I remember a few years ago, being deep in product management, trying to optimize a user flow. I spent countless hours in workshops, refining requirements, and meticulously detailing every interaction. It felt like I was talking to a brilliant but incredibly rigid technical team. Fast forward to today, and the speed at which AI is changing things is absolutely mind-blowing. We've gone from AI tools that needed step-by-step instructions to autonomous agents that can actually make decisions, freeing up PMs to focus on strategy.

For me, this shift isn't just about better AI models. It's about how we, as product managers and builders, adapt and push the boundaries of what AI can do for us. Every stage of AI's growth has opened new doors, not just because the technology improved, but because ambitious teams experimented, iterated, and showed what was achievable. And today, agentic AI is becoming the product manager's new assistant, taking over tasks that used to eat up valuable time.

AI as a Simple Tool for Product Managers

The first wave of AI was all about direct inputs producing direct outputs. As PMs, this meant giving very precise prompts to get things like market research summaries, feature requirement drafts, or basic user story generation. It was useful for structured, individual tasks.

Think about early AI for drafting user stories or basic competitive analysis reports. It was helpful, but it still required PMs to do most of the heavy lifting and give very specific instructions. The AI was responding, not initiating.

AI as a Product Management Assistant

The next phase saw AI move beyond one-off interactions into more integrated assistants within our workflows. Instead of just creating an output, AI became part of a continuous process, helping PMs with ongoing tasks.

For example, AI embedded within our documentation tools might suggest improvements to PRDs, or an AI assisting with A/B test analysis. These tools reduced friction, certainly, but they still needed significant human direction. They helped us, but they didn't act on their own or make complex decisions.

AI as a Product Management Agent

Then came AI that could not only assist but act. AI agents execute tasks end-to-end, taking product management automation to another level. This is where AI truly becomes an extension of the product manager, not just a tool.

For example, imagine an AI agent that monitors user feedback channels, identifies recurring themes, and automatically drafts initial problem statements for new features. This is a major shift—AI isn't just enhancing our capabilities; it's taking on entire segments of work that bogged down PMs.

The Rise of Agentic AI for PMs

Now, agentic AI is moving beyond simply executing predefined tasks to dynamically figuring out what needs to be done next for product initiatives. Instead of following a fixed script, these AI agents can adapt in real time to the evolving needs of a product or market.

Think of AI managing early-stage market validation, autonomously collecting data and even setting up initial user interviews based on predefined criteria. This marks the beginning of AI handling complex product processes with minimal PM intervention, freeing you up for higher-level strategic thinking.

Finding New Use Cases for Agentic AI in Product

With agentic AI on the rise, product leaders and founders are in an intense race to discover the next breakthrough use cases. Investors are making huge bets on startups that can leverage AI to redefine product development and management.

This is both an incredible opportunity and a challenge. Some AI-powered product tools will inevitably fall short, but for the ones that succeed, the upside in efficiency and innovation for product teams is enormous.

Consider a tool that, based on a product brief, generates a comprehensive technical spec, creates an initial UI prototype, and even suggests A/B test variations—all autonomously. This kind of AI-native product development would have been unimaginable just a few years ago.

What's Driving This Shift?

Several key factors are accelerating the evolution of AI for product management:

• Better Models: More capable AI like GPT-4, Claude, and multimodal systems that can understand and generate text, images, and even code with increasing sophistication.

• Better Infrastructure: Advances in AI memory, orchestration frameworks, and specialized databases have made complex, multi-step AI applications more viable and reliable.

• Successful Experiments: Startups are continuously pushing the limits of AI, demonstrating what's possible and inspiring other product teams to adopt and build on these innovations.

Why Agentic AI Matters for Product Managers

Each AI evolution doesn't replace the last; it builds on it. AI assistants didn't make basic AI tools obsolete, and agentic AI won't eliminate the need for assistants. Instead, every step forward unlocks new, higher-value use cases for product managers.

I believe we're just at the beginning of agentic AI. The most exciting part isn't what AI can do today—it's what product managers will build with it next. The teams and companies that master this new paradigm will define the next era of product development, where PMs focus more on vision and strategy, and less on repeatable, time-consuming tasks. Soon, instead of asking AI to perform a task, we'll just define a product goal—and AI will figure out how to achieve it.

The race is on for product managers to harness this powerful new assistant.

Related Articles

• The AI Product Manager: Redefining the Role in the Age of Intelligent Automation

• Scaling Product Innovation: How AI Frees Up PMs for True Creativity

• The Strategic PM: Offloading Operational Burden with AI Agents