Most AI Products Fail Before They’re Built
Most AI projects fail to tie to revenue or outcomes. The problem isn't building—it's choosing what to build.
Why it matters
As AI tooling commoditizes, the bottleneck shifts from engineering to strategy: teams measuring success by releases rather than business impact are guaranteed to fail. Practitioners need to rethink how they justify and measure AI investments.
The key facts
8 to knowMost organizations deploy predictive, generative, and agentic AI but struggle to connect investments to revenue or customer outcomes
Quality of problem selection is now the primary determinant of AI product value
Teams relying on release/feature/utilization metrics as success measures are at risk
AI commoditization means engineering velocity is no longer a competitive differentiator
Most organizations deploy predictive, generative, and agentic AI but cannot connect investments to revenue or profitability
Quality of problem being solved is now the primary determinant of AI product value
Teams measuring success by releases, features, or utilization are at risk of failure
Source: Forrester analyst perspective (not original research with quantified failure rates)
Go to the source
Forrester Blogforrester.com
Publisher excerpt: While most organisations are deploying predictive, generative, and agentic AI, few can directly connect those investments to revenue, customer outcomes, or profitability. As AI becomes cheaper and easier to build, the quality of the problem being solved becomes the primary determinant of value.…