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How to measure an iGaming AI pilot without inventing the ROI

Structure an iGaming AI pilot around reviewed answers, workflow baselines, loaded costs and measurable operational outcomes.

For Owners, CTOs, CFOs and procurement leaders

A pilot can produce an impressive demonstration and still fail to answer the purchasing question. The missing element is often a measurement design agreed before anyone sees the results.

Executive summary

Choose a small set of operational workflows, establish their baseline and define acceptance criteria before introducing the software. Measure the time to a correct, usable result, not merely the time to the first generated answer. Keep product capability, staff adoption and commercial impact as separate findings.

The framework below is a proposed evaluation method. Its numerical example is hypothetical and does not represent a Bounty AI customer result. That separation is essential if the final investment case is to withstand a CFO’s review.

1. Build a baseline that survives comparison

Select recurring work with an observable finish

Good pilot candidates have a clear trigger and an agreed outcome. Examples include producing a reviewed daily report, answering a predefined revenue investigation and resolving the information gap in a payment-service case. Avoid defining success as “the team found AI useful” without specifying what changed.

  • Record task complexity, data scope and the staff involved.
  • Separate active handling time from elapsed waiting time.
  • Count handovers, corrections and repeated requests.
  • Preserve the reviewed result and the evidence used to approve it.

Prevent easier work from inflating the result

Match comparable tasks across the baseline and pilot. A month-end reconciliation is not equivalent to a routine daily total. Track upstream incidents, staffing changes and policy revisions that could explain a difference. Where practical, use a crossover design or a comparable team to help distinguish tool effects from normal variation.

Do not count time removed from one team if the work simply moves to another unmeasured team. Include data-engineering support, definition maintenance and exception review in the baseline and the pilot. The objective is to measure the workflow, not optimise one department’s reported handling time.

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The rest of this guide is for Bounty AI customers.

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  1. 2. Set technical and operational acceptance gates
  2. 3. Convert measured benefits into a transparent cost model
  3. Operational outcome: a decision the business can defend
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