The man who made India digital isn’t done yet

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Organizations tend to change much more slowly than AI technology does these days. This means that forecasting enterprise adoption of AI is a bit easier than predicting technology change in this, the authors third year of making AI predictions. Neither of the authors is a computer or cognitive scientist, so they generally stay 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!).

However, AI seems to have moved beyond being just a technology to becoming the primary force driving economic growth and the stock market. The authors also neither economists nor investment analysts, but that won’t stop them from making their first prediction.  Here are the emerging 2026 AI trends that leaders should understand and be prepared to act on.

The AI bubble will deflate, and the economy will suffer.  Will this bubble burst? It seems inevitable to the authors that it will, and probably soon. It won’t take much for it to happen: a bad quarter for an important vendor, a Chinese AI model that’s much cheaper and just as effective as U.S. models (as we saw with the first DeepSeek “crash” in January 2025), or a few AI spending pullbacks by large corporate customers.  The authors hope the deflation will be gradual, which might mean that the overall stock market would have time to adjust and for investors to move some of the highly inflated AI vendors out of their portfolios. A gradual decline would also give all of us a breather, with more time for companies to absorb the technologies they already have, and for AI users to seek solutions that don’t require more gigawatts than all the lights in Manhattan.  The authors subscribe to the AI variation upon Amara’s Law, which states, “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”

More all-in adopters will create ‘AI factories’ and infrastructure.  Companies that are all in on AI as an ongoing competitive advantage are putting infrastructure in place to speed up the pace of AI models and use-case development. Companies that use rather than sell AI are creating “AI factories”: combinations of technology platforms, methods, data, and previously developed algorithms that make it fast and easy to build AI systems.  Companies that don’t have this kind of internal infrastructure force their data scientists and AI-focused businesspeople to each replicate the hard work of figuring out what tools to use, what data is available, and what methods and algorithms to employ. Not being able to build on an established foundation makes it both more expensive and more time-consuming to build AI at scale.

GenAI will become more of an organizational resource.  Think about generative AI primarily as an enterprise resource for more strategic use cases. Sure, those are typically more difficult to build and deploy, but when they succeed, they can offer considerable value.  There is still a need for employees to have access to GenAI tools, of course; some companies are beginning to view this as an employee satisfaction and retention issue. And some bottom-up ideas are worth turning into enterprise projects.

Agentic AI will still be overhyped but will likely be valuable within five years.  Last year, like virtually everyone else, we predicted that agentic AI would be on the rise. Although we acknowledged that the technology was being hyped and had some challenges, we underestimated the degree of both. Agents turned out to be the most-hyped trend since, well, generative AI. GenAI now resides in the Gartner trough of disillusionment, which we predict agents will fall into in 2026.

Debate will continue over who should manage AI.  A challenging structural issue in emerging picture is who should be managing AI and to whom they should report in the organization. Not surprisingly, a growing percentage of companies have named chief AI officers (or an equivalent title); this year, it’s up to 39%. The problem is that there is little consensus about to whom that job reports. Only 30% report to a chief data officer (where we believe the role should report); other organizations have AI reporting to business leadership (27%), technology leadership (34%), or transformation leadership (9%).  The authors think it’s likely that the diverse reporting relationships are contributing to the widespread problem of AI (particularly generative AI) not delivering sufficient value.

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