Melissa Perri Warns Against ‘Build Trap’ in AI Adoption Metrics

In a recent LinkedIn post, product strategy expert Melissa Perri warns that a widespread focus on AI tool adoption rates risks trapping businesses in the same output-focused pitfalls that have plagued product development for years. Perri argues that measuring success by how many people use a new AI tool, rather than the actual business value it delivers, is a fundamental misunderstanding of how to leverage new technology effectively.

Perri draws a direct parallel between current AI adoption metrics and the long-criticized concept of the “build trap” in product management. She defines the build trap as a situation where teams prioritize shipping more features over creating tangible value, often because success is measured by output rather than outcome.

“The build trap is what happens when you measure success by output instead of outcome. Right now, a whole industry is walking back into it through a new door labeled AI.”

The Pitfalls of Adoption Metrics

Perri highlights how many organizations are currently measuring their AI investment. She points to data from her “State of AI in Product 2026” survey, which indicates that larger organizations are particularly prone to using adoption rates as their primary AI metric. According to the survey, 47% of the largest organizations reported adoption rates as their headline AI metric, more than double the rate seen in smaller companies.

However, Perri notes a critical correlation: these same organizations were also among the least likely to report that AI had actually improved how they work. This, she contends, is not a coincidence.

“Adoption is an output,” Perri writes. “It tells you people are using the tool. It tells you nothing about whether the work got better.”

Focusing on Activity Over Value

The consequence of measuring activity, Perri explains, is that businesses will indeed get more activity. Teams will use AI more frequently, and adoption charts will climb, but the underlying operational model and the actual business impact will remain unchanged. This focus on easily trackable, but ultimately superficial, metrics ensures that the intended transformative benefits of AI are not realized.

“Measure activity, and activity is what you will get. People will use AI more, the chart will climb, and your operating model will sit exactly where it was.”

Measuring True AI Value

To overcome this, Perri urges businesses to shift their focus to metrics that are harder to manipulate and more indicative of genuine value creation. She suggests measuring outcomes that truly reflect an improvement in business performance.

“If you want AI to change your business, measure the things that are hard to fake,” Perri advises. She lists several examples of meaningful metrics:

• Cycle time
• The quality of your decisions
• How fast customer insight reaches a roadmap
• How much review capacity you freed up
• Whether your product outcomes actually moved

Perri emphasizes that these crucial indicators of success do not appear on a typical adoption dashboard, yet they represent the core reasons why organizations invest in AI tools in the first place.

“None of those show up on an adoption dashboard. All of them are the reason you bought the tools in the first place.”

Perri concludes by posing a critical question to her audience: “What is your team’s headline AI metric right now, and is it measuring activity or value?” This prompts a necessary re-evaluation of how businesses are assessing their AI initiatives, encouraging a move towards outcome-oriented measurement that aligns with strategic goals.