The Death of PMs in the Age of AI-Agents is Greatly Exaggerated

How PRDs will become 'Prompt-Based Specs'

photo courtesy Carol M. Highsmith

Only the paranoid survive.

—Andy Grove, former CEO of Intel

Whenever I hear someone foretell the “death” of a discipline—be it marketing, teaching, or in this case, product management—I’m reminded of Andy Grove’s famous warning: “Only the paranoid survive.” In other words, it’s all too easy to fool yourself into thinking everything’s under control, right until the ground shifts beneath you. Today, one of the biggest shifts is the advent of AI Agents—those software-driven entities that perform tasks intelligently and autonomously. Industry heavyweights like Claire Vo (CPTO at LaunchDarkly) have even suggested we’re on the brink of seeing the entire product management function fade away.

But are we, really? In this piece, I’ll walk you through why product management is evolving rather than dying. The epicenter of this revolution? AI Agents—software-driven entities that perform tasks intelligently and autonomously. As these agents become more prevalent, we’ll explore how the product management skill set is shifting from classic requirement-gathering to what is emerging as “prompt engineering.” By the end of this article, you’ll (hopefully) appreciate that the future of product management isn’t six feet under. Instead, it’s alive, well, and primed for reinvention.

I. Introduction

Let’s start with a question: Why are people predicting the death of product management in the first place? The short answer is that AI now performs tasks that product managers have historically owned. A product manager (PM) used to spend significant time:

• Collecting user needs and feedback,

• Writing detailed product requirements documents (PRDs),

• Communicating with engineers and designers to ensure those requirements see the light of day.

Now, thanks to advanced AI, software tools can sift through user feedback by scanning social media, support tickets, and direct user surveys, sometimes drawing up entire feature outlines. The concept goes: If an AI agent can figure out what users need, how best to build it, and even generate prototypes, maybe we don’t require as many PMs.

But hold on. If you’ve read a bit of tech history, you’ll notice a pattern. With each new wave of automation—be it spreadsheets replacing manual ledgers, or code generation tools assisting developers—some folks cry doom for that profession. Often, the opposite happens: The role evolves into something more strategic, more specialized.

Claire Vo’s perspective (link to tweet here) is informed by real data:

AI is absolutely automating product tasks. However, to claim “death” might be an overstatement. A 2024 study by the Stanford Institute for Human-Centered AI found that a majority of organizations investing in AI for product development reported an increased need for strategic oversight and human validation. Put simply, somebody’s got to keep the AI on track, ensuring it churns out features aligned with business goals and user desires.

Enter the new product manager.

II. Setting the Stage: What’s Changing in Product Management?

Historically, product managers juggle three big tasks:

1. Understanding User Needs

They gather user feedback, parse market trends, and conduct competitive analysis. Then they create a vision—“Here’s what we should build and why.”

2. Writing PRDs and Roadmaps

They produce those oh-so-lovely documents (PRDs) enumerating features, constraints, timelines, acceptance criteria—basically the blueprint for the development team.

3. Coordinating Cross-Functional Teams

They talk to engineers about feasibility, designers about usability, marketers about positioning, and so on.

Now let’s toss AI Agents into the mix. An AI agent can:

• Autonomously crawl through user reviews and support tickets, analyzing sentiment at scale.

• Suggest new features or improvements based on pattern matching or predictive analytics.

• Even generate wireframes or initial user flows—some advanced tools are heading in that direction.

So, does that mean we trash the job postings for product managers? Not so fast. For every piece of the job that AI automates, new responsibilities sprout up—particularly around controlling what the AI does and how it does it. It’s not enough to let an AI rummage through data and spit out a 10-page feature specification. You need context. You need someone who truly understands the business domain, user motivations, privacy constraints, and brand identity. That’s the domain of a PM.

III. The Case for Evolution, Not Extinction

1. Emerging Skillsets

Let’s imagine you’re a product manager at a midsized SaaS company. A new AI tool arrives that can ingest tens of thousands of user requests and produce a structured feature backlog. Where does that leave you? Well, now you’re the one who:

• Sets the parameters for what data the AI uses.

• Chooses how the AI weighs certain inputs—maybe enterprise customers’ feedback is more critical than free-tier users’.

• Validates whether the suggestions actually align with your strategic roadmap.

• Translates raw AI outputs into an actionable plan that’s feasible given budget and time constraints.

The net effect is that you start spending more time curating, fine-tuning, and managing the AI’s intelligence—less time manually combing through data. This shift demands a new skillset, commonly referred to as prompt engineering or AI orchestration.

