AI Is Changing Product Management — Chloé Portier Explains How PMs Can Keep Up

Artificial intelligence is revolutionizing software development, playing a central role in building, testing, and launching new software products and drastically changing the role of product managers (PMs) in the process.

As AI becomes more deeply integrated into the development process, PMs are no longer just roadmap managers or liaisons between business and engineering—they are now AI strategists, experimenters, and rapid problem-solvers.

Data scientist-turned-product manager Chloé Portier understands this shift better than most, witnessing firsthand how AI has gone from being a product feature to a core enabler of product development itself. With AI now accelerating prototyping, automating decision-making, and reshaping team dynamics, PMs must evolve their skill sets, processes, and collaboration models to stay ahead.

In today's article, Chloé shares her real-world experiences on how AI is redefining the role of product managers—and what the next generation of PMs needs to succeed.

Who Is Chloé Portier?

Portier is currently a senior PM at MadKudu, which specializes in building AI-enabled prospecting products for sales and revenue teams. She holds two Master's degrees in AI and data science, and has built her career at the intersection of AI technology and business strategy.

"I realized I didn't just want to analyze data; I wanted to build the products that make AI useful," says Portier. "At MadKudu, I bridge the gap between data science and product, turning complex AI models into tools that help revenue teams make better decisions faster."

She has already made a significant impact on MadKudu's clients, with one AI-powered decision model she worked on increasing sales pipeline conversion rates by 200%, while a LinkedIn copilot that can scrape prospect data enabled more personalized outreach.

Beyond her work at MadKudu, Portier regularly reviews research papers and presentations for AI-related conferences. She also co-hosts AI Paper Bites, a podcast that translates AI research into business language, with her background and work in both data science and product management helping her turn academic topics into applicable insights.

From Data Science to Product Management: AI as a Tool, Not Just a Product

At first, AI was largely considered to be a flashy product that didn't really solve tangible business problems. That has changed in recent years, when businesses began to shift focus from building the AI itself to actually building something with AI.

As AI becomes a core part of product development, PMs no longer need to wait for their engineering teams to build early prototypes. Instead, they can quickly test ideas through early proof-of-concept models that require little to no engineering work. In turn, these early insights can lead to better-informed product decisions before the commitment of significant engineering resources.

"Problems that felt unsolvable—or required heavy engineering investment—two years ago can now be tackled in days," says Portier. "That's opened up opportunities to solve complex user experience challenges and automate workflows in ways that weren't possible before."

AI in Action: Reducing Product Development Friction

A key example of AI changing the role of product managers lies in a long-standing pain point in the profession: decision-making bottlenecks. Portier discovered that AI can solve these bottlenecks by reducing the time spent on manual processes, prioritizing tasks, and aligning across business functions.

This was the case when she and her team built a new AI customer ticket prioritization system. MadKudu was experiencing delays in addressing critical customer questions and concerns because of ongoing debates over which tickets were most urgent.

The team built an AI agent that could analyze incoming tickets in real time, using data points like the customer profile, sentiment in the ticket, historical account activity, and larger trends across tickets to streamline the decision-making process.

"We went from long debates and inconsistent prioritization to a process where everyone, from customer support to product management and engineering, had a more objective view of what matters most. The AI-powered prioritization model streamlined our decision-making and allowed us to focus our energy on delivering fixes and improvements faster."

AI's Role in Shaping Collaboration between Product Managers and Engineers

According to Portier, AI is fundamentally changing the role of product managers: "the best PMs won't just be roadmap managers. They'll be the ones who know how to use AI to test, build, and scale products faster than ever."

AI is not just reshaping what PMs do—it's changing how they work with engineers. In traditional product development, PMs spend a significant amount of time gathering feedback, defining user needs, writing product specs, working with engineers to design solutions, and iterating on features based on engineering constraints.

Thanks to AI, PMs can now prototype experiences themselves. By collecting early user feedback before engineering is involved and automating research and workflow insights, the manual work needed for user discovery is drastically reduced. This means engineers can focus on scalability and system design rather than guessing what users need.

When developing a LinkedIn Copilot tool, Portier experienced this shifting relationship firsthand. Her team built an AI prototype that could extract prospect account information from data sources like LinkedIn and turn that data into a structured format for sales reps. They then gathered early feedback from sales reps before engineers stepped in to scale the solution.

"The AI made it possible for the product management team to prototype the experience quickly," she recalls. "Engineers didn't have to waste time figuring out what the users wanted. Instead, they could focus on how to build it best."

The Future of AI-Driven Product Management

AI is no longer just a feature—it's reshaping how products are built and managed. As automation accelerates prototyping and decision-making, product managers must adapt to stay relevant.

It starts with better customer insights: "building AI is about understanding the user," Portier concludes. "AI only works well when it's built around real business problems, and that's where product management comes in."

With AI's ability to automate tasks formerly thought to be impossible or reserved for engineering, PMs can now spend their time better understanding user needs and ensuring that the solution they build solves them. And thanks to simpler prototyping and improved testing abilities, PMs can speed up development cycles and scale up solutions faster.

Chloe Portier sees product managers playing a crucial role in storytelling and alignment around AI. The PMs who use AI as a tool for discovery and execution—not just a feature—will be the ones that thrive.