By Mariana Abdala
I have seen countless versions and types of the Product Requirement Document (PRD), and recently, I started to recognize that the form a PRD takes reflects how teams think. When written well, that document can align people around intent, value, and trade‑offs. It brings clarity and focus to the teams involved in the building, selling, and marketing of a product. When written poorly, it is useless, or just noise. Above all, the PRD should be a living document that is collaboratively informed and updated.
In the age of AI, the PRD is not disappearing. Rapid prototypes and vibe-coding are not replacing it. Instead, the PRD is evolving in its authority and influence. In some organizations, it's maturing from a standalone artifact to a living collaboration space where human judgment and AI acceleration work together.
About ten years ago, I transitioned from a startup environment where I built and scaled a Product and UX team, to a more established Product and Design organization of 40+ individuals at a publicly traded fintech company. Even though both environments were vastly different in maturity and size, and both had differing operating models, many believed Agile rituals could be stand-ins for official product requirements. Standups, Jira boards, and hallway conversations seemed like enough. But in both cases, I found that as the organizations scaled, the need for clarity only grew. Complexity demands articulation. A PRD remains a core way to connect what we are building to why it matters and what about it is measurable.
Along the same lines of reasoning, AI should not replace product thinking. It should strengthen it. Modern PMs use AI not to outsource decisions, but to challenge and refine them. AI can organize messy input, synthesize customer data, and generate structured options, but the product person still owns the judgment, the prioritization, and the narrative with stakeholders.
AI can help PMs:
Summarize qualitative feedback into themes
Draft alternative versions of problem statements
Spot contradictions or missing assumptions
Suggest acceptance criteria and edge cases
Compare PRD versions to highlight changes over time
These tools make iteration faster. They lower friction, so teams spend more time on substance and less on formatting. When I think of the evolution of the PRD as a collaboration space, I find it's less about documentation work and more about shared reasoning.
A modern PRD still answers these same core questions:
What problem are we solving, and for whom?
Why this problem, now?
What outcome defines success?
How will we measure learning and impact?
What risks or unknowns must we surface early?
Again, a PRD in the AI era is not a static artifact. It is a living product of continuous learning. I stress this point often in my trainings, especially with junior or early-career Product Managers, because they are launching their Product careers at such a unique, fascinating time in technology history. If you're a Product Leader or people manager, try to underscore to your direct reports that they should use AI to increase the speed of insight, not the speed of output. Humans still have to author the document, and the document becomes leaner, clearer, and more adaptable.
In sum, AI has raised the bar for how well Product Managers are expected to articulate thinking. And let's be real, we are all getting pretty good at pinpointing a gen-AI narrative. Now it's our time as Product Managers to ship something better, and have it reflected first and foremost in a PRD.
