When AI Does Everything: What’s Left for Product Managers?

Introduction: With each breakthrough in AI, headlines proclaim the end of yet another human occupation. In the last couple of years, we’ve seen AI writing code, designing UI mockups, answering customer support queries, and even generating business strategies. This rapid progress begs the question: If one day AI can do “everything,” what is left for product managers to do? If execution becomes a solved problem - handled by AI agents churning out features 24/7 - do product managers become obsolete, or do they become more important than ever? Let’s explore how the product management role is evolving in an era when AI handles more of the traditional workload.

AI’s Impact on Execution: It’s true that AI is dramatically changing how products are built. Code generation tools can produce boilerplate code or even complex algorithms with minimal human input. Design AIs can create numerous variations of a screen in seconds. Automated analytics can identify user trends and anomalies instantly. In a hypothetical (but increasingly plausible) scenario, you might ask an AI, “Build me a mobile app for X service,” and it could handle everything from infrastructure to interface. We’re not fully there yet, but the trajectory is clear: the cost and time of execution is falling sharply thanks to AI. For product managers, tasks that used to consume a lot of time - writing detailed specifications, creating wireframes, slicing data for insights - are becoming faster. A McKinsey study quantified this, finding that using generative AI tools boosted PM productivity by 40%, and shaved time off the product development cycle, partly by automating tasks like drafting PRDs and synthesizing research  . AI can even generate a first pass at a product roadmap or backlog based on customer inputs and competitive analysis. So, if a lot of the “doing” can be offloaded to smart tools, what is a product manager’s unique value?

The Evolving Role - More Essential Than Ever: Ironically, as AI takes over execution, the strategic and leadership aspects of product management become more pronounced. As tech thought leader Marty Cagan points out, even with generative AI in the mix, the product manager’s role becomes more essential (and more difficult), not less . Why? Because someone still needs to decide which problems are worth solving, which customer pain points align with the business vision, and how to prioritize in a landscape of near-infinite building capability. In many ways, the PM becomes the translator and arbiter between what could be built and what should be built. AI can generate ten different product concepts or 100 feature ideas in a flash, but it lacks human judgment to know which ones fit the company’s mission, or which one genuinely resonates emotionally with users.

Moreover, the PM’s role in understanding users becomes even more critical. Empathy, intuition, and deep user research aren’t fully automated. Yes, AI can crunch survey data or even detect emotions in feedback, but truly understanding the “why” behind user behavior often requires human connection. Product managers will spend more time with users—virtually or in person—digging into their needs and validating ideas, then using AI to help brainstorm solutions or run experiments. Think of the PM as the editor-in-chief in a world where AI is producing endless drafts. The PM sets the vision (the story we want to tell), curates the best ideas (chooses the right articles), and ensures everything aligns to create a coherent product narrative.

Additionally, product management extends into realms that AI can’t handle alone: cross-functional leadership and communication. Someone has to synthesize inputs from stakeholders (leadership, marketing, sales, customers) and make trade-off decisions. When things go wrong (e.g., an AI deployment causes an ethical issue or a PR backlash), a human PM steps in to navigate the crisis, communicate transparently, and adjust course. AI doesn’t shoulder accountability - people do. Product managers become the responsible conscience of products in an AI-driven development process, ensuring that just because something can be built quickly doesn’t mean it should be without consideration of consequences.

Shifting Focus to Strategy and Vision: As mundane tasks diminish, product managers are likely to focus more on strategy. The job may look less like writing user stories or slicing backlogs (the AI might handle the first draft of those), and more like defining the long-term vision, understanding market dynamics, and orchestrating complex systems. A great PM in the AI era might spend time scenario-planning: “If the cost of doing Feature A is near-zero thanks to AI, should we do it? How does it fit our competitive strategy? What about the data implications or brand impact?” They’ll also focus on the human elements of product usage. AI might optimize for clicks or conversions in the short term, but a PM will think about user trust, brand values, and long-term engagement - which sometimes means overriding what pure data/AI optimization would do. Essentially, product managers will be the guardians of the product’s purpose. Interestingly, McKinsey’s research suggests that with AI taking on grunt work, PMs can indeed dedicate more time to defining product vision, guiding roadmap, and engaging with customers . These are the areas where human creativity and insight are irreplaceable. For example, envisioning a product that fundamentally delights or changes user behavior involves creative leaps - something AI, which works on existing patterns, isn’t as good at. PMs will cultivate that creative vision and then use AI as a powerful tool to execute parts of it.

New Skills and Mindset: That’s not to say the PM role won’t change; it will, significantly. Product managers will need to become proficient in working with AI. This means learning how to ask the right questions of AI tools (prompt engineering might become a core PM skill), understanding AI outputs, and correcting AI when it’s off-track. They’ll also have to be savvy about data, privacy, and ethics, as AI often raises these issues. Expect PMs to collaborate closely with data scientists and AI engineers to steer AI’s application in products responsibly. Another crucial skill will be sensemaking - the ability to derive meaningful conclusions from AI-generated analyses. AI might spit out 20 insights from user data; the PM must connect the dots to see the narrative or strategy behind them. In essence, product managers become product coaches or directors, guiding an ensemble where AI is doing many of the hands-on roles.

Conclusion: Far from rendering product managers obsolete, the advance of AI is making the role more pivotal. When AI does “everything,” product managers will do what AI can’t: truly understand the customer, define the vision, ensure ethical alignment, and make the tough decisions that intertwine product success with human values and business strategy. The job may feel less tactical and more like being a strategist, a curator, and a leader. In an AI-saturated future, PMs won’t be writing every user story or analyzing every dataset - their AI assistants will handle much of that - but they will be the ones asking the right questions, telling the right story, and ultimately, accountable for the product’s success or failure. As Marty Cagan succinctly put it, virtually all PMs will need to be “AI product managers” and the role will be more essential but also more difficult with AI . The exciting part is that by offloading drudgery, AI could free product managers to focus on the most meaningful aspects of their work: building products that genuinely matter in users’ lives. And that, arguably, is a future where great product managers are needed more than ever.

Marty Cagan argues the PM role becomes more essential - and more difficult - with AI in the mix.

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