AI Product Management

My AI product management starts one step earlier than most definitions of the job: choosing which problems deserve an AI product at all. An expensive, slow, high-volume workflow with a checkable quality standard is a candidate; a workflow where nobody can say what "good" means is not, yet. The products under my management came out of that filter — a localization system aimed at a cost and turnaround problem the whole company could feel, a retrieval platform built once so every team's AI product inherits the same grounded answers, and agent-facing tool servers that turn systems of record into surfaces agents can safely use.

The method is evaluation before conviction. AILA went wide only after a blind quality comparison against professional human translation, and its first year is stated in measured terms — volume, cost per unit, turnaround — published on its own page. The retrieval platform logs per-stage score distributions and percentile bands, so retrieval quality is calibrated from production evidence rather than demos. Alongside the bar-setting sits the unglamorous majority of the role: aligning support, marketing, engineering, and executives on what ships and what waits, sequencing a roadmap so each release earns trust for the next, and saying no to AI features whose success could not be measured. I also build a large share of what I manage — pipeline code, serving surfaces, tests — which keeps the roadmap grounded in what the systems can actually do; the split between the leading and the building is documented on the AI Product Manager at Spotware page.