2. Why PMs Are Well-Positioned

You might wonder: Why not hire AI specialists or data scientists for this? While they are vital, data scientists aren’t typically responsible for the end user or the business context—PMs are. Product managers stand at the crossroads of user experience, business viability, and technical feasibility. That vantage point is perfect for guiding how AI agents operate, because you grasp the bigger picture. You know how a tweak to the AI’s parameters might affect next quarter’s feature adoption or how certain data constraints (like GDPR) might limit what the AI can do.

3. Shifting Responsibility and Ownership

At this point, the conversation stops being “The AI will replace you!” and starts being “The AI will transform your job.” You’re no longer the note-taker of user feedback; the AI does that. You become the conductor of an orchestra, shaping how the AI interprets different sections of the music (data), how strongly it emphasizes the violins (enterprise customers), and where it softens the brass (edge cases).

In a recent Harvard Business Review article on AI-driven product management, contributors found that early adopters of AI see PMs stepping up into a more strategic conductor-like role. The surprise, though, is that many PMs report loving this transformation. They get to focus on creative problem-solving, while the AI tackles the busywork.

IV. The New Core of Product Development: Prompt Engineering

1. Defining Prompt Engineering

Let me reintroduce a phrase that’s been floating around tech circles: Prompt Engineering. If you’re new to it, think of a “prompt” as the script or set of instructions you give to an AI model—like ChatGPT or a specialized domain model—to guide its output. “Generate a summary of user feedback focusing on key pain points regarding performance.” That’s a prompt. The AI then returns a structured analysis, bullet points of issues, or even user story suggestions.

Prompt engineering is about crafting these instructions carefully to get the best possible result. It’s a bit like writing a PRD, but for an AI. Both tasks require clarity, an understanding of constraints, and an overarching sense of what success looks like.

2. Comparing Prompt Engineering to PRDs

If you take a classic product requirements document (PRD) and strip it down to essentials—what’s left? You get:

• Objective: What are we trying to achieve?

• Constraints: Budget, time, technology limitations.

• User Stories & Acceptance Criteria: Desired behavior from the user’s perspective.

• Edge Cases: Conditions the product must handle gracefully.

• Metrics: How we’ll measure success.

Now take a typical prompt for an AI agent. You might specify:

• Goal: “Analyze 1,000 user support tickets to find the top 5 recurring issues.”

• Constraints: “Only look at data from the past 3 months. Ignore any request that involves out-of-scope features.”

• Format: “Return the results in a bulleted list, ordered by frequency.”

• Context: “Our user base is 80% SMB and 20% enterprise, so weigh enterprise feedback a bit more heavily.”

These instructions look remarkably similar to a high-level PRD. Both revolve around a structured outline of objectives, constraints, and success metrics. The difference is that the audience for your “prompt document” is the AI, not your engineering team.

3. Examples & Best Practices

Let’s say a PM historically wrote a PRD for a new feature: “User needs to export data in CSV format.” (Check out my thoughts on User Stories here). The PRD might detail how the feature should handle internationalization, timezones, large data sets, etc. In the new world, the PM might feed an AI agent a prompt:

“Analyze user feedback from the finance sector regarding data exports. Identify the top 3 requested export formats and propose a high-level design. Use relevant metadata to ensure compliance with local regulations.”

If done right, the AI returns a mini-PRD: a recommended feature set, potential pitfalls, and proposed acceptance criteria. You, the PM, refine that output, weaving in business strategy and practical constraints. Voilà! The AI has done some heavy lifting, but you remain the guiding hand.

V. The PM’s Role in Managing AI Agents

1. AI Agents as “Team Members”

In a world where AI can create user flows, triage bugs, and generate prototypes, these agents essentially become new “teammates.” This isn’t hyperbole; some organizations treat AI agents almost like junior employees who can be assigned tasks. As the product manager, you coordinate not just the human designers, developers, and QA testers, but also these AI “colleagues.”

For instance, you might instruct an AI agent to test UI components for accessibility compliance. It combs through your app, flags color-contrast issues, and sends you a neat summary. A developer then resolves the flagged issues. The AI acts again, retests, and confirms the fix. This synergy between AI agents and people allows for faster iteration cycles.

2. Accountability, Ethics & Guardrails

Here’s where it gets tricky. AI isn’t foolproof; it can make mistakes, or worse, act on biases in the training data. Product managers are uniquely positioned to ensure the AI’s outputs align with corporate values, privacy laws, and ethical guidelines.

