In Practice
How organizations across industries apply the discipline when AI-enabled work gets complex and the stakes for getting it wrong are high.
Medical Devices
AI Hearing Aid Development Program
A hearing aid manufacturer was 18 months into development when regulatory, hardware, and AI research teams were all working toward different versions of "done." No one had decided which constraint governed when they conflicted. Here's how that got resolved before it became a missed submission date.
Read the case study →Automotive
EV Display Platform Development Program
Three teams building the same product from different directions for 14 months — until an AI design tool produced 40 non-compliant UI variants six weeks before integration lockdown. The right answer had been in the output the whole time. The evaluation criteria just hadn't been correct.
Read the case study →Enterprise HR Technology
Fortune 1000 HRIS Consolidation
Four executive sponsors co-owning an HRIS consolidation had never established whose requirements governed when they conflicted. An AI architecture tool was recommending integration patterns the CISO would have rejected. The payroll manager hadn't been consulted. All of this was discoverable before a single vendor demo.
Read the case study →Hospitality & Construction
Historic Hotel Pool Reconstruction
A fixed budget, a set opening date, and a historic preservation society whose approval requirements the AI scheduling and cost estimation tools didn't know about. The scheduling tool was protecting the wrong variable. The cost tool was recommending materials the society would reject on sight.
Read the case study →These scenarios illustrate what Intent Management™ looks like when it's applied before things go wrong. If your organization is navigating something similar, let's talk through it.
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