Adoption pressure is not a validation method. Nielsen Norman Group's PROVE framework gives teams a structured way to test one AI tool against one specific task and produce a defensible, provisional decision on whether to keep it.
The framework targets a real failure mode: organizations push teams to adopt purchased tools faster, spend less on AI, and absorb new releases without dedicated learning time. Under that pressure, evaluations either skip the comparison entirely or measure the wrong thing. The core question PROVE forces you to answer is whether the tool produces an outcome worth adopting compared to your existing workflow, not whether it produces any output at all.
The full article walks through PROVE applied to an actual workflow, not a hypothetical. That applied example is the reason to read it. Frameworks are easy to describe and hard to use. Seeing the evaluation criteria stress-tested against real work conditions is where the method earns its value.
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