The 1987 moment for PMs: Staying Ahead of the Curve

You've seen the headlines. AI is everywhere. Tools promise to write your Product Requirement Documents (PRDs) in minutes, analyze user feedback with a prompt, and turn rough ideas into working prototypes. You're asking: "Will AI replace product managers?" Your LinkedIn feed is full of "AI will replace product managers" think pieces.

Yet when you look around your organization, nothing fundamental has changed. Teams still ship slowly. Strategy still requires human judgment. Stakeholders still need managing.

Welcome to your 1987 moment.

AI Is Changing Everything. There Is Hype. But What Exactly Is Changing?

Even though companies invest significantly into AI (a 44.5% increase year-over-year with $252.3 billion in AI last year) according to Stanford's AI Index and use it in more and more functions, McKinsey's 2024 global survey found that only a few product organizations have adapted to that new trend.

As Sam Altman puts it in his recent post:

"We got some great new products and not much about the world changed, even though computers can now converse and think about hard problems." AI progress and recommendations

This gap between hype and reality creates real anxiety. So yes, things are changing. But not the way most people think.

The Solow Paradox: A Pattern We've Seen Before

In July 1987, Nobel laureate economist Robert Solow made an observation that would become famous: "You can see the computer age everywhere but in the productivity statistics."

This wasn't hyperbole. Computing capacity had increased 100-fold through the 1980s. Personal computers sat on every corporate desk. Spreadsheet software—first VisiCalc, then Lotus 1-2-3, and finally Excel (which launched in November 1987)—had revolutionized business analysis. Companies invested billions in technology.

Yet labor productivity growth had collapsed from over 3% annually in the 1960s to around 1% in the 1980s. The computer age was visible everywhere except where it should have mattered most: the economic data.

This pattern, known as the Solow Paradox or productivity paradox, didn't resolve quickly. It took 20 to 25 years—from roughly 1973 to 1995—before productivity growth returned to its earlier levels of around 2.5% annually, according to Stanford research.

Why did it take so long?

The delay happened for predictable reasons:

• Technology overlaid old processes - Companies used computers to do existing tasks faster, not to reimagine workflows

• Learning curves took time - People needed years to develop fluency and discover new possibilities

• Complementary investments lagged - New skills, infrastructure, and organizational structures had to be built from scratch

Because technology adoption isn't about buying tools. It's about reorganizing work.

The spreadsheet didn't transform business until companies rebuilt their entire planning processes—not just their accounting departments.

I observe a similar pattern with the daily business of product management. AI is being used but it hasn't changed the way we work—yet.

The Three Phases of Transformation

Understanding where we are in the transformation helps you make better decisions. Based on historical patterns and current projections, AI's impact on product management will unfold in three distinct phases.

Phase 1: Task Augmentation and Learning

We're here now. This is the phase where AI tools overlay existing processes without fundamentally changing them. You use ChatGPT to draft PRDs faster, but you're still writing PRDs the same way. You use AI for research synthesis, but your research process hasn't changed. Only a very few organizations have redesigned their workflows.

Phase 2: Workflow Reorganization and Skill Evolution

This is when things get interesting—and when your job fundamentally changes.

Phase 2 is marked by business process redesign and complementary investments. Companies don't just use AI tools; they rebuild entire workflows around AI capabilities. New roles emerge. Old roles transform. Current predictions see that the product trio blends. UX designers doing coding tasks, developers doing product work. The product management function shifts from tactical execution to strategic orchestration. Instead of writing PRDs, we might compose a RAG (Retrieval Augmented Generation) for everyone to find their own answers or even have the final product and reverse engineer the details from a template.

Phase 3: Productivity Gains and New Market Leaders

AI augmentation becomes standard. The tools are ubiquitous. The workflows are established. The productivity gains are realized but also normalized.

For product managers, this means a smaller number of people doing more interconnected work, supported by AI for execution. Not because PMs were replaced, but because the role evolved to focus on what humans do best: judgment, imagination, innovation, and empathy.

How Will AI-Driven Efficiency Change PM Roles?

The efficiency gains are real. You probably know first hand how fast an email is written, a user interview analyzed or a working prototype is vibe-coded.

But here's the critical nuance: experience matters. Senior product managers maintained quality while gaining speed. Junior PMs gained speed but often sacrificed quality—or even worse, adjacent roles think they can do the job. AI amplifies your judgment—it doesn't replace it.

This creates a challenging dynamic. Companies can do more with the same team size, or the same work with smaller teams.

The least vulnerable PM tasks are exactly what you'd expect: strategic thinking, connecting the dots, stakeholder negotiation, ethical decision-making, customer empathy, and cross-functional leadership. The most vulnerable are documentation-heavy tasks that follow repeatable patterns.

Your job isn't disappearing. But if your current work is 70% automatable, you need a clear answer to the question: what's your unique value proposition?

The European Context Makes This More Complex

If you're working in Europe, your timeline looks different. Technology adoption typically takes longer.

If you're working in Europe, you're competing in a market where AI fluency will be scarce and therefore valuable—or you'll be left behind as a small group pulls ahead.

What Should Product Managers Do Now?

AI-based work will be the new normal. Change how you work now, or your role will be designed out later. That's not fear-mongering—it's pattern recognition. It's way less risky to shape the change than being shaped by change.

Start using AI tools effectively today. General-purpose tools like ChatGPT and Claude deliver great productivity gains. Start with high-toil tasks: PRD drafting, meeting summaries, research synthesis, competitive analysis.

The effectiveness gain feels like magic, but that is not the limit. Your imagination is the limiting factor. The journey unfolds new ways that only come naturally if you've been there already and don't stop innovating, playing and trying things out with AI.

Build leverage AI can't replicate. Three capabilities matter most in an AI-augmented world:

Domain expertise that goes beyond what AI can learn from data. Deep understanding of your industry, customers, and market dynamics that comes from lived experience.

Product taste — the ability to know what "good" looks like and how to get there. This is pattern recognition across hundreds of products and experiences. A newbie will derive features directly from user interviews, an expert takes the steps to understand the underlying opportunities, develops and ideates on solutions before selecting the features to build.

Stakeholder management and political navigation. Emotional intelligence, trust-building, and conflict resolution remain stubbornly human. As one product leader put it: "If I can 10x myself with AI, it means we can go longer without hiring another PM—but someone still needs to align the executives."

Be honest about your position. Ask yourself: If AI could do 70% of my current work, what's my unique value proposition?

For those willing to adapt, the opportunities are substantial. LLM experience is now a top-three hard skill requirement for product managers, according to recent job market analysis. AI PM roles are exploding. Companies need people who can bridge human judgment and AI capability.

The Bottom Line

We are at our 1987 moment. AI is visible everywhere in product management but it takes time till you see it in the productivity statistics.

Your job isn't to fight this transformation or to naively embrace every AI tool. Your job is to take a key skill that likely brought you into product: curiosity. Learning what's doable, exploring new frontiers is the best way to position yourself for the future.

Change how you work now, or someone will design your role out later.

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