I build AI products from zero to one, and I teach others how to do the same.Over 12+ years, I've taken AI/ML products from proof-of-concept to launch at Amazon, Nike, T-Mobile, and Disney. The work I love most is early-stage: validating ideas, getting executive greenlight, and shipping MVPs that prove value before scaling. At Nike, that approach drove a $100M annual reduction in product returns.I also teach what I build. I co-created and lead the AI Product Management Certificate at the University of Washington, where experienced PMs ship real GenAI products complete with evaluation plans and GTM strategies. Previously, I trained 4 cohorts at Product School.What I bring: the ability to translate messy AI capabilities into clear product value, align technical and business stakeholders, and move fast without breaking trust. I write about AI PM craft at aipmguru.substack.com.I'm currently exploring opportunities to lead 0→1 AI product work at companies solving meaningful problems. I'm especially energized by AI in healthcare and education, though I'm open to any domain where the product can genuinely improve lives. 

Shaili Guru

Hemant Bothra and I are running an in-person workshop where we're going to build something together - in the same room, like the old days.

3 hours. You'll leave with a working tool.

No coding experience needed (you read that right). We'll use Cursor (an AI coding assistant) and walk through everything step by step. If you've ever been curious about "vibe coding" but didn't know where to start, this is it.

Good fit if you're a PM who wants to actually build things, or just someone who learns better by doing than watching.

AI/ML Product Leader | Driving Innovation with AI | PM Mentor and Advisor

Open article: Build Your First AI Tool: ChatGPT Output Cleaner Workshop · Luma by luma.com

Build Your First AI Tool: ChatGPT Output Cleaner Workshop · Luma

Madhumita Mantri

Product Lead@StarTree | Podcast Host | Follow me for 0 to 1 Data AI Product Management | Product Management Coach | Ex-PayPal | LinkedIn | Yahoo | Grace Hopper 2019 Speaker

Mission: Build trustworthy, customer/user-centric Data and AI/GenAI products and platforms that unlock durable growth at global scale.

I'm a Product Lead with over 20 years of experience, including 9+ years specializing in B2B/B2C Data and AI products. My passion is building and scaling Data/AI/GenAI-powered products from the ground up, with a proven track record of achieving product-market fit and delivering impactful outcomes. My expertise lies in strategic roadmapping, technical leadership, and delivering customer/user-centric products at scale for startups like StarTree and enterprises like Walmart Marketplace, PayPal, LinkedIn, Yahoo, and Intuit.

Key Career Highlights
• Walmart Marketplace: Led the launch of an AI-powered seller risk assessment platform that processed millions of events daily, protecting billions in GMV through automated decisioning.

• StarTree: Drove a product from 0-to-1, launching a real-time anomaly detection tool that secured 7 enterprise customers and grew ARR by 80% in less than 2 years.

• LinkedIn: Spearheaded a content quality scoring system that increased user engagement by 15% and delivered data management products that reduced data inconsistencies by 30% across 8+ products in less than a year.

• PayPal: Led the development of an enterprise data management platform that resulted in an estimated 10x annual savings and reduced merchant churn by 15%.

• Intuit: Launched a self-serve data analytics MVP and QuickBooks Cash Flow Predictor with 92%+ forecast accuracy, which improved SMB retention.

• Yahoo: Owned and delivered a self-serve CRM, booking, and pricing platform and application supporting Yahoo's deals with Microsoft and internal sales.

Expertise
• Product strategy, Data & AI/GenAI products, Marketplace trust & safety, Risk, Real-time decisioning, ML, Feature engineering, Data solutions (observability, governance), Go-to-market, Cross-functional leadership.

I'm interested in - Product Lead for GenAI/Data/AI Products, Marketplace Trust & Safety, Risk/Integrity at scaled B2C/B2B, SaaS, and platform companies. Open to advisory roles with data/AI-first startups.

Let's connect - If you're building high-scale Data/AI/GenAI products or trust & safety systems and need a pragmatic, metrics-obsessed product leader to drive outcomes, reach out here on LinkedIn.

Outside of my primary role, I'm a passionate learner and contributor, speaking at industry events like Grace Hopper and the Realtime Analytics Conference. I also share my expertise as a content author, podcast host, and PM interview prep coach.

Staff Product Manager

Leading product strategy, development and GTM for the decision data & services product for Walmart Marketplace Seller Risk Assessment and Mitigation.

AI Implementation Roadmap Template — my 1-page product plan for AI!

Most AI work fails not on models, but on product: weak problem framing, fuzzy metrics, and no shipping cadence. I use this 1-pager (inspired by CRISP-DM, NIST AI RMF 1.0, ISO/IEC 42001) to turn ideas into shipped value.

What I lock down (as a PM)
- User problem & JTBD — who, pain, trigger, outcome
- Value metric & guardrail — one success metric + one “won’t-do”
- Use case — decision, context, latency needs
- Data plan — sources, consent, quality risks, retention
- Build vs. buy — cost, time, moat, vendor exit
- Model path — baseline → pilot → GA; evals & A/B
- Safety — misuse/bias tests, HITL, rollback, incident playbook
- MLOps — CI/CD, monitoring (data/model/prompt drift), SLOs
- Change management — owners, workflows, enablement, comms
- Release plan — 30/60/90 with decision gates

2 product examples (real PM framing)

1) Support summarization (B2B SaaS)
- Problem: Agents alt-tab; slow resolution; inconsistent tone
- Goal: ↑ first-response CSAT from 4.3→4.6 in 60 days
- Guardrail: hallucinations <1%; source citations required
- Ship plan: shadow mode (wk 2) → agent opt-in (wk 4) → A/B 50% (wk 6) → GA (wk 10)
- Adoption: in-tool nudge, win-story feed, 15-min micro-training

2) Marketplace return-fraud risk (trust & safety)
- Problem: high loss from repeat abusers; blunt rules hurt good users
- Goal: −20% fraud $ in 2 quarters; FP <1.5% on good sellers
- Safety: bias checks by segment; manual review for high-risk; kill-switch
- Ship plan: heuristic baseline (wk 2) → pilot (wk 6) → A/B (wk 10) → GA (wk 12)
- Ops: case templates + “why flagged” explanations in UI

Product tips I actually use
- Start with a “won’t-do” list (e.g., no auto-actions >$X without review).
- Shadow mode first: measure lift before touching users.
- Design the rollback the same day you design the launch.
- Instrument value, not vibes: DAU of the AI feature, task time, CSAT, margin.
- Make it explainable: show inputs, rationale, and next best action in-product.

Exercise to try out!
Pick ONE user problem. Fill these in one line each:
• - JTBD 
• - Value metric 
• - Guardrail 
• - Minimum 2-week test (offline/shadow) 
• - Weekly safety check (bias slice, prompt-injection, drift).
Post it in your team chat with: “What would break this plan?”

#AIProductManagement #MLOps #ResponsibleAI

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