Nielsen Norman Group: A structured way to decide which AI tools are worth keeping

What the article is about

Nielsen Norman Group describes the PROVE framework, a five-part evaluation method for deciding whether a specific AI tool genuinely improves a specific recurring task. The article addresses a situation many designers find themselves in: informal or formal pressure to adopt an AI tool, without any clear criteria for what “better” would actually mean for their work.

Context

The framework is deliberately narrow in scope. It evaluates one tool against one task for one person, and produces a recommendation that can be explained to a manager, teammate, or client in plain terms. It is not a procurement method for large organizations or a way to select tools across teams — the unit of analysis is a single person and a single recurring piece of work.

Key takeaway or method

PROVE stands for five evaluation dimensions.

Problem Alignment requires defining the recurring task before exploring any tool. Starting with tool exploration rather than a defined task is described as a common failure mode — it skews the assessment toward what the tool can do rather than what the work actually needs.

Risk covers whether the tool is approved for the type of data being processed. This includes reviewing privacy policies, identifying shadow AI practices, and confirming access permissions before putting sensitive material into a tool.

Output Quality involves comparing the tool’s output against real work using an actual example. Quality standards vary significantly by task stakes and audience, so the comparison should use a representative task rather than a simple or forgiving one.

Velocity measures total time invested — setup, prompting, reviewing, editing, and reformatting — rather than only the time between submitting a prompt and receiving a result. A tool that generates output in seconds can still cost more time overall if the output requires substantial editing.

Experience asks whether friction in the workflow is one-time setup cost or a recurring obstacle. Some handoffs that feel awkward at first become routine; others remain awkward indefinitely. The framework asks for an honest assessment of which applies.

Scoring uses a five-point scale where 3 means “about the same as my current approach.” A score above 3 suggests genuine improvement; below 3, the tool adds cost without proportionate return. The author demonstrates the framework with a real evaluation of Google Gemini Notebooks for drafting weekly research digests: Output Quality 4/5, Velocity 4/5, Experience 3/5.

The article’s central argument is that “pressure to use AI is real, but pressure is not evidence.” Adoption decisions made after a single promising demo or one poor result are both premature. The framework is designed to produce a provisional conclusion that can be revisited as both the tool and the task evolve. After scoring, the recommendation is explained by answering four questions: What did you evaluate? What did you find? What are you doing next? What caveat matters most?

Who it is useful for

Anyone evaluating AI tools for their own work — designers, researchers, content creators, strategists — particularly people being encouraged to adopt AI without a clear picture of what improvement would look like. The framework applies across roles and does not require technical background.