How Product Managers Are (and Aren't) Using AI 🤖

The American Journalism Project’s Product & AI Studio is seeking a Senior Technical Product Manager to be the primary day-to-day owner of our work with portfolio organizations to build and scale AI-powered tools.

This is a rare opportunity to shape AI products from the ground up, and have direct influence on how nonprofit newsrooms in our portfolio approach product development and applications of technology. You’ll have direct access to users, clear mission alignment, and the autonomy to make real product decisions in service of rebuilding sustainable local news across the country.

📍Remote in U.S.

💰$136,341 – $149,975

Apply here 🔗

Product Managers are responsible for a product’s vision and success from the idea stage to launch and beyond. They use research and data to identify opportunities, define priorities, and turn insights into strategy, design, and execution. Their work consists of collaboration, organization, and a lot of communication.

Since so much of the role involves processing information, coordinating people, and making decisions, AI is becoming a very useful tool for them.

Ways AI helps Product Managers save time:

• Turning product reviews, stakeholder meetings, customer interviews, and sprint planning sessions into concise summaries that highlight key decisions, action items, blockers, and areas requiring follow-up.

• Summarizing research findings, long email/message threads, feedback from customers, test users, etc.

• Scanning large volumes of product analytics and synthesizing important insights from clicks, drop-offs, user behavior, usage patterns, etc.

• Quickly turning large product requirements documents, meeting notes, and strategic plans into detailed user stories, checklists of what a feature needs before it's considered complete, Jira tickets, and development tasks.

• Generating or refining emails for customers, stakeholders, and cross-functional teams to communicate product updates, gather feedback, share decisions, and coordinate next steps.

• Creating project status reports by analyzing information from tools like Jira, meeting transcripts, Slack, and product roadmaps to summarize progress, blockers, risks, and next action items.

Ways AI helps Product Managers discover more:

• Reviewing and summarizing customer interviews, surveys, support tickets, app reviews, sales calls, and social media feedback to identify recurring complaints, feature requests, unmet needs, and areas where customers are struggling.

• Analyzing product data to understand how people actually use a product, including which features they engage with most, where they abandon workflows, how often they return, and which actions are linked to long-term retention.

• Identifying patterns and trends across thousands of customer interactions and data points that would be difficult to spot manually, helping uncover emerging opportunities, potential risks, and changes in customer behavior.

• Grouping customers based on behaviors, preferences, usage habits, and engagement levels to better understand how different types of users experience the product and where their needs differ.

• Monitoring competitors to track new feature releases, pricing changes, product updates, positioning shifts, and broader market trends that may impact product strategy.

• Generating ideas for new features, product improvements, experiments, and areas for further investigation by combining customer feedback, product analytics, competitor research, and market trends.

Ways AI helps Product Managers do things they normally couldn’t:

• Ask questions about product data in plain English and get answers instantly without needing to know SQL or rely on a data team to build reports.

• Analyze thousands of pieces of customer feedback at once, making it possible to understand the experiences and opinions of far more customers than a person could realistically review manually.

• Quickly generate dozens of potential feature ideas, experiments, and solutions for a problem, allowing teams to explore more possibilities before deciding what to build.

• Continuously monitor competitors, market changes, customer sentiment, and product performance at a scale that would be impossible for a single product manager to track manually.

• Simulate the potential impact of product decisions using historical data, helping teams evaluate trade-offs and outcomes before committing resources.

• Act as an on-demand research assistant that can instantly summarize information, answer questions, compare sources, and surface relevant context across thousands of documents.

Ways Product Managers still rely on humans, not AI:

• Deciding which problems are worth solving and which opportunities are worth pursuing.

• Making trade-offs between customer needs, business goals, technical constraints, budgets, and timelines.

• Building relationships and trust with customers, stakeholders, executives, and cross-functional teams.

• Leading discussions, resolving disagreements, and getting teams aligned around a shared direction.

• Understanding the emotions, motivations, and context behind customer feedback that may not be obvious from data alone.

• Making judgment calls when data is incomplete, conflicting, or doesn’t tell the full story.

• Communicating a product vision and inspiring teams to execute on it.

• Taking responsibility for product decisions and their outcomes.