Strategies for Managing Global IT Infrastructure

Just a couple of companies are realizing remarkable worth from AI today, things like surging top-line growth and significant appraisal premiums. Numerous others are also experiencing measurable ROI, however their results are typically modestsome performance gains here, some capability growth there, and basic however unmeasurable efficiency boosts. These results can pay for themselves and after that some.

It's still tough to use AI to drive transformative worth, and the innovation continues to progress at speed. We can now see what it looks like to utilize AI to develop a leading-edge operating or company model.

Companies now have sufficient proof to construct criteria, step efficiency, and determine levers to speed up worth production in both the service and functions like finance and tax so they can become nimbler, faster-growing organizations. Why, then, has this sort of successthe kind that drives revenue growth and opens new marketsbeen focused in so few? Too typically, organizations spread their efforts thin, putting small erratic bets.

Ways to Scale Advanced AI for Business

But genuine outcomes take precision in selecting a few spots where AI can deliver wholesale improvement in manner ins which matter for business, then executing with stable discipline that starts with senior management. After success in your priority locations, the rest of the business can follow. We've seen that discipline settle.

This column series looks at the biggest data and analytics challenges dealing with contemporary business 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 writers Thomas H. Davenport and Randy Bean see five AI patterns 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; higher concentrate on generative AI as an organizational resource rather than an individual one; continued progression toward worth from agentic AI, regardless of the buzz; and ongoing questions around who ought to handle data and AI.

This indicates that forecasting business adoption of AI is a bit much easier than predicting technology change in this, our third year of making AI forecasts. Neither people is a computer system or cognitive researcher, so we usually stay away from prognostication about AI innovation 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 financial investment experts, however that won't stop us from making our very first prediction. Here are the emerging 2026 AI trends that leaders must comprehend and be prepared to act on. Last year, the elephant in the AI space was the increase of agentic AI (and it's still clomping around; see below).

Navigating the Next Era of Cloud Computing

It's difficult not to see the resemblances to today's situation, including the sky-high valuations of startups, the focus on user development (remember "eyeballs"?) over earnings, the media buzz, the costly facilities buildout, etcetera, etcetera. The AI industry and the world at big would probably gain from a small, sluggish leak in the bubble.

It will not take much for it to happen: a bad quarter for an important supplier, 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 couple of AI costs pullbacks by big corporate customers.

A gradual decline would also give all of us a breather, with more time for business to absorb the technologies they already have, and for AI users to look for solutions that do not require more gigawatts than all the lights in Manhattan. We believe that AI is and will stay an essential part of the worldwide economy but that we've surrendered to short-term overestimation.

We're not talking about developing huge data centers with 10s of thousands of GPUs; that's normally being done by vendors. Business that use rather than sell AI are creating "AI factories": combinations of innovation platforms, approaches, data, and formerly established algorithms that make it fast and simple to develop AI systems.

Establishing Strategic Innovation Centers Globally

They had a lot of information and a great deal of possible applications in locations like credit decisioning and fraud avoidance. BBVA opened its AI factory in 2019, and JPMorgan Chase created its factory, called OmniAI, in 2020. At the time, the focus was just on analytical AI. Now the factory movement includes non-banking companies and other types of AI.

Both business, and now the banks too, are stressing all forms of AI: analytical, generative, and agentic. Intuit calls its factory GenOS a generative AI os for business. Business that don't have this kind of internal infrastructure require their data researchers and AI-focused businesspeople to each replicate the tough work of finding out what tools to use, what information is readily available, and what methods and algorithms to use.

If 2025 was the year of realizing that generative AI has a value-realization problem, 2026 will be the year of finding a solution for it (which, we should admit, we forecasted with regard to regulated experiments last year and they didn't actually happen much). One particular method to addressing the worth issue is to shift from implementing GenAI as a mainly individual-based method to an enterprise-level one.

In a lot of cases, the primary tool set was Microsoft's Copilot, which does make it much easier to generate e-mails, composed files, PowerPoints, and spreadsheets. Those types of uses have generally resulted in incremental and primarily unmeasurable performance gains. And what are employees finishing with the minutes or hours they conserve by using GenAI to do such jobs? Nobody appears to know.

Managing the Next Era of Cloud Computing

The alternative is to consider generative AI mainly as a business resource for more strategic usage cases. Sure, those are generally more tough to develop and deploy, but when they prosper, they can offer considerable worth. Believe, for example, of utilizing GenAI to support supply chain management, R&D, and the sales function instead of for speeding up creating a blog post.

Instead of pursuing and vetting 900 individual-level usage cases, the business has actually chosen a handful of strategic projects to highlight. There is still a need for employees to have access to GenAI tools, naturally; some companies are starting to see this as a worker fulfillment and retention concern. And some bottom-up concepts deserve becoming business jobs.

Last year, like practically everybody else, we predicted that agentic AI would be on the increase. Agents turned out to be the most-hyped pattern because, well, generative AI.