HelloPM: The AI Native Product Manager — Masterclass Day 1

This is Day 1 of HelloPM’s free AI Native Product Manager Masterclass, recorded in May 2026. The session is led by Ankit Shukla, founder of HelloPM, which runs AI PM training programs and produces practical product management resources. Day 1 covers the foundational question: what does it actually mean to be an AI-native PM, as opposed to a PM who has simply added AI tools to their existing workflow.

Who it is for

Product managers who feel uncertain about where AI genuinely changes the job versus where the tooling mostly automates the same tasks they were already doing. The session does not assume deep technical knowledge. It also suits PMs preparing to interview at companies where AI product experience is increasingly required, or those looking for a structured way to assess their own AI literacy.

Key takeaways

1. The distinction between AI-native and AI-assisted PM work is one of workflow design, not tool selection. An AI-native PM builds processes that assume AI capabilities from the start. An AI-assisted PM uses AI tools to speed up processes that were originally designed for manual work. Shukla argues that the second approach has lower ceiling because it does not change what decisions get made or how fast the feedback loop runs.

2. The POWER framework structures how an AI-native PM approaches their work: Possibilities (mapping what AI can now do in a given domain), Opportunities (identifying which possibilities map to real user or business problems), Workflow (redesigning how the team moves from discovery to delivery given AI capabilities), Engineering (understanding enough of how the models, APIs, and architecture work to make informed scope and feasibility decisions), and Reflection (building in evaluation practices to catch model failures and drift). The framework is designed to be applicable across product categories, not just AI-first products.

3. Understanding how AI products are built changes how a PM specs them. Shukla walks through the core layers: the LLM layer, the application architecture sitting on top of it, and the user experience layer. PMs who understand that prompts, retrieval systems, and evaluation frameworks are distinct engineering concerns can write better requirements and have more useful conversations with ML engineers.

4. Most AI-related PM mistakes happen at the opportunity identification stage, not the execution stage. Teams build AI features that are technically functional but do not address a well-defined user problem, or address a problem users do not feel urgently enough to change their behavior. Shukla frames this as the core PM judgment call in AI product work: which AI capabilities are solutions looking for problems, and which close a genuine gap.

5. The masterclass is paired with templates, prompt libraries, and workflow resources that participants can access after attending.

Worth watching if

You are trying to articulate what “AI-native PM” means beyond a resume keyword, or your team is building AI features and you want a structured vocabulary for product conversations with engineering. The session runs at a fast pace and assumes the viewer wants practical frameworks rather than conceptual background.