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

This message made my morning ☀️

A reader just shared that they landed an AI Product Manager role, and mentioned my content helped along the way.

Here's the thing though: I'm glad the content helped, but they put in the work.

The learning. The interviewing. The career pivot. That's all them.

Moments like this remind me why I write. Not to have all the answers, but to make the path a little clearer for folks navigating the transition.

If you're in the middle of your own AI PM journey right now, keep going. It's working.

AI Product Leader | 0→1 Builder & Educator | Ex-Nike, Ex-Amazon, Ex-Disney

My kids got Harry Potter Guess Who for Christmas, and watching them play gave me an idea. What if I built a digital version with our 46 family members? They could learn their relatives the same way they're learning about Hogwarts. I opened Bolt and started building. 34 versions later, I had a working game. But the real lesson wasn't about AI coding tools.

It was seeing PM fundamentals play out in real-time, compressed into one afternoon. Clear requirements prevented wrong interpretations. Defining the data model upfront avoided rewrites to the architecture. Thinking through edge cases early meant fewer fixes later. AI doesn't ask clarifying questions or push back on ambiguity. It just builds exactly what you describe.

Vibe coding is a mirror for PM discipline. It validated what I've learned across teams: clear requirements, defined data models, explicit acceptance criteria. The 4 patterns that made this work 👇

If I were teaching someone about AI product lifecycles for the first time, I'd start with POCs.
Not because they're the first step in a framework diagram.

Because they're where most AI products quietly die.

It reminds me of my research days working with Zebrafish embryos. You don't scale up a staining protocol before you've proven it works on a handful of samples.

Same logic applies to AI.

But here's what most teams get wrong: they run AI POCs like traditional software spikes. Build it, demo it, ship it.

AI doesn't work that way.

Your POC might hit 85% accuracy. Looks great in a demo. But what happens to the 15% that fails? Who handles those cases? What's the cost of being wrong?

These are the questions traditional PMs aren't trained to ask.

In my latest post, I break down:
→ Why POCs are the critical gate (not just another phase)
→ Three types of AI products and how they change what you're validating
→ The question most teams forget to ask before calling a POC "successful."

Link in comments 👇

Building your personal brand is hard. On LinkedIn, off LinkedIn—doesn't matter. It's just hard.

I have been at it for about a year now, and honestly? Still figuring it out.

I'm part of PDMA Seattle's committee, and our next session is with Leslie on how to make LinkedIn work for your PM career.

📅 Jan 14, 12-1 PM PST 🔗 https://luma.com/wno7fylk

Our last session on PM hiring trends had a great turnout, and everyone walked away with something useful. Expecting this one to be just as good.

Come through if you're trying to figure out the brand thing too.

Product Development and Management Association Seattle team - Anna Bigornia Sako Eaton Harmony Crawford Prabina B. Matt Foster Sahit G.  Banu R.

#productmanagement #linkedin #personalbrand

Open article: Stand Out With Your Brand Out. How Product Managers can grow their career using LinkedIn. · Zoom · Luma by luma.com

Stand Out With Your Brand Out. How Product Managers can grow their career using LinkedIn. · Zoom · Luma

Chief Product & Growth Officer || Products, AI Data Platforms, Partnerships & Ecosystems || Advisor || 3x Founder || ex: MSFT EBAY

I'm a high rate-of-learning executive in high rate-of-change environments, often at the pioneering stage (Series A-D) of frontier tech (AI: LLM, ML, NLP, etc). My entire career reflects this. I am very good at solving for PMF and starting, building, and commercializing high-growth enterprise product / platform businesses. 

My 15 years of operationalizing and commercializing AI and data products span a wide swath: LLM in SportsTech, ML in HealthTech, NLP (Conversational AI), Recommendation Systems for Marketplaces, Expert Systems, Data Quality in Health, Data Science, Data Management in FinTech, etc.

I have a strong commercial orientation with 25 years of P&L ownership in pre-IPO start-ups, corporate enterprises and product and data strategy consulting practices. I am entrepreneurial and technically-adept with Product, Engineering, Data, Partnership Ecosystem, and Growth functions.

Team athletics were woven across my life; I played 6 different sports in H.S. and College. Today, I move a little slower but still enjoy mountain running, freediving, and skiing with others.

RESULTS
My contributions, collaborations, and leadership have resulted in 
-Product: 3 pioneering products, 5 technology and data platforms, 3 new ventures, 2 start-ups, 1 joint venture in Japan
-Growth & GTM: 4 GTM programs, $700M+ in attributable revenue
-Ecosystem: 20+ strategic Big Tech alliances, 100+ channel partnerships, 50+ technical alliances and OEM partnerships

𝗖𝗟𝗜𝗘𝗡𝗧𝗦
70+ executive-level strategic client engagements in the F500: Microsoft, Meta, Google, Amazon, Salesforce.com, Snowflake, Capital One, PayPal, eBay, Cisco, GSK, T-Mobile, Xerox Parc, F5, Providence Health, Weyerhaeuser, etc.

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