Artificial intelligence is revolutionizing software development, playing a central role in the construction, tests and launch of new software products and drastically changing the role of product managers (PMS) in the process.
As AI is more deeply integrated into the development process, PM are no longer only route managers or links between businesses and engineering; They are now AI strategists, experiments and rapid problem solving.
The data scientist turned into the producers manager Chloé Portier understands this change better than the majority, witnessing first hand how AI has gone from being a product characteristic to a central enabling or product development itself. With AI now accelerating the creation of prototypes, automobile decision making and the remodeling of the dynamics of the equipment, the PM must evolve their sets of skills, processes and collaboration models to remain at the forefront.
Who is Chloé Portier?
Portier is currently PM senior in Madkudu, who specializes in the construction of prospecting products enabled for AI for sales and income equipment. He has two master’s degrees in AI and data science, and has built his career at the intersection of AI technology and commercial strategy.
“I realized that I not only wanted data analysis; I wanted to build the products that made the AI useful,” says Portier. “In Madkudu, close the gap between data science and the product, turning complex models into tools that help income equipment to make better decisions faster.”
She has already had a significant impact on Madkudu’s customers, with a AI decision model that worked on increasing the conversion rates of the sales pipe by 200%, while a LinkedIn co -pilot that can scrape prospect data enabled more.
Beyond his work in Madkudu, Portier regularly reviews research and presentations for re -related conferences. It is also Coanfrerion of AI Paper Bites, a podcast that translates the research of AI in business language, with its background and work in both data science and product management, helping to convert academic tops into ideas of Applice.
More artificial intelligence
From data science to product management: artificial intelligence as a tool, not just a product
At first, AI was largely considered a striking product that really did not solve tangible commercial problems. That has changed in recent years, when companies had to change the approach to the construction of AI itself to the real construction of some with AI.
As AI becomes a central part of product development, PM no longer need to wait for their engineering equipment to build early prototypes. Instead, they can quickly try the ideas through early proof of concept models that require little or no engineering work. In turn, early ideas of the thesis can lead to better -informed products decisions before the commitment of important engineering resources
“The problems that were insoluble or request a large engineering investment two years ago can now be addressed in days,” says Portier. “That has opened opportunities to solve complex challenges of user experience and automatic workflows possible before.”
AI in action: Reduction of product development friction
A key example of AI that changes the role of products managers lies in a long -standing pain point in the profession: decision bottlenecks. Portier discovered that AI can solve these bottlenecks by reducing the time spent to manual processes, prioritization tasks and alignment between commercial functions.
This was the case when she and her team build a new ticket prioritization system for AI clients. Madkudu received delays in addressing customer’s critical questions and concerns due to ongoing debates about which tickets were more urgent.
The team built an AI agent that could analyze incoming tickets in real time, using data points such as the client’s profile, the feeling in the ticket, the activity of the historical account and the largest tendencies in the tickets to optimize the decision -making process.
“We get rid of the long debates and the inconsistent prioritization to a process in which everyone, from customer service to product management and engineering, had a more objective vision of what matters most. The Model of AI prioritization dealt with steam our decision -making and the allowed decision making and allowing to do and allow and allow to take and allow and allow better the fastest improvements.”
The role of AI in the configuration of the collaboration between products and engineers managers
According to Portier, AI is fundamentally changing the role of product managers: “The best pms won are road map managers. They will be those who know how to use AI to try, build and climb products faster than ever.”
The AI is not only remodeling what the PM do, but is changing how they work with engineers. In the traditional development of products, the PMs pass a significant amount of time collecting comments, defining the needs of users, writing product specifications, working with engineers to design solutions and altering the characteristics based on engineering councils.
Thanks to AI, PM can now prototype experiences. By collecting early comments from users before engineering participates and automating research and knowledge of work flow, the manual labor necessary for the discovery of users is drastically reduced. This means that engineers can focus on scalability and system design instead of guessing what users need.
When developing a LinkedIn co -driver tool, Portier experienced this changing first -hand relationship. His team builds an AI prototype that could extract information from the prospect account from data sources such as LinkedIn and turn that data into a structured format for sales representatives. Then they gathered early comments from the sales representatives before engineers intervened to climb the solution.
“The AI made it possible for the product management team to grow the experience quickly,” he recalls. “The engineers did not have to waste time to discover what users wanted. On the other hand, they could concentrate on how to build it better.”
The future of product -promoted product management
The AI is no longer just a characteristic, it is remodeling how products are built and managed. As automation accelerates the creation of prototypes and decision making, product managers must adapt to remain relevant.
Start with better customer information: “Building AI is about understanding the user,” concludes Portier. “IA only works well when it is built around real commercial problems, and that is where it enters product management.”
With AI’s ability to automatic tasks that were previously thought to be impossible or reserved for engineering, PM can now spend their time better user needs and ensure that the solution they build solves them.
And thanks to the creation of simpler prototypes and improved test capabilities, PMS can accelerate development cycles and expand solutions faster.
Chloe Porter sees the managers of products who play a crucial role in the storytelling and alignment around AI. The PMs that use AI as a tool for discovery and execution, not only one characteristic will be the ones that prosper.
