AI as Your Co-PM: How Agentic AI is Reshaping Product Strategy

For decades, the role of the Product Manager has been defined by an ability to synthesize inputs, prioritize what matters most, and guide cross-functional teams toward building the right thing at the right time. In today’s environment, however, the “right thing” is harder than ever to pin down. Customer expectations are rising, digital funnels are increasingly complex, and the velocity of change—especially in AI—is relentless.

That’s why the idea of an AI “co-PM” is no longer theoretical. Agentic AI systems are beginning to reshape product strategy itself: gathering insights, proposing hypotheses, generating experiments, and even influencing roadmap decisions. Rather than replacing product leaders, these tools are becoming force multipliers—augmenting human judgment with scale and speed that was previously impossible.

From Data Analyst to Decision Partner

Traditional analytics platforms have always helped PMs understand what customers do. But the leap with AI is that it can now proactively surface opportunities, detect patterns, and recommend next steps. Think of it as moving from a static dashboard to a dynamic decision partner.

When I led subscription funnel redesigns at SiriusXM, analytics told us that customers were dropping out of mobile purchase flows at disproportionate rates. That insight led us to run a year-long series of A/B tests, ultimately delivering a 30%+ increase in mobile conversion.

Today, tools like Amplitude with AI Assist, Mixpanel Signal, or GA4 with predictive metrics can go further—identifying drop-offs, simulating design alternatives, and forecasting which changes would likely drive the biggest impact before you even launch a test.

AI in the Retention Battleground

Churn remains one of the thorniest challenges for subscription businesses. At Lee Enterprises, I partnered with their teams to design a “Click-to-Cancel” flow—mandated by FTC and Visa requirements, but also reframed as a strategic retention opportunity. By embedding behavioral prompts and save offers into the cancel journey, we achieved a 50%+ increase in online saves.

With AI-driven retention platforms like Pega Customer Decision Hub, Optimove, or Salesforce Einstein, that same workflow can now be fully dynamic. An AI co-PM can automatically segment customers, assign personalized save offers in real time, and continuously optimize based on results—turning what was once a static logic tree into a living, learning retention engine.

Personalization at Scale

Personalization has always been the holy grail, but execution is messy. It requires unifying data, building segmentation frameworks, and testing variations. At The Arena Group, I operationalized BlueConic as a Customer Data Platform (CDP), unlocking AI-driven ad placements and targeted segmentation.

Platforms like BlueConic, Segment + Twilio Engage, and Braze’s AI Journeys are now embedding generative AI directly into campaign orchestration. Instead of manually defining segments, AI can cluster users into “micro-cohorts,” test creative variations, and predict which messages are most likely to drive engagement—all without requiring manual rules from the product team.

Beyond the Backlog: AI in Roadmapping

One of the most time-consuming challenges for PMs is prioritization. Every quarter, roadmaps become battlefields where data, intuition, and stakeholder input collide. AI is beginning to play a role here, too. By ingesting product metrics, customer feedback, and resource constraints, an AI system can propose roadmap scenarios—helping PMs evaluate trade-offs more quickly and objectively.

In my consulting work at Chimera Digital Strategy, I’ve helped companies build prioritization frameworks that standardize intake and align development with strategic outcomes. The next step is embedding AI into these frameworks. Emerging tools like airfocus with AI Insights, Dragonboat’s AI prioritization, and Craft.io’s GPT-powered backlog assistant allow PMs to simulate roadmap choices in real time, balancing cost, effort, and value with data-backed recommendations.

The Human + AI Partnership

So, will AI replace product managers? Hardly. Product leadership is as much art as science. It requires empathy, storytelling, and organizational influence—areas where human judgment is irreplaceable.

But just as designers embraced Figma AI and engineers embraced GitHub Copilot, product leaders will increasingly embrace AI co-PMs as strategic partners. The winners will be those who see AI not as a threat, but as an amplifier: giving them leverage to focus on higher-order strategy while AI handles the heavy lift of analysis, experimentation, and optimization.

Getting Started

For product leaders wondering how to begin integrating AI as a co-PM, here are three practical steps and tools to consider:

1. Embed AI into experimentation: Use Optimizely’s AI Experiment Generator or AB Tasty’s AI-driven test design to accelerate your learning loops.

2. Use AI in retention journeys: Test Optimove’s churn prediction or Salesforce Einstein’s next-best-action models to dynamically reduce churn.

3. Pilot AI for roadmap simulation: Try Dragonboat’s AI prioritization engine or airfocus’s GPT roadmap assistant to evaluate “what-if” scenarios in seconds.

Closing Thought

The future of product strategy isn’t man or machine. It’s a partnership. Just as I’ve seen in projects at SiriusXM, Lee Enterprises, and The Arena Group, the right combination of data-driven experimentation and AI-powered insights can drive outcomes once thought unattainable.

The question isn’t whether AI will reshape product strategy. It already is. The question is: are you ready to bring an AI co-PM onto your team?