By Gary Rivlin
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It has been barely 1,200 days since OpenAI unleashed ChatGPT. Yet, if you believe the most extreme artificial-intelligence boosters, the technology should have transformed the business world already. (Or it will do so any day now.) It is just as easy to find critics who think AI is just the latest tech fad that is doomed to fizzle before it achieves anything. That, too, is going to happen any day now.
The truth is more complicated than either the hype or its critics allow. Step inside a large company today, and AI is everywhere and nowhere at once. Employees use it to summarize meetings, draft emails and generate first cuts of presentations. But those gains haven't yet translated into a clear, economywide productivity surge -- or fundamental changes to how people do their jobs.
So, how long will it take AI to reach its potential? Figuring it out means sorting through a lot of challenges facing the business world: institutional inertia, human resistance to change, limited and often just plain messy data, privacy and security concerns, and the imaginative leap required to redesign how organizations actually function.
It is an out-of-focus picture. But even though it isn't as sharp as we'd like, it can tell us a lot about where we stand and how far we have to go.
Making gains
For all the grumbling and negative news, AI is making strides in the business world. surveys of CIOs and CEOs consistently show that companies plan to spend more on AI this year and next. A research report Deloitte released in January, and a separate Wharton study, both show large companies moving beyond experimentation and integrating AI into essential operations. The Wharton study, released in the fall, also found that three-quarters of the 801 executives surveyed reported positive returns on their AI investments.
The gains are showing up across a range of industries. Retailers are leaning on AI for real-time pricing and product recommendations. Private-equity firms have built AI analysts that synthesize research and inform investment decisions. Manufacturers are deploying computer vision to catch defects on the production line.
The most dramatic progress has been in software development. AI has become so capable at writing code that many software engineers simply describe what they want in plain English and the AI does the rest.
Given all that, it is flat-out wrong to say that AI adoption is stagnating, says Ethan Mollick, a Wharton professor who studies how companies adopt AI. "Saying we're stuck in pilot mode is this outdated idea that's wrong," he says. "I'm talking to companies all the time getting real value out of AI."
Limited effects
But the AI revolution is up against many obstacles in the business world. For one thing, there is basic skepticism about all the hype: Boards and investors keep pushing for clearer evidence that AI investment is paying off. And, so far at least, AI hasn't shown that it is versatile enough to transform businesses and industries on a large scale.
Researchers have coined a term to describe AI's uneven capabilities: "jagged frontier." The models are great at some things and surprisingly bad at others, and it is rarely obvious which is which until after a company has already committed, says Benedict Evans, an independent analyst who tracks enterprise AI adoption.
For instance, AI excels at tasks with clear structures such as coding, legal-document review and financial analysis. But ask AI to navigate the more-contextual work that fills most of a workday, and the jaggedness shows. It gives wrong answers with great confidence, and can't draw on the human factors -- judgment calls, unwritten rules and hard-won instincts -- that never make it into training data.
That is a hard ceiling on what present-day AI can do. "Whether you're a CEO, a manager, a journalist, a professor or a construction worker, I see your skills as beyond what AI can perform," says Nobel laureate and MIT economist Daron Acemoglu, who says he believes current AI tools will have an impact on only a fraction of jobs.
What's more, to actually do something useful, AI needs a lot of "wrapping": the right data, the right permissions, the right guardrails and defined roles for the humans who oversee it. Because every company's systems and workflows are different, that surrounding architecture usually has to be built from scratch. And that is a lot harder than it looks.
The human factor
But as obstacles go, the technological issues may be much easier to overcome than the human ones. Simply put, a lot of people need to be convinced before the AI revolution can happen in earnest.
Executives face five-year planning cycles, depreciation schedules on systems they bought three years ago, and boards demanding returns. Risk aversion in that environment isn't irrational. Then there are the workers: People who believe they are training their own replacements aren't going to be enthusiastic partners in making it work.
"What is being sold is this idea of productivity and efficiency," says Kate Brennan, associate director of the AI Now Institute, an AI-policy research center, "and what that means for the people doing the actual work is rarely part of the conversation."
Management and employees can also be hesitant about really integrating AI into their operations, rather than just using it for drudgework. People's instinct is to use AI to automate parts of existing processes rather than rethink the processes themselves.
Consider an insurer handling a fender-bender claim. Typically, a company will use AI to speed up the paperwork while keeping the same layers of review and approval in place. But the real opportunity lies in redesigning the process entirely -- having AI assess the damage based on a customer's photos, then approving the claim and triggering payment nearly instantly. That kind of reimagining is difficult, and threatens established hierarchies and routines.
The big picture
Finally, it is important to remember that transformative technologies have always taken longer to bring about the kind of deep changes their champions promised. Electricity rewired civilization but took four decades to show up meaningfully in productivity data. The internet reshaped the foundations of business, work and global competition but needed 10 to 15 years to seep into the bones of the economy. The internet's early years looked, from the inside, a lot like AI does now: spectacular promise, uneven results and an industry with every incentive to tell you the revolution was already here.
"It takes time on human scales to actually transform organizations and unlock big changes," says James Landay, the co-director of Stanford Institute for Human-Centered Artificial Intelligence, who has spent years watching businesses struggle to absorb new technology. "My sense is more like five to 10 years -- not the next two or three."
AI will almost certainly prove as consequential as the internet, and probably will take about as long to reshape the economy. The boosters are directionally right about where this is all heading. The skeptics are probably right about how long it will take. Holding both thoughts at once is the most useful thing any executive, investor or policymaker can do right now.
Gary Rivlin is a writer in New York and author of "AI Valley." He can be reached at reports@wsj.com.
(END) Dow Jones Newswires
June 07, 2026 12:00 ET (16:00 GMT)
Copyright (c) 2026 Dow Jones & Company, Inc.
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