The Middle East Tech Startup News

This column series looks at the greatest information and analytics difficulties facing contemporary companies and dives deep into successful usage cases that can help other organizations accelerate their AI progress. 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" facilities for all-in AI adapters; greater focus on generative AI as an organizational resource rather than a specific one; continued progression towards value from agentic AI, despite the hype; and continuous concerns around who should handle data and AI.

This indicates that forecasting business adoption of AI is a bit easier than forecasting innovation modification in this, our 3rd year of making AI forecasts. Neither people is a computer system or cognitive scientist, so we usually keep away from prognostication about AI innovation or the specific methods it will rot our brains (though we do expect that to be a continuous phenomenon!).

We're also neither economists nor financial investment analysts, but that will not stop us from making our first prediction. Here are the emerging 2026 AI trends that leaders must comprehend and be prepared to act upon. 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 difficult not to see the resemblances to today's situation, consisting of the sky-high valuations of startups, the emphasis on user growth (keep in mind "eyeballs"?) over revenues, the media hype, the expensive facilities buildout, etcetera, etcetera. The AI market and the world at large would probably take advantage of a little, slow leakage in the bubble.

ANSR July GCC PRs 50DR+

GCC Digital Startup Updates

It won't take much for it to happen: a bad quarter for an important supplier, a Chinese AI design that's much cheaper and just as reliable as U.S. designs (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 greatest data and analytics obstacles facing contemporary business and dives deep into effective use cases that can assist other companies accelerate their AI development. Thomas H. Davenport (@tdav) is the President's Distinguished Professor of Details Innovation 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 adviser to Fortune 1000 companies on data and AI management for over four years. He is the author of Fail Fast, Find Out Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021).

Quantum computing has actually long felt like science fiction. Researchers are getting in a "years, not years" period where quantum machines will begin dealing with issues classical computers can't, states Jason Zander, executive vice president of Microsoft Discovery and Quantum. That looming development, called quantum advantage, might help solve society's hardest challenges, Zander says.

ANSR July GCC PRs 50DR+

AI discovers 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 rational qubits, which are physical quantum bits organized together so they can find and correct mistakes and calculate a crucial action towards reliability.

Exploring the Future of GCC Innovation

It's the first quantum chip constructed utilizing topological qubits, a style that naturally makes fragile qubits more stable and reliable. It's likewise the only quantum solution engineered to catch and right errors. That architecture leads the way for machines with countless qubits on a single chip, providing the processing power needed for complex clinical and industrial problems.

"The future of AI and science won't simply be much faster, it will be essentially redefined." Lead image produced 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.

IBM's Granite 3.0 had only simply shown up. And the representative discussion was only beginning: MCP had simply gotten traction in the spring, with a notable endorsement from Sam Altman. Meanwhile, worldwide of facilities, chips and calculate resources were becoming scarce, giving new territories a competitive benefit. Over the last few weeks, IBM Think talked with a dozen professionals in techresearchers, founders and leaders from IBM and beyondto get their insights on what to expect in the year ahead.