders reduced headcount due to AI (source: Gartner Survey Finds Only 20% of Customer Service Leaders Report AI-Driven Headcount Reduction). In February 2026, Gartner projected that half of the companies that cut customer service staff will ultimately rehire by 2027. And, in a finding that should give every enterprise pause, Gartner also predicts that 50 percent of organizations will abandon efforts to reduce customer service workforce levels through AI altogether (source: Gartner Predicts 50% of Organizations Will Abandon Plans to Reduce Customer Service Workforce Due to AI).
AI is delivering measurable value, but not in the ways organizations initially anticipated. The impact is emerging in increased agent productivity and higher throughput, not mass layoffs. Most AI self-service deployments in contact centers today handle straightforward and repetitive tasks, including password resets, order status checks, and basic FAQ inquiries. Interactions requiring judgment, empathy, or complex problem‑solving still depend on skilled human agents, and BPOs remain a highly effective way to source and scale talent, particularly when rapid staffing ramp-up is required.
Bottom Line
AI will not eliminate the BPO industry. It will reshape it, as is the case for many business functions. BPOs that embrace AI as a complementary capability, integrate it into their operations, and evolve their business and pricing models will be well‑positioned for success. The BPOs that thrive during this period of technology and resource transformation will be those that adapt quickly and align their offerings with the evolving needs of their enterprise clients.
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]]>July 2026
AI is everywhere in CX right now: in vendor pitches, boardroom conversations, budget cycles, analyst reports, earnings calls, and conference presentations. But for all the excitement, one thing has become clear: many of the claims and assumptions about AI’s impact on CX organizations are not true. And those misconceptions are inflating expectations, slowing down progress, and creating avoidable failures.
DMG Consulting’s new executive brief Beyond the Myths: The Realities of AI in CX cuts through 20 of the most persistent myths distorting CX decision-making, providing the operational realities leaders need to understand to deliver AI initiatives with positive outcomes and results. The brief helps enterprise, CX, and IT leaders separate what’s real from what’s aspirational by grounding expectations in evidence rather than conflicting narratives. It gives leaders the necessary clarity, facts, and actionable guidance to make informed, practical decisions in a landscape defined by rapid and ongoing change.
The Myth Holding CX Back in 2026
Among the top 20 CX myths examined in our new executive brief, one stands out because it is the most pervasive. It influences nearly every conversation about AI in contact centers, from strategy sessions and evaluations to budget decisions and workforce planning. The problem is that this assumption is not only inaccurate; it is harmful and holding organizations back by distorting expectations and setting AI initiatives up to underperform.
Myth: AI is going to eliminate the need for human agents.
This belief has become so widespread that many organizations treat it as a foregone conclusion. Headlines amplify it. CEOs and CFOs ask about it. And CX leaders assume this is the ultimate goal, given the ongoing pressure to deliver more with fewer or less expensive resources.
The Reality: AI changes the work, not the workforce.
AI is already reducing handle time, automating repetitive tasks, and improving accuracy. But it does not eliminate the need for human judgment, empathy, escalation handling, or complex problem-solving. In fact, as AI takes on more low-value work, the remaining interactions become more complex, requiring more skilled agents, not fewer.
Organizations that deploy AI effectively are seeing higher agent productivity, better customer outcomes, and lower agent attrition. The goal is not replacement. The goal is reallocation: from repetitive to meaningful, from reactive to proactive, from transactional to relationship-oriented. AI becomes the capability multiplier, agents remain the differentiator. This is the shift that matters.
Why this myth is damaging
When leaders believe AI will replace agents, they make the wrong investments. They chase automation instead of orchestration. They prioritize cost-cutting over capability-building. They underinvest in human training, change management, and agent-assist tools, the elements that determine AI’s success. But the damage goes deeper. This myth fuels employee fear which negatively impacts their performance. It reduces agent engagement, slows adoption, and makes agents less willing to trust or use the AI tools designed to help them. The result is pilots that don’t deliver the expected cost savings, disappointed executives, and the belief that “AI didn’t work for us.” But the problem isn’t the technology; it’s the erroneous expectations set by the myth.
What leaders should focus on
The organizations realizing the strongest benefits from AI initiatives in 2026 are the ones that:
These are the realities that matter, and they are the foundation of DMG Consulting’s Beyond the Myths: The Realities of AI in CX.
What else you’ll find in the Brief
This is just one of the 20 most common misconceptions shaping the AI in CX landscape. The brief also addresses:
Each myth is paired with practical guidance grounded in reality to help leaders navigate the next wave of AI investment.
Why this matters now
2026 is the year CX leaders are moving from AI pilots and experimentation to operationalizing it. But for companies to succeed, they need clarity, not hype. The successful organizations will be the ones who understand what AI can and can’t do and what it takes to make it work. And that starts with a clear understanding of what AI is and isn’t capable of today and in the near-term.
