AI Versus Traditional Methods: the 2026 Guide

This column series looks at the greatest information and analytics difficulties facing modern business and dives deep into successful use cases that can help other organizations accelerate their AI progress. Carolyn Geason-Beissel/MIT SMR Getty Images MIT SMR columnists Thomas H. Davenport and Randy Bean see 5 AI trends to focus on in 2026: deflation of the AI bubble and subsequent hits to the economy; growth of the "factory" infrastructure for all-in AI adapters; higher concentrate on generative AI as an organizational resource instead of an individual one; continued development toward value from agentic AI, despite the buzz; and ongoing concerns around who should handle information and AI.

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

We're likewise neither financial experts nor financial investment analysts, but that will not stop us from making our very first prediction. 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 space was the rise of agentic AI (and it's still clomping around; see listed below).

It's tough not to see the similarities to today's situation, including the sky-high assessments of startups, the emphasis on user growth (remember "eyeballs"?) over earnings, the media buzz, the pricey facilities buildout, etcetera, etcetera. The AI market and the world at big would most likely take advantage of a little, slow leakage in the bubble.

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It won't take much for it to occur: a bad quarter for an essential vendor, a Chinese AI design that's much cheaper and just as effective as U.S. models (as we saw with the first DeepSeek "crash" in January 2025), or a few AI costs pullbacks by big corporate clients.

This column series takes a look at the biggest information and analytics challenges dealing with contemporary business and dives deep into successful use cases that can assist other organizations accelerate their AI development. Thomas H. Davenport (@tdav) is the President's Distinguished Teacher of Details Technology 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 decades. He is the author of Fail Fast, Learn Faster: Lessons in Data-Driven Management in an Age of Disruption, Big Data, and AI (Wiley, 2021).

Quantum computing has actually long seemed like sci-fi. But scientists are entering a "years, not years" era where quantum makers will start tackling issues classical computers can't, says Jason Zander, executive vice president of Microsoft Discovery and Quantum. That looming development, called quantum advantage, might help solve society's hardest obstacles, Zander says.

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AI discovers patterns in information. Supercomputers run massive simulations. And quantum adds a brand-new layer that will drive far higher accuracy for modeling particles and materials, he states. This development accompanies advances in sensible qubits, which are physical quantum bits organized together so they can identify and proper mistakes and compute a crucial action toward reliability.

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It's the first quantum chip developed utilizing topological qubits, a design that inherently makes fragile qubits more stable and reliable. It's also the only quantum service engineered to catch and appropriate errors. That architecture paves the method for makers with countless qubits on a single chip, offering the processing power required for intricate scientific and commercial issues.

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

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

, giving new areas a competitive benefit. Over the last few weeks, IBM Think 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.