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62% of finance executives have taken on enterprise technology or AI strategy leadership responsibilities. But what does that look like in practice?

AI is transforming how organizations make decisions, but many leadership teams are still speaking different languages when it comes to data, risk, governance and accountability.

AI advantage is built before you can measure it. But it’s not about some people working with better tools. The difference is organizational readiness. 

AI can raise the floor for everyone, but leadership advantage comes from using AI to break the ceiling, redefining how the business competes.

Discover how COOs can redesign their operating model to turn AI investments into measurable business value.

Real AI value comes from redesigning how work gets done. HR needs to help shape that shift from the start.

CFOs need to get comfortable with being uncomfortable because ambiguity is not a reason to wait. It’s a reason to design your AI investment approach differently.

The real opportunity isn’t using AI to speed up old workflows. It’s using AI to redesign work and reinvest the gains in growth.

Enterprises have invested heavily in AI, yet most still struggle to turn capability into business performance.

Charged with scaling their initial but siloed successes, chief AI officers push to unite the C-suite behind a shared AI vision.

Explore free learning experiences that help you build the skills employers increasingly expect, from AI collaboration and problem-solving to system thinking and secure software development.

Explore a wealth of AI topics, from the working fundamentals of artificial intelligence to the management, governance, use cases and industry applications.

Explore free learning experiences that help you build the skills employers increasingly expect, from AI collaboration and problem-solving to system thinking and secure software development.

The rise of AI coding tools reflects a broader change in how engineers navigate massive codebases, technical documentation and increasingly complex systems. See how one research scientist has embedded AI in her daily work.

Unpack the most important news and announcements for DevOps leaders and professionals coming out of IBM Think 2026.

The better AI agents get, the harder they fail. Learn the new critical thinking skills to keep yours in check.

Long called “black boxes,” AI models are increasingly yielding their secret inner workings—even, this playful guide shows, in terms that anyone can understand.

Spec-driven development (SDD) is a software methodology in which a detailed specification of implementation details is authored and agreed upon before development begins.

“Vibe coding” is a new and loosely defined term in software development that refers to the practice of prompting AI tools to generate code rather than writing code manually.

Agentic engineering is the practice of using engineering expertise to orchestrate and oversee AI agents through the software development process.

At the core of agentic coding are coding agents—AI systems that combine reasoning capabilities from LLMs with access to coding tools and execution environments.

The race to build an AI “superforecaster” is making human forecasters more valuable. The harder part: Helping organizations pinpoint what they want to achieve.

Georgia’s Seckinger High teaches AI as a superpower. Its students are beating the state average in math, reading and science.

People are scared that Anthropic’s text watermark will be a scarlet letter on anyone who uses AI. But that’s not how it works.

The better AI agents get, the harder they fail. Learn the new critical thinking skills to keep yours in check.

Long called “black boxes,” AI models are increasingly yielding their secret inner workings—even, this playful guide shows, in terms that anyone can understand.

Quantum computers could soon break the encryption schemes that protect the internet. Here’s how enterprises like Cloudflare and Signal prepared.

AI still struggles to recognize sarcasm. Researchers haven’t given up.

Synthetic data helps save lives and protect customers. But combining privacy and accuracy remains a challenge.

Companies with a CAIO had a 5% higher return on their AI investments. How did they do it?

Critics call orbital data centers “peak insanity.” Engineers are chipping away at the challenges.

17 experts predict what’s ahead for AI, quantum and security

Their emails landed in clients’ spam folders. Getting back to the inbox took months.

The better AI agents get, the harder they fail. Learn the new critical thinking skills to keep yours in check.

AI coding tools are putting senior software engineers back behind the keyboard after years spent managing teams and designing systems.

Learn how retailers can embed AI agents directly into their operations to monitor real-time signals and autonomously execute workflows.

Because agentic AI is a rapidly developing field, the threat landscape is evolving in real time alongside the technology.

Banks must move from box-checking compliance to intelligent, goal-driven governance.

Replacing human agents with chatbots that can’t resolve issues leaves customers frustrated and unsupported. There’s a better way.

A customer wakes up to an email: “Your order has been canceled.” But they didn’t cancel it. Your system did.

Learn about supervising, coordinating and governing AI agents across an organization.

A new paper from researchers at Microsoft points at a major problem with AI workflows.

For companies pursuing agentic transformation, the challenge is no longer getting to real-world results—it’s controlling what happens next.

Learn about a structured, scalable end-to-end methodology for building and managing enterprise AI agents.

An IBM developer says AI tools now handle much of the early work of building demos, letting him jump straight into experimentation.

See how the world’s leading brands are charting the path to build smarter businesses with agentic AI.

Build an AI-powered support agent that serves customers across different languages, powered by cross-lingual LLMs and watsonx Orchestrate.

The organizations that win the agent era can govern, orchestrate and scale an agent workforce across clouds, vendors and systems.

The future of automation is a deliberate balance of agentic and deterministic approaches.

A comprehensive resource with explainers and hands-on tutorials

IBM connected two large, ultracold cryogenic modules, clearing an engineering hurdle on the way to a much larger quantum computer.

A surprise discovery about atom-thin materials has earned physicist Eva Andrei the Kavli Prize.

Anderon, a new IBM company, will be America's first pure-play quantum foundry

Recent results show how quantum computers could begin to tackle some of the most computationally demanding tasks in finance.

New demonstration of a 52-qubit quantum Fourier transform shows how ecosystem innovation improves algorithm performance on quantum hardware.

“Most of the applications are probably yet to be discovered.”

Jay Gambetta, Director of IBM Research, believes quantum computing could be just as transformative: not just a new tool, but a new way of thinking.

Learn about state-of-the-art processing hardware that solves complex problems using quantum mechanics.

Brassard spent forty years warning the world that its secrets could be stolen.

“You can have 10 things that are wonderful, but if one thing is not right, nothing works.”

Mainframes such as IBM Z have hidden in plain sight and powered the world of commerce since the 1960s.

Learn how hardware capability enables the matrix math required to run LLMs, and hear creative examples of how to put AI to work.

With the majority of enterprise data residing on them, mainframes are a perfect fit for integrated AI.

Learn how an AI stack provides a structure for building AI solutions.

An intentional hybrid cloud approach can help your business improve operational agility and scalability.

As power limits and GPU scarcity reshape the AI landscape, leading organizations are shifting from deployment to optimization.

If succeeding with AI means redirecting your enterprise infrastructure, will the gains outweigh the costs?

The success of AI initiatives heavily depends on the underlying infrastructure’s ability to support demanding workloads efficiently.

Remove the friction that keeps data, workflows and intelligence from moving at the speed the business requires.

Learn how these two deployment types differ in running AI models and where they store and process data.

Making intentional architecture decisions is key to amplifying the value of hybrid cloud and AI.

"Attackers simply do not need zero-days, they just need valid credentials and a little bit of patience."

An IBM expert sees a few big shifts reshaping the data stack all at once.

17 experts predict the year ahead.

IBM asked more than 1,000 C-suite executives how economic, geopolitical and technological factors would influence their decision-making in 2026.

In 2026, AI adoption will force organizations to make their observability strategies more intelligent, cost-effective and compatible with open standards.

The Mixture of Experts panelists recap 2025 and predict 2026.

2026 is an inflection point for enterprise AI: the agentic pivot.

New risks from autonomous AI and AI-assisted social engineering

Explore Gartner’s nine predictions for 2026 and how businesses can prepare.

The coming 12 months will be defined by the scramble to develop wiser models that think before they speak and do more with less.