Agree what should improve first.
People can agree that they want to use AI while picturing different improvements. One person means faster work; another means better quality. The playbook asks what should improve, for whom and without making what worse.
Participants define a measure and examine the baseline. They then prioritise candidate use cases and keep the argument against their preferred choice in view.
Make the recommendation reviewable.
The group considers recurring costs, including checking, exceptions and maintenance. The closing summary records its recommendation and the evidence that is still missing.
The preview includes the cover and three selected pages. The complete workbook is used within the programme. Kakkar and Dagar (2026), © Gradeless AI.
Use a concrete business question to shape the learning.
Executive education and workshops