The Middle East Tech Startup Updates

This column series looks at the biggest information and analytics challenges facing modern-day business and dives deep into successful usage cases that can assist other organizations accelerate their AI development. Carolyn Geason-Beissel/MIT SMR Getty Images MIT SMR writers Thomas H. Davenport and Randy Bean see 5 AI trends to pay attention to in 2026: deflation of the AI bubble and subsequent hits to the economy; growth of the "factory" infrastructure for all-in AI adapters; greater focus on generative AI as an organizational resource rather than an individual one; continued progression toward worth from agentic AI, despite the buzz; and ongoing concerns around who must handle data and AI.

This suggests that forecasting enterprise adoption of AI is a bit simpler than forecasting technology modification in this, our third year of making AI forecasts. Neither of us is a computer or cognitive researcher, so we usually keep away from prognostication about AI technology or the particular methods it will rot our brains (though we do expect that to be a continuous phenomenon!).

We're also neither economic experts nor investment analysts, however that will not stop us from making our very first forecast. Here are the emerging 2026 AI patterns that leaders need to understand and be prepared to act on. In 2015, the elephant in the AI room was the increase of agentic AI (and it's still clomping around; see listed below).

It's hard not to see the similarities to today's circumstance, including the sky-high evaluations of startups, the focus on user development (remember "eyeballs"?) over earnings, the media hype, the pricey facilities buildout, etcetera, etcetera. The AI industry and the world at big would most likely benefit from a small, slow leakage in the bubble.

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Exploring the Landscape of GCC Innovation

It will not take much for it to take place: a bad quarter for an essential supplier, a Chinese AI model that's more affordable 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 consumers.

This column series takes a look at the most significant data and analytics challenges dealing with contemporary companies and dives deep into successful usage cases that can assist other organizations accelerate their AI development. Thomas H. Davenport (@tdav) is the President's Distinguished Professor of Infotech 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 been an advisor to Fortune 1000 companies on data and AI leadership for over 4 decades. He is the author of Fail Fast, Discover Faster: Lessons in Data-Driven Management in an Age of Interruption, Big Data, and AI (Wiley, 2021).

Quantum computing has long felt like sci-fi. Scientists are going into a "years, not years" period where quantum machines 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 benefit, might help resolve society's most difficult challenges, Zander says.

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AI finds patterns in information. Supercomputers run enormous simulations. And quantum adds a new layer that will drive far greater accuracy for modeling particles and materials, he says. This progress corresponds with advances in sensible qubits, which are physical quantum bits organized together so they can identify and appropriate errors and calculate a critical action towards dependability.

Building Applied AI Strategies for Modern Businesses

It's the first quantum chip built using topological qubits, a design that naturally makes fragile qubits more stable and trustworthy. It's likewise the only quantum option engineered to capture and correct mistakes. That architecture paves the way for machines with countless qubits on a single chip, providing the processing power needed for intricate clinical and industrial problems.

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

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

IBM's Granite 3.0 had actually only just gotten here. And the agent discussion was just beginning: MCP had actually just acquired traction in the spring, with a noteworthy recommendation from Sam Altman. In the world of infrastructure, chips and calculate resources were becoming scarce, giving brand-new areas a competitive advantage. Over the last couple of weeks, IBM Believe talked to a lots experts in techresearchers, founders and leaders from IBM and beyondto get their insights on what to anticipate in the year ahead.