Download Beyond the Myths: The Realities of AI in CX
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]]>View this article on the publisher’s website.
Ask the chief operating officer of any large company to describe the current state of their customer experience (CX) back-office (also referred to as the mid-office) operations and the answer will be remarkably consistent regardless of industry. These environments are highly fragmented, dependent on legacy and dated systems, lack transparency, and are anchored to manual workflows that haven’t changed much over the past few decades. Back-office departments lack real-time work-in-progress, aging, backlog, and service level agreement (SLA) risk visibility. Simply put, the back-office groups that support CX organizations have not kept pace with improvements and innovation in their companies’ customer-facing contact centers and customer service functions, and the consequences are showing up in lack of CX, cost, and organizational agility.
Executives are well aware of their CX back-office challenges, but they have been reluctant to invest in modernizing these areas, hoping the issues would quietly disappear without disrupting customers or operations. Organizations have been whittling down back-office operations over the past couple of decades, as reflected in the number of employees who work in these areas. In 2024, there were approximately 1.3 times as many back-office employees as front-office agents in enterprises (excluding government) in the U.S., compared to 2.5 times as many in 2014, according to the U.S. Bureau of Labor Statistics, based on DMG Consulting research and analysis.
Companies have made significant investments to improve the operational and cost-effectiveness of their back offices since 2000. These initiatives include hiring back-office specialty consultants; instituting business process management (BPM) programs; implementing enterprise resource planning (ERP) modules; and, in the past few years, customizing desktop analytics and other workforce engagement management (WEM) modules. While progress has been made in shrinking the number of employees dedicated to these functions, the underlying operational issues that plague the customer journey remain. The primary obstacle has not been a lack of available solutions; it has been organizational politics and siloed decision making that have resisted end-to-end change. DMG expects artificial intelligence to be the catalyst that finally overcomes these dynamics, as the cost and CX implications of inaction become impossible to defend.
Agentic AI has the potential to resolve the CX back-office challenge by automating a significant percentage of customer requests and tasks that are currently handed off by the front office for resolution. Today, customer-facing resources (human and automated) resolve 85 percent to 95 percent of inquires during the initial contact. (The percentage varies by vertical and greatly reflects investments in intelligent automation, workflow, AI, and training. Financial services are on the high end of point-of-contact resolution, and healthcare is on the low end.) The remaining 5 percent to 15 percent of interactions, ones that typically require deeper research, multi-department fulfillment, or exception handling, are routed to back-office teams, where they enter an operating environment that is far less automated, transparent, or efficient. This creates friction because these are often more complex or already delayed interactions, yet they are sent to the CX organization that is least equipped to handle them with speed and transparency.
Agentic AI can fundamentally alter this dynamic. Unlike traditional workflow automation, which is designed to follow rigid, predefined rules, agentic AI can reason and make decisions, access multiple systems and data sources, and execute multistep processes autonomously. However, it is a best practice to have a human in the loop when these capabilities are rolled out and to perform ongoing automated quality management (AQM) on all of the interactions. An agentic AI system can receive a back-office work item, identify its context, retrieve the relevant information from disparate systems (internal, external, and third-party), apply business rules and judgment, and either resolve the task outright or advance it to the point where only a brief human review is needed.
In practical terms, this means that a significant share of the work currently sent to the back office can be resolved in minutes rather than days, often without human intervention. The implications for improving the CX and employee experience (EX), reducing operating expenses, and strengthening compliance are substantial. Back-office tasks that once took days to complete can be handled in near real time. Customers and front-office employees will no longer experience the black hole where front-end commitments are delayed or broken.
The most significant requirement is for executives to combine the front and back office into a cohesive CX function by eliminating organizational silos. Here are the remaining high-level steps to success:
DMG recommends starting small, with one or two agentic workflows, to allow time to learn to use the AI tool and how to best test and implement each one.
Companies that deploy agentic AI workflows to automate the handling and resolution of work items passed from front-office teams to back-office teams are expected to realize savings equivalent to a reduction of at least 40 percent of back-office operating costs. This does not mean all savings come from reducing back-office employees; efficiencies will also come from organizational improvements. It is essential to retain the appropriate resources from both teams, as the complexity of exceptions and work items will increase as more activities are automated.
Organizations that deploy agentic AI workflows to further automate the flow of work from the front office will experience a measurable improvement in customer satisfaction, first-contact resolution rates, and the end-to-end customer journey. Employee satisfaction will also improve due to fewer customer complaints and faster, more accurate resolutions.
The CX back-office problem is not new, but the ability to finally solve it is. Agentic AI gives enterprises the tools to automate what decades of process improvement were unable to achieve. DMG recommends that CX leaders start by unifying their front- and back-office CX operations and launching a targeted rollout of agentic workflows to eliminate servicing silos, delays, and costly broken promises that are no longer tolerated by customers.
