Will Your Enterprise Be Powered By Automation?

This column series looks at the greatest information and analytics difficulties facing modern-day companies and dives deep into successful usage cases that can assist other organizations accelerate their AI progress. Carolyn Geason-Beissel/MIT SMR Getty Images MIT SMR columnists Thomas H. Davenport and Randy Bean see five AI trends to take notice of in 2026: deflation of the AI bubble and subsequent hits to the economy; growth of the "factory" facilities for all-in AI adapters; greater focus on generative AI as an organizational resource rather than a private one; continued progression towards value from agentic AI, despite the buzz; and continuous questions around who need to manage data and AI.

This indicates that forecasting business adoption of AI is a bit easier than forecasting technology change in this, our 3rd year of making AI predictions. Neither people is a computer system or cognitive scientist, so we generally stay away from prognostication about AI technology or the particular ways it will rot our brains (though we do anticipate that to be an ongoing phenomenon!).

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

It's hard not to see the resemblances to today's scenario, consisting of the sky-high assessments of startups, the emphasis on user development (keep in mind "eyeballs"?) over profits, the media hype, the expensive facilities buildout, etcetera, etcetera. The AI market and the world at large would probably gain from a small, slow leakage in the bubble.

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

This column series takes a look at the biggest information and analytics difficulties dealing with modern business and dives deep into effective usage cases that can assist other companies accelerate their AI progress. Thomas H. Davenport (@tdav) is the President's Distinguished Teacher of Infotech and Management and professors director of the Metropoulos Institute for Innovation and Entrepreneurship at Babson College, and a fellow of the MIT Effort on the Digital Economy.

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

Quantum computing has actually long seemed like science fiction. However scientists are going into a "years, not decades" period where quantum machines will start dealing with problems classical computers can't, states Jason Zander, executive vice president of Microsoft Discovery and Quantum. That looming advancement, called quantum benefit, could assist resolve society's toughest obstacles, Zander says.

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AI discovers patterns in information. And quantum adds a brand-new layer that will drive far greater accuracy for modeling molecules and products, he states.

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It's the first quantum chip constructed using topological qubits, a design that inherently makes delicate qubits more steady and trusted. It's also the only quantum solution crafted to capture and appropriate errors. That architecture paves the way for devices with countless qubits on a single chip, providing the processing power needed for complicated clinical and commercial problems.

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

A year in tech can seem like a decade anywhere else. Think about it: a year ago, we were discussing how ChatGPT wasn't able to count the number of "r"s in "strawberry." Thinking models from Chinese frontier laboratories (like DeepSeek-R1) hadn't taken the world by storm, and neither had open-source thinking representatives.

IBM's Granite 3.0 had only just shown up. And the agent discussion was just beginning: MCP had actually simply gotten traction in the spring, with a significant endorsement from Sam Altman. In the world of facilities, chips and calculate resources were becoming limited, giving new territories a competitive advantage. Over the last few weeks, IBM Believe spoken to a dozen professionals in techresearchers, founders and leaders from IBM and beyondto get their insights on what to expect in the year ahead.