Artificial Intelligence is Changing Product Management: How PMs Can Keep Up
Artificial intelligence is transforming software development by becoming a key part of building, testing, and launching products. This shift is changing the role of product managers (PMs). They’re no longer just managing roadmaps or acting as intermediaries between business and engineering teams. Today, PMs are also AI strategists, experimenters, and quick problem-solvers.
Chloé Portier, a data scientist turned product manager, has witnessed this change up close. She explains how AI has moved from being a product feature to a core tool that accelerates prototyping, automates decisions, and reshapes team collaboration. To keep up, PMs need to update their skills, processes, and how they work with others.
Who is Chloé Portier?
Chloé Portier is a senior product manager at MadKudu, a company that builds AI-powered prospecting tools for sales and revenue teams. She holds two Master’s degrees in AI and data science and works at the crossroads of AI technology and business strategy.
Portier says, “I wanted to build products that make AI useful, not just analyze data.” At MadKudu, she turns complex AI models into practical tools that help revenue teams make faster, smarter decisions. One of her AI models boosted sales pipeline conversion rates by 200%, while another AI-powered LinkedIn copilot enabled more personalised outreach.
Beyond her role at MadKudu, Portier frequently reviews AI research for conferences and co-hosts AI Paper Bites, a podcast that translates AI research into business insights.
From Data Science to Product Management: AI as a Tool, Not Just a Product
Initially, AI was seen as a flashy feature without clear business impact. That’s changed. Companies now focus on building with AI rather than just building AI itself.
For PMs, this means they can quickly prototype ideas with AI-powered proof-of-concept models without waiting on engineering teams. These early experiments provide valuable insights to guide product decisions before heavy engineering investment.
Portier points out, “Problems that needed heavy engineering two years ago can now be solved in days.” This opens doors to improving user experiences and automating workflows in new ways.
AI in Action: Reducing Product Development Friction
Decision-making bottlenecks have long slowed down product teams. Portier found that AI can ease these bottlenecks by automating manual tasks, prioritising work, and aligning teams.
For example, at MadKudu, customer support often debated which tickets were urgent, delaying responses. The team built an AI agent that analysed tickets in real time, considering customer profiles, sentiment, past activity, and broader trends to prioritise effectively.
“We moved from inconsistent prioritisation to a clear, objective view of what matters most,” Portier explains. This AI-driven approach sped up fixing issues and improved collaboration across support, product, and engineering.
AI’s Role in Shaping Collaboration Between PMs and Engineers
AI isn’t just changing what PMs do—it’s changing how they work with engineers. Traditionally, PMs spent a lot of time gathering feedback, defining needs, writing specs, and iterating based on engineering constraints.
Now, AI lets PMs prototype experiences themselves. They can collect early user feedback and automate research, reducing the manual work needed for discovery. This frees engineers to focus on scalability and system design instead of guessing user needs.
Portier recalls building a LinkedIn copilot tool where the PM team created an AI prototype that extracted and structured prospect data before engineers got involved. “AI allowed us to prototype quickly, so engineers could focus on building it right,” she says.
The Future of AI-Driven Product Management
AI is no longer just a product feature; it’s changing how products are built and managed. Automation speeds up prototyping and decision-making, so PMs must adapt to stay relevant.
Portier sums it up: “Building AI is about understanding the user. AI works well only when it tackles real business problems, and that’s where product management shines.”
With AI automating tasks once reserved for engineering, PMs can spend more time uncovering user needs and ensuring solutions truly address them. Faster prototyping and better testing let teams develop and scale products more efficiently.
The PMs who use AI as a discovery and execution tool—not just a feature—will be the ones who thrive in this evolving landscape.
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