Shreya Agrawal

🔎 Hiring Panel Lens [Series 17] The AI Failure Question Most PMs Answer Wrong After several AI PM interviews recently, I noticed something interesting. Many candidates are very comfortable explaining: ➡️the model ➡️the architecture ➡️the feature ➡️the launch But the conversation becomes much quieter when we ask something else. “What was the biggest failure risk in that AI system?” Strong candidates rarely talk about AI projects as only success stories. They talk about: ➡️where the system could fail ➡️what decision boundary was risky ➡️how they contained that risk ➡️when the system should escalate to humans Because AI products are not just feature launches. They are decision systems operating under uncertainty. One answer signals execution. The other signals product ownership. Execution framing: “We built an AI assistant to automate support queries.” Ownership framing: “We had to decide which support queries the AI could resolve autonomously without increasing escalation risk.” Now the panel understands: ✔️the product tension ✔️the decision boundary ✔️the risk trade-off In AI product work, seniority often appears in how someone discusses failure scenarios, not just outcomes. Strong PMs can explain: ✅️where the AI system might break ✅️how failure is detected ✅️who takes control when it does. That thinking is surprisingly rare. 📌 Quick reflection: When describing an AI initiative, do you naturally talk about: A) The feature B) The technology C) The failure risk and decision boundary Curious where most people land. #productmanagement #aiproductmanager #productleadership #artificialintelligence #pmcareers #techcareers #HiringPanelLens

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I am opening 4-6 PM interview prep slots for the Oct-Nov time frame. As I come close to finishing up with the August - September cohort of aspiring PMs who signed up with me, I'm feeling energized and motivated to engage with a few more before this year ends! The best frameworks, skills, tools have all been shared a million times on LinkedIn. I wont repeat the same again, but I will make you aware of your blindspots if I see any. Here's what we'll focus on through our sessions: • Instilling genuine confidence, the kind interviewers can feel • Helping you stand apart using your own strengths and unique skills • Sharpening (or introducing) product thinking, metrics-based execution, and product strategizing • Behavioral preparation that sounds like you, not a script • Real examples from real PM careers, not textbook frameworks If you're an aspiring or early-career PM getting ready for interviews this fall, my DMs are open. #ProductManagement #PMInterviews #CareerCoaching Update: I may be unable to respond to connection requests if we have not met previously. Best way to have an intro is to use my calendar link to connect. You can also email me at kos@epkstay.com as an option, but fastest way to schedule is right here. https://lnkd.in/gu8VaadQ

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How do you prove a product is working when the evidence is that people stop using it? That's the question behind ambient AI.  The product is working best when users barely notice it. Most teams still measure success with engagement metrics - DAU, session length, click-through rate.  These made sense when the job was to pull users in and keep them there. Ambient AI inverts that entirely. Fewer opens means the product caught the problem before you had to look for it. Fewer clicks mean it decided without you.  Shorter sessions mean it respected your time. The product doing its job well looks identical to the product failing silently. That's a genuinely hard case to make in a quarterly review.  "Users are spending less time with us" still sounds like churn, no matter how you frame it.  Stakeholders trained on engagement metrics will hear alarm bells. So what do you measure instead?  Can be outcomes avoided, decisions accelerated, or time reclaimed.  But those require instrumentation most teams haven't built, and trust most stakeholders haven't extended yet. This isn't a communication problem. Figuring out what "good" looks like in ambient AI products is a real unsolved design problem - one most product teams are walking into without a framework. The teams building ambient AI right now are essentially making up the measurement model as they go. If you've shipped something where success means users notice it less - how did you convince anyone it was working? #ProductManagement #AIProduct #ProductStrategy --- I'm Rishav. I write about the gap between how product management is sold and how it actually works. Follow if you live in that gap.

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FDE is a revenue function. That’s how Tanay Padhi thinks about forward deployed teams. He led product at Orby AI, building agentic process discovery tools for enterprises. Now he’s starting something new. His framing stuck with me. A few highlights: 🏢 On how enterprises actually work: “How executives and even process owners think work is happening versus how it’s actually happening often has a very big gulf between it. Once you get to the people doing the job day to day, they show you all the edge cases.” 📋 On process debt: “In the same way you have technical debt, it’s like process debt. People have figured out band-aids to solve problems that exist in their heads but don’t lend themselves to real transformation.” 🤝 On what enterprises actually buy: “Enterprises are not trying to buy software. They’re not trying to buy a seat of a certain product. They’re trying to buy outcomes.” 🔄 On the FDE-product feedback loop: “If the FDE is seeing the same thing four or five times, that’s probably a failure on the core product team. They’re not building into the platform the things they’re hearing repeatedly.” 💰 On the economics: “The numbers they target are about one to one and a half million dollars revenue per FDE. Your 50th deployment has to be substantially faster, like four times faster than your 10th deployment. That’s what makes the economics work.” Thanks Tanay for the conversation, it was a pleasure! Full conversation: https://lnkd.in/efCVfaRN

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The first time someone asked me to write a PRD, I nodded like I understood. Then I opened a blank doc and stared at it for a while. I knew how to write SOAP notes. Treatment plans? Easy. Outcome measures? Automatic. But this? This felt like jumping into a group chat mid-conversation with no context. So I did what most clinicians do when we’re unsure. I observed. I over-researched. I mapped it out like a new eval. I studied real PRDs and broke them down the only way I knew how. What’s the problem? Who’s impacted? What’s at risk if we don’t solve this? And what does “better” actually look like? That was the turning point. Because it turns out, clinical training builds product thinkers. We know how to listen deeply. We look for friction. We build plans that fit human behavior, not just what’s on paper. That first spec wasn’t perfect. But it was clear, grounded, and built around real people. That’s something I still bring into every room. I ask, “What does this feel like on the other side?” Because it’s easy to obsess over flows and features. Much harder to remember someone is using this in pain, on a time crunch, with a kid screaming in the background. So if you’re a clinician stepping toward tech — Don’t underestimate what you already know. You’ve been writing specs your whole career. They just didn’t call them that. #pttoproduct #productmanagement #healthtech #careertransition #uxdesign

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