AI in product management is no longer a question of if. It is a when. And when we say ‘when,’ we are not talking about years. We are talking months, given the pace of innovation and adoption.
A new study in Management Review Quarterly, “Where does AI play a major role in the new product development and product management process?” by Aron Witkowski and Andrzej Wodecki, maps out the current state of AI in product work. It synthesizes more than 190 publications and practitioner insights to show where AI is already embedded and where research has yet to catch up.
Where AI is Already Making an Impact
The research, combined with our daily experience, highlights early discovery as one of the most active areas of AI.
Product teams are already experimenting with AI-powered tools that generate product requirements documents, saving hours of manual formatting and ensuring consistency across teams.
Some PMs use AI-driven prototyping platforms that can translate a natural language prompt into a clickable interface, allowing faster validation of early ideas. Others lean on AI assistants that generate user stories directly from interview transcripts or customer feedback, making backlog grooming less about rewriting and more about prioritizing.
These examples show how AI is no longer limited to analytics or personalization. It is working its way into the daily fabric of product management—the unglamorous but essential tasks that keep product cycles moving.
Where Research is Still Lacking
The Witkowski study also identifies blind spots. There is a lack of systematic research on how AI can influence concept testing or post-launch validation. Integrative frameworks that span the entire product lifecycle are almost absent. Questions of trust and transparency, which directly affect adoption, remain underexplored.
This gap matters. The activities most critical to practitioners—how AI can accelerate experiments or help close the loop after release—are barely documented in academic work. For example, while AI can now generate dozens of prototype variants instantly, there is little evidence to suggest whether this accelerates decision-making or overwhelms teams with choices. Similarly, while AI can generate user stories, there is no structured evidence that these outputs improve product outcomes or prevent the introduction of new biases.
Why This Matters for PMs
For PMs, the takeaway is that you are the testbed. The tools exist, but the validated playbooks do not. Your experiments with PRD generators, your trials of vibe-coding prototypes, and your attempts to scale user stories with AI are not just tactical choices—they are contributions to the collective understanding of what works.
Because academic research often lags by years, product managers are often the early researchers in their own field. Each team’s learnings can ripple outward, closing the gap between practice and theory.
Closing Thought
AI is already woven into the toolkit of product managers, and its presence will only grow. The question is not whether it belongs, but how quickly PMs can adopt responsibly and share what they learn. By using today’s tools thoughtfully and documenting outcomes, product managers do not just adapt to the future—they help define it.