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This column series looks at the most significant information and analytics obstacles facing modern companies and dives deep into effective usage cases that can help 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 trends to pay attention to in 2026: deflation of the AI bubble and subsequent hits to the economy; development of the "factory" facilities for all-in AI adapters; higher concentrate on generative AI as an organizational resource rather than a private one; continued development towards value from agentic AI, in spite of the hype; and ongoing concerns around who must manage information and AI.

This implies that forecasting enterprise adoption of AI is a bit simpler than anticipating innovation change in this, our third year of making AI predictions. Neither of us is a computer or cognitive researcher, so we generally keep away from prognostication about AI technology or the specific ways it will rot our brains (though we do anticipate that to be an ongoing phenomenon!).

We're also neither economists nor investment analysts, however that won't stop us from making our first forecast. Here are the emerging 2026 AI trends that leaders should understand and be prepared to act upon. Last year, the elephant in the AI space was the increase of agentic AI (and it's still clomping around; see below).

It's tough not to see the similarities to today's circumstance, including the sky-high evaluations of start-ups, the emphasis on user growth (keep in mind "eyeballs"?) over earnings, the media buzz, the expensive facilities buildout, etcetera, etcetera. The AI market and the world at large would probably gain from a small, sluggish leakage in the bubble.

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It won't take much for it to happen: a bad quarter for an important supplier, a Chinese AI model that's more affordable and simply as efficient as U.S. models (as we saw with the very first DeepSeek "crash" in January 2025), or a few AI spending pullbacks by large business consumers.

This column series looks at the biggest information and analytics challenges dealing with modern companies and dives deep into successful usage cases that can help other companies accelerate their AI progress. Thomas H. Davenport (@tdav) is the President's Distinguished Teacher of Info Innovation and Management and faculty director of the Metropoulos Institute for Technology and Entrepreneurship at Babson College, and a fellow of the MIT Effort on the Digital Economy.

Randy Bean (@randybeannvp) has been an advisor to Fortune 1000 companies on data and AI management for over 4 years. He is the author of Fail Fast, Learn Faster: Lessons in Data-Driven Management in an Age of Interruption, Big Data, and AI (Wiley, 2021).

Quantum computing has actually long felt like science fiction. Scientists are going into a "years, not decades" age where quantum makers will begin taking on problems classical computers can't, states Jason Zander, executive vice president of Microsoft Discovery and Quantum. That looming breakthrough, called quantum advantage, might help resolve society's most difficult challenges, Zander states.

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AI finds patterns in information. And quantum adds a new layer that will drive far greater precision for modeling particles and materials, he states.

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It's the first quantum chip developed utilizing topological qubits, a design that inherently makes delicate qubits more steady and trustworthy. It's also the only quantum option engineered to catch and right mistakes. That architecture paves the method for machines with countless qubits on a single chip, supplying the processing power required for intricate scientific and commercial issues.

"The future of AI and science will not just be faster, it will be essentially redefined." Lead image created by Kathy Oneha/ We. Communications. Illustrations produced with Create in Microsoft 365 Copilot. Story published on Dec. 8, 2025.

A year in tech can feel like a decade anywhere else.

IBM's Granite 3.0 had actually only just shown up. And the representative discussion was just starting: MCP had actually just gained traction in the spring, with a noteworthy endorsement from Sam Altman. In the world of infrastructure, chips and compute resources were ending up being scarce, offering new areas a competitive advantage. Over the last couple of weeks, IBM Believe consulted with a dozen specialists in techresearchers, creators and leaders from IBM and beyondto get their insights on what to expect in the year ahead.