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]]>*** MEDIA ALERT ***
Who: DMG Consulting LLC, a leading provider of contact center, back-office, and real-time analytics market research and consulting services
What: Releases Beyond the Myths: The Realities of AI in CX Executive Brief
When: Today, 23 June, 2026
Where: Available on the DMG Consulting website
Background:
West Orange, NJ, June 23, 2026 — DMG Consulting LLC, a leading provider of AI-enabled CX, contact center, and back-office market research and consulting services, today released its Beyond the Myths: The Realities of AI in CX Executive Brief. Designed to help CX, IT, and enterprise leaders navigate an environment where AI narratives are evolving faster than organizations can evaluate them, the brief examines 20 of the most persistent and consequential myths shaping CX AI decision-making. It provides a grounded, evidence‑based view of what AI can reliably deliver today, its likely evolution, and the considerations leaders must weigh as they plan for what comes next.
The Executive Brief highlights the gap between AI expectations and operational reality, which is slowing adoption and contributing to stalled initiatives, misallocated budgets, and unmet expectations. While AI is already delivering measurable benefits across customer experience (CX) environments, many organizations are being asked to make strategic decisions based on information that is incomplete, oversimplified, or aspirational. These misconceptions are particularly challenging as enterprises accelerate investing in AI-enabled platforms, orchestration layers, and automation designed to modernize contact centers and improve customer and employee experiences.
“The goal of this executive brief, Beyond the Myths: The Realities of AI in CX, is to accelerate the adoption of AI by helping leaders base their decisions on evidence rather than hype,” said Donna Fluss, President of DMG Consulting LLC. “By separating current AI capabilities from what is coming in the future, this brief gives CX, IT, and enterprise leaders a practical foundation for planning, investing, and executing AI initiatives with confidence.”
The Executive Brief makes it clear that while AI is the most transformative technology to enter the CX market, its success depends on realistic expectations, strong governance, and a commitment to continuous improvement. For enterprises, the brief provides a framework to guide AI planning, investment, and deployment decisions. For vendors, it highlights the capabilities, practices, and messaging that will resonate most with buyers seeking clarity in a rapidly evolving landscape.
A complimentary copy of the executive brief is available at dmgconsult.com.
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]]>June 2026
We were recently asked to help build a contact center (also known as a CX organization) from the ground up for a new AI start-up. The opportunity sounded straightforward: what system and applications are needed? However, the answer doesn’t start with technology; it starts with strategy. Before determining system and application categories, an organization must first define the contact center’s mission in alignment with the company’s broader objectives. Is this a digital‑first operation? An AI‑centric service model? Something else? The strategic foundation and mission dictate the functionality, underlying technologies, and applications needed to achieve the goal. And while most contact centers share a standard set of functions and core components, AI’s rapid rise is reshaping what that stack looks like, making the old blueprint less of a given and more of a starting point for a different kind of build.
The most effective contact center build starts by identifying the functional capabilities required to execute the strategy and then mapping those needs to specific solutions or platforms. This approach enables organizations to document their requirements before evaluating vendors. Start by asking: what does the customer journey require? Will front and back-office operations be combined or operate as silos? Is omnichannel (voice and digital) intelligent routing necessary, or will this be a digital-only environment? Are both inbound and outbound interactions standard? What role will AI play in the contact center? Will self-service be the primary servicing approach, or are you striving to provide a balanced human/automated support organization? What AI tools do you need to support agents? What system integrations are needed today and in the next 3 to 5 years? How will security be addressed? While there are many more questions to ask, avoid relying on past assumptions and focus on defining functional characteristics for the customer journey you want to deliver in the future.
With functional requirements documented, the next step is to organize them into logical categories to determine the systems or platforms required to build your contact center. It is also good practice to prioritize your company’s requirements as contact centers use more than 45 different systems/applications, several of which overlap. But this is where things get interesting: the traditional way of selling these solutions has changed and continues to evolve. Vendors that came to market with a point solution, such as automated quality management (AQM) or workforce management (WFM), are expanding their offerings to cover multiple product categories. At the same time, contact center as a service (CCaaS) providers that started as routing and queuing engines are incorporating functionalities, such as AQM, real-time-guidance (RTG), and automated post-interaction summarization, that previously required separate purchases. The result is a CX technology market where category lines are blurred, vendor capabilities overlap, and the best solution for a given function may be bundled into a platform originally targeted for a different, albeit complementary purpose. For these reasons, DMG recommends that companies:
1. Separate their functional requirements into logical CX IT categories so that they have insight into the actual systems they need, while keeping an open mind about how they may acquire them. The more common categories are:
2. Conduct market research and speak to an industry analyst about the most effective approach for acquiring the solutions your organization requires. For small and mid-sized contact centers, this will likely mean purchasing an open platform that addresses most of your functional requirements. For highly complex organizations with internal resources to implement, integrate, and maintain their own systems, it may be more effective to purchase composable point solutions. Or it could be a combination of a broad platform along with point solutions to fill functional gaps. The tradeoff is that a platform provides relative simplicity, and composable provides greater flexibility. What is essential is selecting platforms and solutions that come with an open integration layer, whether through model context protocol (MCP) or APIs to many of the other AI and CX applications.
