Executive dashboard
The small set of measures leadership uses to judge AI progress. It should show outcomes and relevance, not activity such as licences, prompts or pilot counts.
An executive dashboard is the short list of numbers that leadership looks at to decide whether an AI effort is working. Its purpose is to answer business questions, so it should contain the measures that can answer them.
Many popular AI measures are activity counts: licences issued, prompts run, pilots launched, hours saved, benchmark scores. Each can rise while the business stays the same. Those numbers still belong in operational monitoring, engineering and evaluation, where they answer different questions. They do not belong in the executive headline.
A useful executive dashboard separates activity, efficiency and relevance, and asks for a measure of outcome for each. See cost per accepted outcome and AI business case.
Related terms
Cost per accepted outcome
The total cost of getting one AI-assisted result that is actually accepted and used, counting model spend, retries, human review, exceptions and recovery — a truer measure than cost per call.
Benchmark
A standardised test used to compare AI models. Useful as evidence about someone else's system, but only trustworthy for you when it describes the exact serving route, harness and conditions you will run.
AI business case
The evidence that an AI use case changes a business outcome, net of the work needed to keep it dependable. A demo or a benchmark score is not a business case.
Used in these essays
A pilot with no kill criteria is not an experiment
A pilot is not an experiment if every result can be used to justify continuing. The missing design element is the authority to stop.
Five AI metrics I would remove from an executive dashboard
Licenses, prompts, pilots, hours saved and benchmark scores can all rise while the business stands still. The executive dashboard needs a different contract.
Your AI program can be efficient and still be strategically irrelevant
AI can make low-value work dramatically cheaper. That is still low-value work. The portfolio question is not only what became faster, but whether the work deserved investment in the first place.