Becoming a Tech Hub in the GCC

This column series takes a look at the greatest data and analytics challenges dealing with modern-day business and dives deep into effective use 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 take note 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 concentrate on generative AI as an organizational resource rather than an individual one; continued development toward worth from agentic AI, in spite of the buzz; and continuous concerns around who need to handle information and AI.

This suggests that forecasting enterprise adoption of AI is a bit easier than anticipating technology change in this, our 3rd year of making AI forecasts. Neither of us is a computer system 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 expect that to be an ongoing phenomenon!).

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

It's difficult not to see the similarities to today's circumstance, consisting of the sky-high assessments of start-ups, the emphasis on user development (keep in mind "eyeballs"?) over profits, the media hype, the pricey infrastructure buildout, etcetera, etcetera. The AI industry and the world at big would probably benefit from a small, sluggish leakage in the bubble.

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Implementing AI Strategies for Modern Enterprises

It will not take much for it to happen: a bad quarter for a crucial vendor, a Chinese AI design that's more affordable and simply as reliable as U.S. designs (as we saw with the first DeepSeek "crash" in January 2025), or a couple of AI spending pullbacks by large business customers.

This column series looks at the most significant information and analytics difficulties dealing with modern companies and dives deep into successful usage cases that can help other companies accelerate their AI development. 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 Initiative on the Digital Economy.

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

Quantum computing has long felt like sci-fi. But researchers are getting in a "years, not decades" age where quantum makers will start dealing with problems classical computer systems can't, states Jason Zander, executive vice president of Microsoft Discovery and Quantum. That looming development, called quantum advantage, could help solve society's hardest challenges, Zander states.

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AI finds patterns in data. And quantum adds a new layer that will drive far greater accuracy for modeling molecules and materials, he says.

Building AI Strategies for Modern Businesses

It's the first quantum chip developed utilizing topological qubits, a design that inherently makes vulnerable qubits more steady and reputable. It's likewise the only quantum option crafted to capture and proper mistakes. That architecture paves the method for makers with countless qubits on a single chip, providing the processing power needed for complicated scientific and industrial issues.

"The future of AI and science won't simply be quicker, it will be essentially redefined." Lead image developed by Kathy Oneha/ We. Communications. Illustrations produced with Produce in Microsoft 365 Copilot. Story published on Dec. 8, 2025.

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

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