The Top Use Cases of AI for Product Managers (PMs)

AI is reshaping product management by automating repetitive tasks, analyzing complex data, and improving decision-making. For product managers, this means more time for solving problems and delivering better results. Here’s what AI can help with:

• Data-Driven Decisions: AI simplifies messy data (like emails or call recordings), highlights trends, and even suggests actions based on customer feedback and system performance.
• Improved Operations: Automates routine tasks like status updates, report generation, and document revisions, saving hours of work.
• Customer Insights: Quickly processes feedback from surveys, tickets, and social media to identify patterns, pain points, and opportunities.
• Knowledge Sharing: Turns scattered team notes, chats, and meeting recordings into organized, searchable knowledge hubs.
• Planning and Prioritization: Helps prioritize features and roadmap decisions using historical data, predictive analysis, and impact forecasting.

AI tools integrate with platforms like Jira, Slack, and Salesforce, making them easy to adopt without overhauling workflows. The result? Faster decisions, clearer insights, and better collaboration across teams.

How Product Managers Can Use AI in Their Daily Work | Dan Corbin

1. Using AI for Data-Driven Decisions

Product managers today are bombarded with data from every direction. Customer feedback pours in through support tickets, user interviews produce hours of recordings, and team discussions unfold across various platforms. Sifting through all this manually? Practically impossible. That’s where AI steps in, effortlessly managing this data overload and reshaping how product managers approach their work.

Making Sense of Unstructured Data

AI’s real strength lies in handling unstructured data - the kind of information that doesn’t fit neatly into rows and columns. Think customer emails, social media posts, call recordings, or even internal team chats. These messy data sources hold valuable insights, but extracting them manually is a daunting task.

AI can process a wide variety of unstructured inputs, like PDFs, reports, internal wikis, social media posts, and even audio files. It identifies themes, tracks sentiment, and uncovers trends in just minutes. Beyond that, AI can combine this with technical data - such as server logs or IoT sensor outputs - giving product managers a comprehensive view of user experiences and system performance.

Turning Data Into Actionable Insights

AI doesn’t just analyze data; it turns it into actionable recommendations. Instead of just presenting charts and numbers, AI can highlight shifts in customer satisfaction, pinpoint problematic features, identify which customer segments are affected, and even prioritize fixes or updates based on potential revenue impact.

What’s more, AI can uncover patterns that might otherwise go unnoticed. For instance, it might reveal that customers reporting specific issues in support tickets are at a higher risk of churning - or that certain feature requests from sales calls align with trends in product usage data. These insights make it easier to prioritize the right actions at the right time.

Seamless Integration with Everyday Tools

Modern AI tools are designed to fit right into the platforms product managers already use, like Jira, Salesforce, Slack, or Zoom. They can automatically update project statuses, share summarized insights with stakeholders, and populate product requirement documents with relevant customer feedback. This seamless integration ensures that insights are part of the workflow, not trapped in isolated reports.

Saving Time and Boosting Efficiency

One of AI’s biggest advantages is the time it saves. Tasks that used to take days of manual effort can now be completed in minutes. Instead of spending hours combing through data, product managers can access key insights almost instantly. This efficiency frees up time for strategic thinking and creative problem-solving - shifting the focus from organizing data to interpreting it and making smarter decisions.

Over time, AI gets even better. As it learns from the decisions you make and the feedback you provide, it becomes increasingly skilled at surfacing the most relevant insights for your specific needs and priorities.

2. AI for Product Operations

AI is revolutionizing product operations by tackling the complex web of tasks that product managers juggle daily. From managing roadmaps and planning releases to tracking features and maintaining documentation, these responsibilities often eat into time that could be spent on strategic work. By automating routine tasks and streamlining workflows, AI is reshaping how product teams operate, making day-to-day processes more efficient and freeing up valuable time for bigger-picture initiatives.

Impact on Efficiency and Time Savings

Product management often involves time-intensive tasks like updating stakeholders, preparing status reports, and coordinating across teams. These repetitive activities can take up a significant chunk of a manager's week. Enter AI, which automates these processes and slashes the time spent on them.

Take stakeholder updates, for example. Instead of manually pulling data from various tools and sources, AI can gather, analyze, and compile this information into detailed reports within minutes. What once took hours can now be completed almost instantly, leaving product managers with more time to dive into strategic efforts like refining user experiences or sharpening competitive strategies.

AI also plays a critical role in keeping documentation up to date. It can automatically revise product requirement documents (PRDs) based on team discussions, track changes across versions, and ensure everyone has access to the latest information. This eliminates the frustrations of outdated files and keeps teams aligned.

Making Sense of Unstructured Data

AI doesn’t just save time - it changes how unstructured data is managed and used. Product operations generate vast amounts of scattered data, from Slack messages and meeting recordings to emails and process logs. Sifting through this manually for insights is time-consuming and often impractical.

AI can process these disparate data streams, uncovering patterns and identifying recurring issues that might otherwise go unnoticed. For instance, it can analyze team communications to pinpoint bottlenecks or inefficiencies, allowing managers to address problems before they grow.

Additionally, AI can transform technical documentation, user manuals, and internal wikis into searchable knowledge bases. Instead of combing through countless files for a specific detail, product teams can simply query an AI system to find quick answers about product specs, past decisions, or implementation details.