At this point, enterprises are in a good position to evaluate vendors and their solutions. Identify vendors whose solutions align with your documented requirements, and use those requirements to guide the evaluation and demo process. Keep the conversation grounded in what your organization needs, not what each vendor wants to sell. While it is helpful to learn about new capabilities and market direction, avoid letting vendors reshape your priorities or expand your scope beyond what your strategy requires.
At the same time, keep an open mind about how vendors are solving problems differently than expected. AI is enabling approaches that did not exist even a year ago, and a solution that addresses your requirements in an unconventional way may be more effective and future-proof than one that simply follows the traditional model. The goal is to stay anchored to your requirements while remaining open to better ways of meeting them.
The contact center technology landscape is changing so quickly that over the next few years, it will be nearly impossible for any enterprise to remain fully current. The goal is to set up a CX technology infrastructure that can quickly and relatively easily evolve to take advantage of market innovation. Start with a clear strategy, select solutions with open integration capabilities, and design your operating environment for both effectiveness and adaptability. While AI is transforming the technology market, CX organizations still require many of the same core capabilities they needed in the past. What’s changing is how they are delivered, how they perform, and the outcomes they produce. The contact centers that thrive and consistently deliver an outstanding CX will be built with the flexibility to adjust as the market, technology, and customer expectations evolve.
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]]>May 21, 2026
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Recent headlines suggest that artificial intelligence will replace traditional enterprise software (even though AI is itself a form of software). Whether or not that is true, the narrative alone is reshaping how the market values the world’s largest software companies and how enterprises as buyers are planning their future IT investments. AI technology is still maturing, but it is already strongly influencing how companies think about the delivery of their products and services.
Although this transformation will be difficult and expensive for the market, the long-term outcome will be positive, as customer experience organizations have been stuck in their ways for more than 25 years and will need a reset to be more effective. The question is how this is going to play out, and which vendors will win the battle to become the core AI platform (or orchestration layer) for enterprise operations. This shift is playing out across verticals, but with particularly significant implications for CX organizations. The contact center/CX software industry has seen platform battles before, including the fight to own the agent desktop, which produced no clear winner.
Every major category of providers, including AI platforms, hyperscalers, enterprise software, contact center-as-a-service (CCaaS), CRM, and other CX specialists, is striving to become the central hub where enterprise data, processes, and AI converge. Each wants to be perceived as the unified agentic layer that orchestrates autonomous AI across the enterprise, connecting it to the systems and data it needs to act.
The ambition of the vendors positioning themselves in this area is as ruthless as it is ambiguous, and there is no consensus on what these AI orchestration platforms will become from a theoretical or practical perspective. Most business and IT leaders are trying to get a handle on how AI will impact their day-to-day operations, especially how autonomous agents will be managed and what governance, integration, and trust controls will be required.
Vendors are staking out positions with broad promises about AI’s future, but they are making few commitments, forcing companies to come up with their own vision of the future even as the landscape shifts daily. Organizations, guided by their strategic technology providers, are being pushed to make major platform decisions before the underlying model for AI-driven operations is fully defined. The vendors, many of which didn’t expect to be fighting for their existence, are trying to convince their customers that they are the “right” and safe choice, even though they are uncertain about their future. The real risk is committing before the operating model itself is proven.
As the category matures, organizations should focus on proof points that indicate a platform is ready for long-term commitment. This includes demonstrable governance controls for autonomous agents, transparent integration models that reduce rather than increase technical debt, trusted data foundations and quality frameworks, and evidence that the platform can operate across the full customer journey rather than isolated use cases. These capabilities will help companies distinguish between vendors making broad promises and those building the foundations of a sustainable agentic architecture.
While the path ahead is uncertain, waiting passively is not a viable strategy. The current conservative approach for companies is not to pick a single vendor to manage their AI and agentic orchestration layer. Instead, companies should set themselves up for success in the next two to three years by staying architecturally flexible. Invest in functions and activities that will position the company for success in the future, including cleaning their data and establishing processes to keep it that way, reimagining their processes (which for CX organizations should be the entire customer journey), developing an appropriate governance framework and practices, and building their AI literacy and resources to make the right strategic moves when the time and technology are better understood.
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