To be sure, the MIT report is actually a bit more nuanced than the headline finding suggests: It argues that while individuals are successfully adopting gen AI tools that increase their productivity, such results aren’t measurable at a P&L level, and companies are struggling with enterprise-wide deployments. Moreover, its authors found that most spending on AI experiments goes to sales and marketing initiatives, despite the fact that back-end transformations tend to produce the biggest ROI.

Even so, headlines like these worry leaders. If 95% of the tens of billions invested in experimentation has failed to produce value, is the effort to experiment with AI a complete waste? On the other hand, how will companies learn how to use these tools without running experiments? How are leaders supposed to interpret these results?

As researchers who study AI and teach about AI transformation and technology, we believe that many leaders are making the same mistake they made a decade earlier with digital transformation: encouraging experimentation, which is good, but falling into the trap of letting experimentation run wild, which is counterproductive. For context, in the previous wave of digital transformation, when many leaders felt confused about digital transformation and the path forward, they embraced innovation and experimentation. Leaders embraced a “let 10,000 flowers bloom” approach, hoping that a few experiments produced unicorn-level returns.

The lack of focus proved to be a blunder, however. Without a clear connection to the real business opportunity—the way to create meaningful value for users—the result was a morass of unfocused, under-resourced teams that produced few scalable results. Facing such disappointing returns, many leaders naturally concluded that experimentation with digital was broken and shut down the experiments. In its place they either returned to business as usual or refocused on a few safer bets: perhaps replacing an aging IT system or a near-term payoff like a digital asset management system.

What went wrong? While experimentation is good, without a connection to the true business opportunity—e.g., transforming the core to serve existing and new customers—experiments inevitably fall short of hopes and expectations. It sounds obvious, but by framing AI as radical and disruptive we often lose sight of the connection to the most fundamental objective of business: to solve problems for customers. The way out of this trap is to 1) understand this AI moment in the larger arc of transformation, 2) focus on AI’s potential to help better serve customers, 3) experiment with a focused set of opportunities to prove them out (with an eye toward scaling), and then 4) scale them up. Here’s how.

Understand AI in the Larger Arc of Transformation

Although the world is talking about AI right now, pull back the frame to recall that AI is a recent conversation that is part of a larger shift. The true change we are all wrestling with is a fundamental shift from digital technology operating at the periphery of organizations (e.g., IT was about laptops, wifi, printing, and IT databases for registry of core activities) to digital at the very core of organizations (e.g., an organization built around digital workflows and customer journeys rather than its own production activities). Said differently, in many senses, every company is becoming a technology company. Rather than people performing tasks based on human judgement and intuition, we are moving to a world of data- and AI-driven decisions, overseen by humans but not necessarily with people as the core engine of the activity.

Consider, for example, how Ant Financial makes lending decisions or Amazon makes pricing decisions with humans only overseeing, not doing, the activity. This is a truly profound shift, and we’re only in the middle of it—in the end, it will take many years but it will lead to a fundamentally different kind of organization. Understanding this bigger picture helps remind leaders that the point is to transform the business to use technology to serve customers better, faster, easier, cheaper, etc. All forms of AI (including gen AI) are just a tool—one of many—that can help accomplish that. Just as the internet fundamentally changed how customers are served, but not why they’re served, AI adoption needs to be seen through this laser-like focus to succeed.