As a Product Manager (PM) building AI and analytics tools, I’m constantly seeking ways to accelerate our customers’ work. The irony, however, was that my own role often involved the very kind of manual labor I was trying to optimize for others.

Like many PMs, I found myself caught in a cycle of admin overhead: manually compiling competitive research from scattered sources, formatting and reformatting Product Requirements Documents (PRDs) during and after review cycles, and translating finalized requirements into dozens of Jira tickets.

This work isn’t just time-consuming; it creates Strategic Debt. Every hour spent managing documents is an hour pulled away from high-value activities like customer conversations, validating critical assumptions through prototyping and meetings, and defining what the roadmap looks like. The real value of a PM comes from synthesizing information and exercising judgment, not managing & formatting documents.

My goal wasn’t to eliminate our documentation, but to create a system that instantly converts my decisions into documentation (i.e., PRDs and Jira plans).

Redefining How I Work

I didn’t just build software; I built a new way of working for myself. It’s a framework that organizes my thinking & helps me execute. Because the system is only as good as what I put into it, it’s pushed me to be disciplined about delivering clean, validated strategy upfront.

Here’s how it works:

1. Smart Market Validation

Instead of spending hours sifting through different websites & reports, I start by clearly stating my key market assumptions and targets. The system then quickly gathers and structures data on competitors, user pain points, and current trends. The output is a tight, synthesized brief. This brief immediately tells me if the data validates or challenges my thesis, ensuring I start the PRD process on solid ground.

Example: Testing an Onboarding Hypothesis

I’ll walk through a fictional scenario where I’m acting as a PM rethinking a mobile fitness app’s onboarding flow. The starting assumption is that longer, more personalized onboarding drives higher conversion. Rather than accept that at face value, I designed the prompt to explicitly test the assumption by analyzing how leading competitors structure their onboarding experiences.

The result was an Executive Market Briefing that showed the trend wasn’t about onboarding length, but perceived value. As the system surfaced: “The most successful apps don’t simply ask more questions — they make data collection feel like value delivery.” In practice, I’d normally validate this direction through customer conversations and qualitative research; here, the system helps surface and pressure-test assumptions early so that validation focuses on the most promising directions.

Starting from this validated insight helped us avoid building around the wrong premise and grounded subsequent product decisions in what actually drives user value.

2. Translating Intent into Structure

Once I have a validated direction from the market brief, I translate that intent into something executable. I give the system a clear problem statement and the 3–5 core user outcomes we need to deliver. From there, it generates a strong first draft of the PRD, including user stories and early technical considerations. This isn’t meant to be a final document — it’s a well-structured starting point.

The value here isn’t that the system “writes requirements for me,” but that it removes the overhead of formatting and initial narrative-building. That lets me focus immediately on the quality of the decisions, tradeoffs, and open questions.

Example: An Organized PRD

Using the strategic direction surfaced in the research brief, I generated the V1 requirements. The system translates an abstract goal like “value before paywall” into concrete, review-ready user stories. I still provided significant context — the problem framing, success metrics, constraints, and desired outcomes — but the system handled the structuring.

The output isn’t a messy text dump. It’s a nearly complete PRD with a clear problem statement and prioritized user stories, which moves the conversation quickly from writing the document to aligning stakeholders on what we’re building and why.

3. Resolving Conflicts and Aligning Teams

When I get feedback from engineering, design, and leadership, the system actively helps me implement those changes. Rather than manually addressing disparate comments, I simply tag the core disagreement (e.g., “Engineering constraint vs. Design goal”). The system helps me brainstorm tradeoffs and helps me make an educated decision.

4. Automated Work Breakdown

Once I have the final PRD, the system takes over the mechanical task of splitting the work by generating a detailed Jira plan.

The Core Lesson: Quality In, Quality Out

The system itself is built using modern tooling (n8n workflows using Gemini, Claude Desktop & MCP), but the specific tech stack isn’t the point.

The core principle I learned is that the AI acts as a reliable, powerful tool that follows rules, not a chatty assistant.

My most critical takeaway is that we can build powerful systems, but the quality of what we’re putting into the systems and how we’re using it directly correlates to what we get out. Moving forward, I think we need to build experiences to help people ensure that they’re entering high quality inputs to get high quality outputs.

This framework is like a strict editor: a vague, fuzzy idea yields a useless PRD, but a focused, validated thesis results in a clean, deployable plan.

A New Focus

I’m no longer spending a decent chunk of time managing my documents; I’m investing it in activities that actually add value:

• Deeper Customer Focus: I now have more time to talk to customers and partners to uncover those crucial, non-obvious details that define a great product.
• Accelerated Learning: I have dedicated time to read and absorb new publications, research emerging technologies (like new product releases from Anthropic), and stay ahead of the curve.
• Strategic Forward-Thinking: I spend more time thinking about questions like: Are we making the right trade-offs? Is this the most valuable path forward? How does this fit with where our company is headed and what our customers are looking for?

The future of Product Management is about moving past manual tasks and stepping fully into a strategic role. So, don’t write your next PRD — focus on the validated strategy that the machine will translate into your next PRD. Even if you don’t work in Product, I strongly encourage you to discover ways in which you can accelerate your own work with emerging tech!

If you’re interested in chatting about anything AI or Product related- feel free to connect with me on LinkedIn.

Stop Wasting Time: Don’t Write Your Next PRD (Do This Instead) was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.