How do you prove an enterprise AI pilot is worth funding?

Measure an accepted answer, not a fast response

An employee receiving a fluent answer is not the same as an employee completing a task. For a company-knowledge pilot, define accepted as an answer that uses the current permitted source, gives the employee the correct next action and survives reviewer inspection. Keep that definition unchanged during the comparison. Count unsupported answers and escalations rather than quietly removing them from the sample.

Choose one question family, such as ordinary remote-access instructions. Record current search time, service-desk handling time and follow-up work. Your baseline might include people who solve the task themselves as well as people who contact IT. Measuring only ticket handlers can miss time spent searching before the ticket exists.

Calculate the whole cost per accepted task

Use this working formula: total relevant pilot cost divided by accepted tasks. Relevant cost includes implementation, licenses, model usage, integration support, source maintenance, evaluation and employee review time. Separate one-time setup from recurring cost so the decision maker can see what would change in a second month. Do not bury setup in a multi-year forecast before the pilot demonstrates repeatable use.

These are invented planning numbers, not vendor prices or customer results. Add setup separately. If the comparison process costs $2 per accepted task, a $3 alternative has not demonstrated labor-cost improvement, even if employees like it. It may have another benefit, but name that benefit and measure it independently.

Illustrative editorial photograph, not a customer result.

Do not turn minutes into an invented budget reduction

Suppose 600 accepted questions save four net minutes each, after review and corrections. That is 40 hours of potential capacity. At an illustrative loaded rate of $50 per hour, modeled capacity value is $2,000. It is not $2,000 of cash savings unless a real expense decreases. Finance must confirm whether overtime, contractor work or another paid cost changes.

Also ask where the capacity goes. If staff use the recovered time to clear a queue, measure the queue. If they improve service, measure the agreed service outcome. A spreadsheet multiplying minutes by salary can explain an opportunity; it cannot establish that the opportunity was realized.

Make quality failures visible beside economics

Track correct answers, correct abstentions, wrong-source answers, access failures, unresolved questions and reviewer minutes separately. An average satisfaction score can conceal a restricted disclosure. Set hard stop conditions before the trial, including the responsible owner and recovery route. Do not trade a serious permission failure for a favorable average time result.

Use the same mix of easy and difficult questions before and during the pilot. Record volume changes and process changes. If the source owner rewrites the guide halfway through, the improvement may partly come from clearer guidance rather than AI. That is still useful learning, but it changes what the experiment demonstrates.

Take a decision, not just a dashboard, to the sponsor

Write one page with the task, audience, baseline, observed result, total cost, unresolved failures and next decision. Recommend continue, narrow, revise or stop. Give the sponsor the actual comparison rather than an enterprise-wide extrapolation from a handful of questions. A bounded pilot can justify another bounded test without proving every department needs the same system.

Put this to work this week

Bring ten representative completed questions to a thirty-minute review with finance and the service owner. For each, record staff effort and whether the answer was accepted. Put setup cost on its own line. Ask what expense actually changes before using the word savings. Take the resulting evidence page, including unresolved questions, to the sponsor. Compare this with the board-report guide.

Use the AI Knowledge Access Test Sheet to define accepted answers before measuring. The NIST AI RMF offers broader voluntary risk-management context; this calculation is our suggested operating worksheet, not a NIST requirement. Request a focused demo with one question family and ask SimplSolutions to identify the sources, reviewers and ongoing costs needed to test it.