Strategies for Managing Global IT Infrastructure

Just a few companies are recognizing remarkable value from AI today, things like surging top-line growth and significant assessment premiums. Lots of others are also experiencing measurable ROI, however their outcomes are typically modestsome effectiveness gains here, some capability growth there, and basic however unmeasurable efficiency boosts. These outcomes can spend for themselves and then some.

It's still difficult to use AI to drive transformative value, and the innovation continues to develop at speed. We can now see what it looks like to use AI to build a leading-edge operating or organization design.

Companies now have sufficient evidence to develop benchmarks, step performance, and identify levers to speed up value production in both business and functions like finance and tax so they can end up being nimbler, faster-growing organizations. Why, then, has this kind of successthe kind that drives revenue development and opens new marketsbeen concentrated in so few? Frequently, organizations spread their efforts thin, placing small erratic bets.

Realizing the Business Value of Machine Learning

But genuine outcomes take precision in picking a few spots where AI can provide wholesale change in ways that matter for the business, then carrying out with steady discipline that begins with senior management. After success in your priority areas, the rest of the company can follow. We've seen that discipline settle.

This column series looks at the most significant data and analytics obstacles dealing with modern business and dives deep into effective use cases that can assist other companies accelerate their AI progress. Carolyn Geason-Beissel/MIT SMR Getty Images MIT SMR columnists Thomas H. Davenport and Randy Bean see five AI patterns to focus on in 2026: deflation of the AI bubble and subsequent hits to the economy; development of the "factory" infrastructure for all-in AI adapters; higher focus on generative AI as an organizational resource instead of a private one; continued development towards worth from agentic AI, in spite of the buzz; and ongoing questions around who need to manage information and AI.

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

We're likewise neither economists nor investment experts, but that will not stop us from making our very first forecast. Here are the emerging 2026 AI trends that leaders ought 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 below).

Ways to Improve Infrastructure Agility

It's hard not to see the resemblances to today's circumstance, including the sky-high valuations of startups, the emphasis on user development (keep in mind "eyeballs"?) over profits, the media hype, the costly infrastructure buildout, etcetera, etcetera. The AI industry and the world at big would probably benefit from a little, slow leakage in the bubble.

It won't take much for it to happen: a bad quarter for an essential supplier, a Chinese AI model that's much cheaper and simply as reliable as U.S. models (as we saw with the first DeepSeek "crash" in January 2025), or a few AI costs pullbacks by big business clients.

A progressive decrease would also provide all of us a breather, with more time for companies to take in the technologies they currently have, and for AI users to seek services that do not require more gigawatts than all the lights in Manhattan. Both people subscribe to the AI variation upon Amara's Law, which mentions, "We tend to overestimate the effect of a technology in the short run and underestimate the result in the long run." We think that AI is and will remain a fundamental part of the worldwide economy however that we have actually caught short-term overestimation.

We're not talking about developing big information centers with 10s of thousands of GPUs; that's normally being done by vendors. Business that utilize rather than offer AI are creating "AI factories": mixes of technology platforms, approaches, data, and formerly established algorithms that make it fast and simple to build AI systems.

Why Technology Innovation Drives Global Success

They had a great deal of data and a lot of prospective applications in areas like credit decisioning and scams prevention. 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. But now the factory movement involves non-banking business and other kinds of AI.

Both business, and now the banks also, are highlighting all kinds of AI: analytical, generative, and agentic. Intuit calls its factory GenOS a generative AI os for the service. Companies that do not have this sort of internal infrastructure force their information scientists and AI-focused businesspeople to each replicate the tough work of determining what tools to use, what data is available, and what techniques and algorithms to utilize.

If 2025 was the year of recognizing that generative AI has a value-realization issue, 2026 will be the year of throwing down the gauntlet (which, we need to admit, we forecasted with regard to controlled experiments last year and they didn't actually take place much). One specific approach to addressing the value problem is to move from executing GenAI as a primarily individual-based approach to an enterprise-level one.

Oftentimes, the primary tool set was Microsoft's Copilot, which does make it easier to create emails, written files, PowerPoints, and spreadsheets. However, those types of usages have normally led to incremental and primarily unmeasurable productivity gains. And what are employees finishing with the minutes or hours they save by utilizing GenAI to do such tasks? No one seems to understand.

Overcoming Challenges in Global Digital Scaling

The option is to consider generative AI primarily as an enterprise resource for more tactical use cases. Sure, those are typically more hard to develop and release, however when they prosper, they can use substantial value. Think, for instance, of using GenAI to support supply chain management, R&D, and the sales function rather than for speeding up creating a blog post.

Rather of pursuing and vetting 900 individual-level usage cases, the company has actually selected a handful of strategic jobs to highlight. There is still a need for workers 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 tasks.

In 2015, like virtually everybody else, we anticipated that agentic AI would be on the increase. We acknowledged that the innovation was being hyped and had some obstacles, we underestimated the degree of both. Agents turned out to be the most-hyped trend because, well, generative AI. GenAI now resides in the Gartner trough of disillusionment, which we predict agents will fall under in 2026.