Will Your Enterprise Be Powered By AI?

This column series takes a look at the most significant information and analytics difficulties facing modern-day business and dives deep into successful usage cases that can assist other companies accelerate their AI progress. Carolyn Geason-Beissel/MIT SMR Getty Images MIT SMR writers Thomas H. Davenport and Randy Bean see five AI patterns to focus on in 2026: deflation of the AI bubble and subsequent hits to the economy; growth of the "factory" facilities for all-in AI adapters; higher concentrate on generative AI as an organizational resource rather than a specific one; continued development toward value from agentic AI, despite the hype; and continuous questions around who need to handle data and AI.

This means that forecasting enterprise adoption of AI is a bit simpler than forecasting technology change in this, our third year of making AI predictions. Neither people is a computer or cognitive researcher, so we normally stay away from prognostication about AI innovation or the specific methods it will rot our brains (though we do expect that to be an ongoing phenomenon!).

We're also neither financial experts nor financial investment analysts, but that won't stop us from making our first forecast. Here are the emerging 2026 AI patterns that leaders need to comprehend and be prepared to act on. In 2015, the elephant in the AI room was the rise of agentic AI (and it's still clomping around; see below).

It's hard not to see the similarities to today's scenario, including the sky-high evaluations of startups, the focus on user growth (remember "eyeballs"?) over profits, the media buzz, the costly facilities buildout, etcetera, etcetera. The AI market and the world at large would probably benefit from a small, sluggish leakage in the bubble.

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Navigating the Landscape of GCC AI

It will not take much for it to take place: a bad quarter for an essential vendor, a Chinese AI model that's much more affordable and simply as efficient as U.S. designs (as we saw with the very first DeepSeek "crash" in January 2025), or a few AI spending pullbacks by big business clients.

This column series takes a look at the biggest data and analytics difficulties dealing with modern business and dives deep into successful use cases that can help other organizations accelerate their AI development. Thomas H. Davenport (@tdav) is the President's Distinguished Professor of Infotech and Management and professors director of the Metropoulos Institute for Technology and Entrepreneurship at Babson College, and a fellow of the MIT Initiative on the Digital Economy.

Randy Bean (@randybeannvp) has been an advisor to Fortune 1000 organizations on data and AI management for over 4 years. He is the author of Fail Quick, Find Out Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021).

Quantum computing has long seemed like science fiction. But researchers are entering a "years, not years" era where quantum makers will start tackling problems classical computers can't, states Jason Zander, executive vice president of Microsoft Discovery and Quantum. That looming breakthrough, called quantum benefit, might assist resolve society's toughest challenges, Zander says.

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AI discovers patterns in information. And quantum includes a new layer that will drive far higher precision for modeling particles and products, he says.

Establishing the Tech Hub in the Middle East

It's the very first quantum chip built utilizing topological qubits, a design that naturally makes delicate qubits more steady and trusted. It's likewise the only quantum option engineered to capture and right errors. That architecture leads the way for machines with countless qubits on a single chip, offering the processing power needed for intricate scientific and commercial problems.

Lead image created by Kathy Oneha/ We. Illustrations produced with Produce in Microsoft 365 Copilot.

A year in tech can feel like a years anywhere else. Think of it: a year ago, we were discussing how ChatGPT wasn't able to count the number of "r"s in "strawberry." Reasoning models from Chinese frontier labs (like DeepSeek-R1) had not taken the world by storm, and neither had open-source reasoning agents.

IBM's Granite 3.0 had only just shown up. And the agent conversation was just beginning: MCP had simply gained traction in the spring, with a noteworthy endorsement from Sam Altman. In the world of facilities, chips and compute resources were becoming scarce, offering brand-new areas a competitive advantage. Over the last couple of weeks, IBM Believe talked with a dozen specialists in techresearchers, creators and leaders from IBM and beyondto get their insights on what to anticipate in the year ahead.