Topic
AI Product Management
Most AI products fail not because the model is weak, but because nobody named the business number it had to move. I scope AI where it beats manual reliably — brand-voice wedges, inventory truth, conversational automation — and kill everything else. My AI work spans consumer apps (Anne), SMB platforms (Kaizen), enterprise inventory (AssetIQ), and customer automation (Navbot). The through-line: deploy AI only where operators trust the output enough to change behavior.
Impact
Key stats
- 264%
- Revenue growth (AssetIQ)
- 85%
- Inquiry automation (Navbot)
- 2×
- Core retention (Kaizen wedge)
Frameworks
How I think about this
Writing
Related reading
FAQ
Common questions
- How do you decide when AI is worth building?
- When it beats manual reliably on a metric leadership tracks — accuracy, response time, retention, or revenue — and operators trust it enough to change behavior.
- What's your approach to AI product scope?
- Find a narrow wedge with a measurable outcome, ship the smallest proof that earns trust, then expand. Kaizen's brand-voice focus doubled retention vs. a broader launch.
- Consumer vs. enterprise AI — what's different?
- Consumer AI wins on retention and acquisition efficiency (Anne). Enterprise AI wins on accuracy and auditability first (AssetIQ held the line on reconciliation before dashboards).
- Can you share AI product case studies?
- Yes — browse Kaizen, Anne, AssetIQ, Navbot, and Pico on the work page, each with headline metrics and narrative.