Best Practices

3 ways AI will make product managers more efficient

Published Nov 8, 2023
AI can help product managers move quicker and make better, data-driven decisions.

Even before AI took the business world by storm, the product manager (PM) role was changing. What was once a role singularly focused on shipping features has evolved into a strategic, cross-functional role that’s critical to business growth. So how does AI fit into this evolution?

The goal of the modern product manager is to drive business outcomes. Ultimately, AI can enable PMs to achieve those outcomes faster and more effectively. AI tools can help product practitioners identify signals from noise in their data, provide insight into their product that was difficult—or impossible—to find on their own, and automate workflows to save them time. 

AI doesn’t mean the end of the product manager role—but it is going to change. In the end, AI frees up product managers to deliver more value to customers.

Here are three ways AI can make product managers more efficient in their roles:

1. Analyzing and summarizing data

A large part of a product manager’s job involves collecting, analyzing, and distilling quantitative data (e.g. product usage and user journeys) and qualitative data (e.g. customer feedback and NPS open-text responses). AI has a large role to play here with pattern recognition, which is a data analysis method that uses machine learning algorithms to automatically recognize patterns and trends in a data set.

Instead of spending time sifting through product analytics data and feedback submissions, product managers can lean on AI to analyze and summarize this data, and then dig into any themes they want to examine more closely. In addition to saving PMs time, this also means they can analyze and extract insights from larger data sets than ever before.

Although this is arguably one of the largest AI opportunities for product managers, a recent study commissioned by Pendo and Mind the Product found that while 66% of product leaders trust AI to summarize and analyze results from product data, only 15% are actually leveraging it today.

2. Creating documentation

While product managers collaborate with multiple teams across an organization, they partner closely with engineering and design teams to ensure their vision for the product comes to life—and that these teams have everything they need to do their jobs.

A large part of a PM’s job is pulling together various types of documentation for engineers and designers so they know what needs to be built, who it’s for, and what success looks like. This includes user stories, which communicate product or feature requirements from the perspective of user value; product requirements documents (PRDs), which outline what capabilities need to be included in a release; and acceptance criteria, which are the conditions a product or feature needs to fulfill so a user performing a given task will accept it.

AI can help automate the creation of these assets (plus persona descriptions, release notes, and more), allowing product managers to speed up work that can be very manual and time consuming in moments when they need to move quickly.

For example, rather than drafting user stories and acceptance criteria from scratch, product managers can submit short descriptions to a large language model (LLM) to generate the documents and then edit them from there. Similarly, product managers can input key information and have an AI tool automatically generate a PRD based on data it already has stored about the product and its users.

3. Automating key processes

Things move quickly in product management—so the more automation, the better. AI technology enables more automation, and can help PMs automate processes like A/B testing new features and feature tagging in a product analytics tool.

This is particularly useful when it comes to experimentation. For example, an AI tool can suggest what to test for multi-variant feature testing and even run tests in the product automatically. With AI, product managers can conduct more experiments than ever before and more easily learn what’s working so they can implement changes quickly.

Automating key processes also means product managers free up their time to focus on higher-impact initiatives, which will only make them stronger partners across the business.

Want to learn more about how AI will transform every stage of a product manager’s work? Read the e-book here.