• If your AI suggests a feature that scrapes personal user data without consent, it’s your job to catch that.

• If your AI tries to push out a feature with an accessibility gap, you must hold it accountable.

Those guardrails become crucial in regulated industries—healthcare, finance, education—where missteps can have serious consequences. The PM ensures the AI adheres to compliance requirements and moral guidelines. If there’s one surefire argument that product management isn’t dying, it’s that human oversight is indispensable for ethical, user-centric products.

3. Human Oversight & Iteration

I like to think of an AI agent as that super-smart but sometimes naive intern who wows you with speed and cleverness—but occasionally misses context or nuance. You let them run with an idea, you appreciate their fresh perspective, but you also have to check their work. You refine the outcome, or else risk shipping a solution that’s out of touch with real user needs.

VI. Practical Steps for PMs to Embrace the Future

1. Learn the Basics of AI & Prompt Engineering

Let’s not pretend any of this is trivial. If you’re a PM who’s new to AI, you might wonder where to start. Here are some quick suggestions:

• Online Courses: Platforms like Coursera, Udemy, and edX offer beginner-friendly AI overviews.

• Hands-On Tutorials: Tools such as ChatGPT or Hugging Face’s open-source models let you tinker with prompts.

• Community Forums: Reddit’s r/MachineLearning or specialized Slack communities can be gold mines of shared knowledge.

2. Experiment Internally

One of the best ways to grasp AI’s potential is to run small pilots within your existing product workflow:

• Use an AI agent to analyze customer support logs for recurring bugs or feature requests.

• Ask the AI to generate initial user stories or acceptance criteria, then see how well it performs.

The more you experiment, the better you’ll get at writing prompts, setting constraints, and understanding the AI’s limitations.

3. Collaborate with Data Scientists & Engineers

If you work at a company with a data science team, invite them to your product planning sessions. They can explain the machine learning models at work, help you interpret output, and highlight technical constraints. In turn, you can clarify business goals, user needs, and how you’d like the AI to behave ethically. This cross-pollination ensures the AI solutions are both cutting-edge and user-aligned.

4. Redefine Product Requirements

Here’s a radical idea: Instead of the typical PRD, consider a “prompt-based spec” that you share with the AI and your human teammates. For instance:

Prompt Title: “Dashboard Feature Suggestions”

Goal: Identify the 3 most requested dashboard improvements from enterprise accounts.

Constraints: Use feedback data only from Q2 and Q3 of 2025.

Format: Return bullet points, each with a recommended feature name, user rationale, and complexity estimate.

Edge Cases: Mention any compliance flags for regulated sectors.

Success Metric: If 80% of the time, these suggestions align with actual user feedback from separate interviews, we’ll refine and implement them.

This merges the old PRD style with modern AI usage. The rest of your team sees how you’re directing the AI, and they can weigh in on constraints or success metrics. Over time, you’ll refine this approach, maybe even building a library of “prompt specs” for different areas of the product.

VII. Conclusion

Recap

Despite the doomsday scenarios and bold pronouncements of “the death of product management,” we’re witnessing a renaissance rather than a funeral. AI agents undoubtedly automate tasks that PMs used to handle manually—like scouring user feedback and drafting requirements. But so what? History shows that when automation arrives, roles often shift upward in strategic value. Product managers aren’t becoming obsolete; they’re taking on new responsibilities around prompt engineering, AI oversight, and ethical guardrails.

Looking Ahead

As AI continues to work its way into every nook of our tech stacks, we’ll see new frameworks, job titles, and best practices. Some companies might have “AI-Oriented Product Manager” roles or specialized “Prompt Architect” positions. The key is to adapt, upskill, and keep that big-picture perspective that PMs have always excelled at.

Call to Action

If you’re in product management, now’s the time to explore how AI can handle the grunt work, allowing you to focus on creativity and user empathy—things automation can’t easily replicate. Start small: craft a pilot prompt, gather feedback, refine, and repeat. You’ll discover how quickly an AI can become your assistant, not your replacement.

In the words of Steve Jobs, “We’re here to put a dent in the universe. Otherwise why else even be here?” Embrace this mindset: create prompts, experiment with AI, and understand how it can elevate your product processes. Because if there’s one thing clear to me, it’s that while AI might be shaking up product management, it’s also offering a golden opportunity for PMs to become more strategic, more innovative, and more indispensable than ever.

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Ready to dive in? Give prompt engineering a whirl and show your AI who’s boss.