Is Your Enterprise Be Driven By Automation?

This column series takes a look at the biggest data and analytics obstacles facing modern-day business and dives deep into effective usage cases that can help other companies accelerate their AI development. Carolyn Geason-Beissel/MIT SMR Getty Images MIT SMR columnists Thomas H. Davenport and Randy Bean see 5 AI patterns to pay attention to in 2026: deflation of the AI bubble and subsequent hits to the economy; growth of the "factory" facilities for all-in AI adapters; higher focus on generative AI as an organizational resource instead of a specific one; continued progression towards worth from agentic AI, despite the buzz; and continuous concerns around who should handle information and AI.

This means that forecasting business adoption of AI is a bit simpler than predicting innovation change in this, our third year of making AI forecasts. Neither people is a computer or cognitive researcher, so we generally keep away from prognostication about AI innovation or the particular ways it will rot our brains (though we do anticipate that to be an ongoing phenomenon!).

We're likewise neither financial experts nor financial investment experts, but that won't stop us from making our first forecast. Here are the emerging 2026 AI patterns 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 below).

It's tough not to see the resemblances to today's situation, including the sky-high evaluations of startups, the focus on user growth (remember "eyeballs"?) over revenues, the media hype, the expensive facilities buildout, etcetera, etcetera. The AI market and the world at large would most likely benefit from a small, slow leakage in the bubble.

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It will not take much for it to happen: a bad quarter for an important vendor, a Chinese AI model that's much cheaper 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 greatest data and analytics difficulties facing contemporary business and dives deep into effective use cases that can help other organizations accelerate their AI development. Thomas H. Davenport (@tdav) is the President's Distinguished Professor of Info Innovation 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 a consultant to Fortune 1000 companies on data and AI management for over four decades. He is the author of Fail Quick, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021).

Quantum computing has actually long felt like science fiction. However scientists are entering a "years, not years" period where quantum devices will begin taking on problems classical computer systems can't, says Jason Zander, executive vice president of Microsoft Discovery and Quantum. That looming advancement, called quantum advantage, could help solve society's hardest challenges, Zander says.

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AI discovers patterns in data. Supercomputers run enormous simulations. And quantum adds a brand-new layer that will drive far higher precision for modeling molecules and products, he states. This progress coincides with advances in rational qubits, which are physical quantum bits grouped together so they can identify and correct errors and compute a crucial step towards reliability.

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It's the very first quantum chip built using topological qubits, a style that inherently makes fragile qubits more steady and dependable. It's also the only quantum option crafted to catch and proper mistakes. That architecture leads the way for devices with millions of qubits on a single chip, providing the processing power required for complicated scientific and industrial problems.

"The future of AI and science won't simply be much faster, it will be essentially redefined." Lead image created 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. Consider it: a year ago, we were going over how ChatGPT wasn't able to count the number of "r"s in "strawberry." Thinking models from Chinese frontier laboratories (like DeepSeek-R1) had not taken the world by storm, and neither had open-source thinking representatives.

, providing new areas a competitive benefit. Over the last couple of weeks, IBM Believe spoke with a dozen specialists in techresearchers, founders and leaders from IBM and beyondto get their insights on what to expect in the year ahead.