20+ years
In the environments where AI actually gets deployed
Industrial AI product strategy
Your AI roadmap has to work in the field. Not just the pitch deck.
I pressure-test world models, agentic workflows, and mission-critical AI bets against field reality, operator trust, and technical constraint—before they harden.
AI can simulate the next move. Operators still own the consequence.
1M+ assets
Operational scale where AI earns trust or gets switched off
7,000+ field users
Across workflows used by 50+ operators
$1.2B+
Efficiency gains from deployed systems, not demos
The decision loop
Foresight without field accountability is still a demo.
Model the operating system.
Start with the assets, workflows, exceptions, and data scars that shape the real decision.
Explore consequences.
Use world models and agents to make paths visible before a roadmap hardens around one.
Put decision rights back in view.
Define where AI recommends, where it acts, and where an accountable operator must decide.
Where this work matters most.
When the AI bet is still open
The roadmap hasn’t committed yet, but pressure is building to pick a direction. The highest-leverage moment.
When operators won’t trust the product
Demos work. Field adoption is stalled. Usually a positioning and workflow-fit problem, not a product problem.
When the roadmap is filling too fast
Scope is compounding. The question is what to cut, what to sequence, and what should never have been on it.
Latest essays
July 22, 2026
World Models
When AI learns to ask what happens next—and why operational systems need more than plausible predictions.
Read the essay
June 30, 2026
When AI Learns Physics…
What changes when industrial AI learns the rules of the physical system it is trying to predict.
June 11, 2026The Small Business AI Opportunity Is Hiding in Plain Sight
AI can create capacity, but only when the product removes work instead of adding another system to manage.
Selected proof
Built products that reached operator scale.
- 50+ operators across the U.S., Canada, and Argentina.
- ~15% U.S. market share for a mobile-first field automation product.
- 50%+ ARR expansion during the shift from services into SaaS.
Improved time-to-value in messy field environments.
- 1M+ assets supported across deployed platforms.
- 7,000+ field users across operator workflows.
- 6 weeks instead of 6+ months to value.
- 90%+ retention on multi-tenant enterprise SaaS.
Built AI systems for decisioning and execution.
- 30-50% less manual work through OCR, route optimization, and workflow redesign.
- $13M+ ARR portfolio with AI-ready foundations across 10+ systems.
- $100M+ revenue opportunities shaped through advisory work.
"Hari is an exceptional Product Manager with deep knowledge in the AI space. His ability to bring together competing views and needs into a cohesive product vision is extremely valuable."
EVP, Chief Supply Chain Officer · Healthcare
"He combines technical depth, business acumen, and exceptional communication skill in a way that drives product initiatives forward."
Product Executive · Energy Operations Software
Download the decision framework
Mission-Critical AI: A Decision Framework
A workbook for making the right bet before the roadmap hardens.
- Score roadmap bets.
- Audit trust and workflow fit.
- Pressure-test data reality.
Instant access
Fill this out and the next screen opens the guide.
Need a direct link instead? Open the ebook.
Bring the decision before it gets expensive.
A short note on the decision or AI bet is enough to start.
Fastest path
Share the decision and context. I usually reply within one business day.
Or email hari@turingpilgrim.com.