HelloPM: The ultimate guide to AI native product management

Published in May 2026, this Substack post by Ankit Shukla summarizes a live masterclass on AI native product management. The article distinguishes between three types of practitioners: skeptics who dismiss AI outright, followers who copy workflows from others, and inventors who build for their own problems. The guide is aimed at helping PMs move into the third group.

The central framework is POWER — Possibilities, Opportunities, Workflow, Engineering, and Reflection. The sequence matters: before picking a tool, a PM identifies what AI is capable of doing, then maps those capabilities to specific friction points in their own workflow. Only after that does the work become technical. Engineering in the POWER model means choosing the right tool for the task — from a simple chat prompt to a full agent system — not defaulting to whatever is newest. Reflection closes the loop: evaluating what worked and what did not, then adjusting.

A substantial portion of the guide covers LLM constraints in terms a PM can act on. Hallucination, indeterminism, context limits, knowledge cutoffs, and the cost of running inference at scale are each explained with practical implications. This section is useful for PMs who need to set user expectations or write acceptance criteria for AI features, since it frames limitations not as bugs to apologize for but as system properties to design around.

The technical section introduces three solution types: RAG for grounding models on current data, fine-tuning for specialized or cost-reduced inference, and agents for tasks requiring external action. The guide is explicit that simple approaches should be attempted before complex ones — a chat prompt before an agent chain.

The POWER framework applies both to PMs building AI products and to those looking to use AI in their day-to-day work. The article treats these as the same skill developed on different inputs: practice on your own workflow first, then apply what you learn to the product you are building.

Useful for PMs at any level who are spending time on AI tools without clear outcomes to show for it, and for product leads evaluating how to bring AI judgment, not just AI features, into their teams.