Agentic AI is rewriting the product manager's job description

Agentic AI is a hot topic in product management circles – and for good reason. The technology is progressing rapidly, and many predict it’ll shake up the industry forever.

A recent research paper by Nishant A. Parikh of Capitol Technology University offers a thoughtful answer to how agentic AI will change the product manager (PM) role.

Titled "Agentic AI in Product Management: A Co-Evolutionary Model," the research lays out a framework for understanding how autonomous AI systems are changing every stage of the product lifecycle, and what that means for the humans running the show.

The paper synthesizes more than 70 sources, draws on systems theory, co-evolutionary theory, and human-AI interaction research, and grounds its arguments in case studies from companies like Airbnb, Unilever, Intuit, JP Morgan, Microsoft, and Bank of America.

The result is a conceptual model that explores where product management is heading in the next few years.

From generative AI to agentic AI

Before getting into the framework, the paper draws a useful distinction between three categories of AI that often get lumped together.

Generative AI refers to reactive systems like standard large language models (LLMs). You prompt them, they produce content. They don't pursue goals on their own.

AI agents take things further. They can use tools, call functions, reason in sequence, and execute multi-step workflows. Their autonomy is limited to a single session or task, but they can retrieve real-time information and interact with external systems.

Agentic AI is the next step up. These systems involve multiple specialized agents that collaborate, plan dynamically, decompose tasks, and coordinate to achieve shared objectives. They have persistent memory, exhibit emergent behaviors, and can adapt in unstructured environments. Parikh describes them as orchestrated systems capable of real-time reasoning and adaptive control in mission-critical applications.

This matters because the practical implications of agentic AI for product management are categorically different from what generative AI alone offers.

Mapping agentic AI to the product lifecycle

The paper uses the Stage-Gate framework to show how agentic AI capabilities map onto each phase of product development.

In discovery, agentic AI can autonomously monitor social media, news, patents, and competitor announcements to surface emerging trends and unmet needs. Unilever, for example, used AI agents to scan unstructured data across platforms and detected a surge in demand for vegan and cruelty-free skincare before it became mainstream.

Intuit applied LLMs to QuickBooks support transcripts to cluster customer pain points around tax calculation, which then informed product planning.

In scoping, agentic AI acts as a co-creator. It can generate product concepts, user stories, wireframes, and even functional prototypes. Airbnb built an internal tool that converts wireframe sketches into frontend code, letting designers and PMs explore multiple UX layouts in hours.

In business case development, agentic AI provides dynamic market sizing, real-time financial modeling, and scenario simulation. JP Morgan's COiN platform reduced 360,000 hours of legal document review to seconds, freeing teams to focus on strategy rather than paperwork.

In development and testing, agentic AI generates code, orchestrates testing strategies, and manages CI/CD pipelines. Microsoft's use of GitHub Copilot in Agile sprint workflows illustrates how AI agents can accelerate iteration cycles and free developer time for higher-value work.

In launch, agentic AI orchestrates deployments, monitors system health proactively, and optimizes post-launch performance. Bank of America's Erica has handled over two billion interactions across 42 million clients, anticipating user needs and delivering personalized assistance at scale.

The co-evolutionary model

The heart of the paper is what Parikh calls the PM-AI co-evolutionary model.

The core argument is that product managers and agentic AI systems shape each other in an ongoing feedback loop. As AI takes over more autonomous task execution, product managers evolve toward higher-order responsibilities like ethical supervision, prompt engineering, multi-agent orchestration, and strategic alignment.

At the same time, AI systems are continuously refined based on the guidance, constraints, and feedback PMs provide.

A few specific shifts are worth highlighting.

First, the PM role moves from process gatekeeper to ecosystem orchestrator. You're no longer just facilitating human cross-functional teams. You're managing hybrid workflows where human and machine actors collaborate, sometimes smoothly and sometimes with friction.

Second, the decision-making hierarchy gets inverted in interesting ways. Traditionally, humans initiate, and AI executes. With agentic systems, AI can autonomously generate hypotheses, propose experiments, identify opportunities, and even initiate user-facing changes.

Your job shifts toward designing behavioral guardrails and ethical frameworks rather than directly controlling every decision.

Third, governance becomes central. As agentic systems take on strategic tasks like feature prioritization, financial modeling, and customer segmentation, you become accountable for the traceability, fairness, and reliability of AI-driven decisions.

That means model validation, human-in-the-loop protocols where appropriate, and clear documentation of how AI makes its calls.

Why this matters beyond product management

The paper places its findings in a wider context that should concern anyone working in AI-augmented organizations.

For one, it bridges a gap that most AI research ignores. Plenty of work focuses on making models faster or smarter, but very little addresses the organizational bottleneck of actually deploying agentic systems into corporate workflows without them failing.

Given that over 80% of AI projects fail to deliver expected outcomes, this gap matters.

The framework also offers a working blueprint for AI governance. As regulators wrestle with how to oversee autonomous AI, the paper clarifies where human accountability should sit when an AI makes an autonomous business decision.

And then there's the longer-term question the paper raises with refreshing candor. Parikh acknowledges that as agentic AI approaches something like AGI, the co-evolutionary relationship could become asymmetric.

Agentic systems might eventually manage the entire product lifecycle autonomously, from market analysis to launch execution, with minimal human oversight. The human PM role could become marginal or obsolete.

The paper notes early signs of workforce restructuring at Google, Amazon, Microsoft, IBM, Meta, and Salesforce, where AI-enabled productivity gains are beginning to consolidate job functions – though it cautions that this reflects a short-term trend and that the long-term trajectory remains uncertain.

What this means for you

If you're working in or around product management, the practical takeaways are straightforward. You'll want to build AI literacy, develop skills in model governance, and get comfortable with prompt engineering. Systems thinking becomes a strategic necessity rather than a nice-to-have because you'll be designing feedback loops across user, market, and ecosystem levels.

Organizations need to invest in infrastructure that supports human-AI collaboration, build governance frameworks that clarify decision boundaries, and create learning mechanisms that let teams and technologies adapt together.

The shift toward responsible innovation, ethical stewardship, and collaborative intelligence with autonomous systems is already underway. The question is how prepared you are to orchestrate it.

Editor’s note: The paper comes with significant caveats worth acknowledging: its framework is built entirely on conceptual synthesis with no empirical validation, and its focus on technology-intensive software organisations means its conclusions may not generalize to other industries or contexts.

The authors themselves flag these as core limitations rather than minor qualifications, and call for future research to test the model in real-world settings before it is treated as prescriptive guidance.

But as a roadmap for thinking through where product management is heading, it's a useful contribution.