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, our third year of making AI predictions. Neither of us is a computer or cognitive scientist, so we 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. We’re also neither economists nor investment analysts, but that won’t stop us from making our first prediction.

Here are [two of] the emerging 2026 AI trends that leaders should understand and be prepared to act on.

1. The AI bubble will deflate, and the economy will suffer.

Last year, the elephant in the AI room was the rise of agentic AI (and it’s still clomping around; see below). This year, it’s the AI bubble that has monopolized discussion: Is there one? If so, when will it burst? Will the money rush out quickly or slowly? And what are the implications for the broader economy and the ongoing use of AI?

Both of us have been around for a while, and we remember the deflation of the dot-com bubble. It’s hard not to see the similarities to today’s situation, including the sky-high valuations of startups, the emphasis on user growth (remember “eyeballs”?) over profits, the media hype, the expensive infrastructure buildout, etcetera, etcetera.

Will this bubble burst? It seems inevitable to us 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.

We 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.

Both of us 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.” We think that AI is and will remain an important part of the global economy but that we’ve succumbed to short-term overestimation. The AI industry and the world at large would probably benefit from a small, slow leak in the bubble.

2. 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. We’re not talking about building big data centers with tens of thousands of GPUs; that’s generally being done by vendors. But 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.

Leading banks adopted this approach several years ago. They had a lot of data and a lot of potential applications in areas like credit decisioning and fraud prevention. For example, BBVA opened its AI factory in 2019, and JPMorgan Chase created its factory, called OmniAI, in 2020. At the time, the focus was only on analytical AI.

But now the factory movement involves non-banking companies and other forms of AI. We described AI factories in a consumer products company (Procter & Gamble) and a software company (Intuit). Both companies, and now the banks as well, are emphasizing all forms of AI: analytical, generative, and agentic. Intuit calls its factory GenOS — a generative AI operating system for the business.

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.