Post by Syed Shawzab Hassan Hadi

Just completed the AI Product Management Certification — and it changed how I run every engagement. The biggest shift: AI isn't a feature you bolt on. It's a product discipline. System prompts are product specs. Evaluation sets are your unit tests. Model selection is a PM decision, not an engineering one. I'm applying this right now across enterprise modernization engagements — building AI copilots that compress months-long manual workflows into minutes, and running spec-driven development where AI generates the majority of production code and the team validates and hardens. We're consistently beating traditional delivery timelines by weeks. Two ideas from the program I can't stop thinking about: Henry Shi (Anthropic) on defensibility: Product-Market Fit alone doesn't cut it anymore. You need Product-Market-Model-Channel Fit — your model choice and distribution strategy aligned with your product and market. As building gets easier, distribution becomes the moat. And hallucinations? Largely overblown. The real gap is evaluation rigor. A demo is not a product. Rohan Varma (OpenAI) on staying frontier: Lean into the latest model capabilities. Push them to the limit. If something doesn't work, shelve it — then retry when the next model drops. The frontier PM is the most curious one in the room. Let the tokens flow. That last one rewired my approach. I stopped waiting for perfect solutions and started testing aggressively with every model release. Some ideas that failed 6 months ago work now. The cost of experimentation is near zero. The cost of waiting is enormous. If you're a PM still treating AI as someone else's problem, you're already behind. Thank you Rohan, Henry, Moe Ali, Jennifer Liu and everyone who I met and collaborated with along the way! Stay tuned for my Capstone project and now back to shipping! #AIProductManagement #AI #ProductManagement #EnterpriseAI #AIPM #GenerativeAI #LLM #Maven