Building AI Strategies for Global Businesses

This column series looks at the greatest data and analytics challenges facing modern companies and dives deep into successful use cases that can assist other companies 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 focus on in 2026: deflation of the AI bubble and subsequent hits to the economy; development of the "factory" infrastructure for all-in AI adapters; higher focus on generative AI as an organizational resource instead of a specific one; continued progression toward value from agentic AI, in spite of the hype; and continuous questions around who ought to handle information and AI.

This indicates that forecasting enterprise adoption of AI is a bit simpler than anticipating innovation change in this, our third year of making AI predictions. Neither people is a computer or cognitive researcher, so we normally keep away from prognostication about AI technology or the specific methods it will rot our brains (though we do expect that to be a continuous phenomenon!).

We're also neither economic experts nor financial investment experts, however that won't stop us from making our first prediction. Here are the emerging 2026 AI trends that leaders should understand and be prepared to act on. In 2015, the elephant in the AI space was the increase 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 assessments of start-ups, the emphasis on user development (keep in mind "eyeballs"?) over profits, the media buzz, the expensive facilities buildout, etcetera, etcetera. The AI market and the world at large would probably take advantage of a little, sluggish leak in the bubble.

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Leveraging Cloud Infrastructure Within the GCC

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

This column series takes a look at the biggest information and analytics difficulties facing contemporary business and dives deep into effective usage cases that can help other companies accelerate their AI progress. Thomas H. Davenport (@tdav) is the President's Distinguished Teacher 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 actually been an advisor to Fortune 1000 companies on data and AI leadership for over 4 decades. He is the author of Fail Quick, Find Out Faster: Lessons in Data-Driven Leadership in an Age of Disturbance, Big Data, and AI (Wiley, 2021).

Quantum computing has long felt like science fiction. But scientists are entering a "years, not years" age where quantum machines will begin tackling issues classical computers can't, states Jason Zander, executive vice president of Microsoft Discovery and Quantum. That looming advancement, called quantum advantage, might assist fix society's toughest difficulties, Zander says.

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AI discovers patterns in information. Supercomputers run massive simulations. And quantum adds a new layer that will drive far greater precision for modeling particles and materials, he states. This progress accompanies advances in logical qubits, which are physical quantum bits organized together so they can identify and proper errors and calculate a vital action toward dependability.

AI Versus Traditional Systems: a 2026 Review

It's the first quantum chip developed using topological qubits, a style that inherently makes fragile qubits more stable and trusted. It's likewise the only quantum service crafted to capture and correct mistakes. That architecture leads the way for makers with countless qubits on a single chip, providing 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 decade anywhere else.

IBM's Granite 3.0 had only just shown up. And the agent discussion was only beginning: MCP had actually just acquired traction in the spring, with a notable recommendation from Sam Altman. On the other hand, in the world of infrastructure, chips and calculate resources were ending up being scarce, giving new areas a competitive benefit. Over the last few weeks, IBM Think talked to a dozen experts in techresearchers, creators and leaders from IBM and beyondto get their insights on what to anticipate in the year ahead.