Establishing a Tech Leader for the Middle East

This column series takes a look at the greatest information and analytics challenges dealing with contemporary business and dives deep into effective usage 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 trends to take note of 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 rather than a specific one; continued progression towards worth from agentic AI, regardless of the hype; and ongoing questions around who need to handle information and AI.

This means that forecasting enterprise adoption of AI is a bit much easier than anticipating innovation modification in this, our 3rd year of making AI forecasts. Neither people is a computer system or cognitive researcher, so we usually stay away from prognostication about AI technology or the particular ways it will rot our brains (though we do anticipate that to be a continuous phenomenon!).

We're likewise neither economic experts nor financial investment experts, however that will not stop us from making our first prediction. Here are the emerging 2026 AI trends 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 difficult not to see the similarities to today's situation, including the sky-high valuations of start-ups, the emphasis on user development (remember "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 little, slow leak in the bubble.

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Middle East Digital Startup News

It will not take much for it to take place: a bad quarter for an essential vendor, a Chinese AI model that's more affordable and simply as reliable as U.S. models (as we saw with the very first DeepSeek "crash" in January 2025), or a few AI spending pullbacks by large corporate clients.

This column series takes a look at the biggest information and analytics challenges facing contemporary 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 Professor of Infotech and Management and faculty 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 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 Leadership 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 years" period where quantum makers will begin tackling issues classical computers can't, states Jason Zander, executive vice president of Microsoft Discovery and Quantum. That looming development, called quantum advantage, might assist fix society's toughest difficulties, Zander says.

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

How Applied AI Accelerates High-Impact Innovation

It's the first quantum chip constructed utilizing topological qubits, a design that naturally makes fragile qubits more steady and dependable. It's also the only quantum option crafted to capture and appropriate errors. That architecture leads the way for machines with countless qubits on a single chip, supplying the processing power required for complex clinical and commercial problems.

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

A year in tech can seem like a decade anywhere else. Think of it: a year earlier, we were discussing how ChatGPT wasn't able to count the variety 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 agents.

, providing brand-new territories a competitive advantage. Over the last couple of weeks, IBM Think spoke with a dozen experts in techresearchers, founders and leaders from IBM and beyondto get their insights on what to anticipate in the year ahead.