Seamless Integration with Existing Tools

One of AI’s greatest strengths is its ability to work within tools that teams already rely on, such as Jira for project tracking, Slack for communication, or Zoom for virtual meetings. This means teams don’t have to overhaul their systems to benefit from AI.

For example, if a critical issue is raised during a meeting, AI can instantly update project statuses in Jira, create action items based on the discussion, and sync these updates across platforms. This kind of integration ensures that operational improvements are made without disrupting workflows teams are already comfortable with.

Turning Data into Actionable Insights

AI doesn’t just organize data - it provides actionable insights that help product managers make better decisions. By analyzing data from multiple sources, AI can identify frequent support issues, flag process delays, and suggest changes that could improve overall efficiency.

These insights allow product managers to optimize workflows proactively. AI can also help with resource allocation by assessing team workloads, spotting skill gaps, and forecasting future project needs. This forward-thinking capability helps teams plan better and avoid potential roadblocks, giving managers the clarity they need to focus on impactful, strategic decisions.

3. AI for Customer Insights and Action

Understanding customers is essential for successful product management, but the sheer volume of data can feel overwhelming. Product managers are constantly sifting through support tickets, user interviews, surveys, sales calls, and social media mentions. AI steps in to bring order to this chaos, transforming scattered customer feedback into clear insights that drive smarter product decisions.

Impact on Efficiency and Time Savings

Traditional customer research takes time - lots of it. Manually analyzing hundreds of support tickets, reviewing interview transcripts, or sorting through survey responses can drag on for weeks. AI changes the game, condensing months of work into just a few hours.

With AI, tasks like transcribing calls, identifying recurring themes, and generating concise reports happen in minutes. Beyond analysis, AI helps create customer personas from behavioral data, maps user journeys based on support interactions, and delivers executive summaries that simplify complex findings. This level of efficiency frees product managers to focus on what matters most: acting on insights rather than getting bogged down in data collection.

Handling Unstructured Data

Customer feedback doesn’t come neatly packaged. It’s scattered across emails, chat logs, call transcripts, and social media posts. This unstructured data holds valuable insights, but traditional methods often fall short in making sense of it.

AI excels at processing diverse data streams, detecting sentiment, extracting key themes, and uncovering frustrations hidden in tone, word choice, or context. It captures nuances that might otherwise slip through the cracks, helping product managers see the bigger picture and address underlying customer concerns.

Seamless Integration with Existing Tools

AI works effortlessly with tools you already use - platforms like Salesforce, Zendesk, and Slack. It auto-updates records, generates action items, and synchronizes insights across workflows, ensuring customer feedback flows directly into product development without manual effort.

It doesn’t stop there. AI can populate research repositories with new findings, update customer feedback databases, and sync insights with roadmap planning tools. This keeps customer voices front and center throughout the product development process.

Turning Insights into Action

The real value of AI lies in its ability to turn raw data into actionable recommendations. It doesn’t just tell you what customers are saying - it helps you decide what to do about it.

AI identifies patterns across customer segments, prioritizing feature requests based on potential impact or highlighting the most pressing pain points. It can even predict how changes might affect different user groups or flag emerging trends before they become widespread issues. This gives product teams a competitive edge in addressing customer needs.

By connecting data points from various sources, AI uncovers insights like how specific grievances might correlate with churn risk or how certain feature requests tie to revenue growth. These insights aren’t just informational - they’re strategic, helping product managers prioritize actions and align customer needs with business goals.

With AI, teams gain not only a deeper understanding of their customers but also the tools to collaborate and plan effectively, ensuring that every decision is backed by meaningful, data-driven insights.

4. AI for Team Knowledge Sharing

AI doesn't just enhance product operations and customer insights - it also revolutionizes how teams share and access knowledge. In product management, where collaboration is key, keeping everyone aligned becomes more difficult as teams grow. Important insights often end up scattered across emails, chats, and meeting notes, making it hard for new team members to catch up quickly. AI steps in by turning this scattered information into a centralized, searchable knowledge hub that ensures everyone stays on the same page.

Impact on Efficiency and Time Savings

Traditional knowledge sharing can be tedious. Think about the time spent drafting meeting notes, updating status reports, or answering repetitive questions. Product managers often find themselves bogged down by these tasks, while team members waste hours hunting for information in email threads or scheduling extra meetings just to get context.

AI smooths out these inefficiencies by automatically capturing and organizing team knowledge. Instead of explaining why a feature decision was made, managers can direct new team members to AI-generated documentation that includes the original discussion, the reasoning behind the decision, and supporting data.

Need to revisit a project from six months ago? AI can instantly pull up relevant discussions, decisions, and context. This means product managers can focus on strategic initiatives rather than answering routine questions, saving time and boosting overall productivity.

Processing and Organizing Unstructured Data

Team knowledge is rarely neat and tidy - it’s often buried in call recordings, Slack messages, emails, and even casual conversations. While these unstructured sources hold valuable insights about product decisions and customer feedback, they’re notoriously hard to search and reference.

AI changes the game by processing this unstructured data. It extracts key decisions, action items, and insights from sources like meeting recordings or Slack threads, linking them to historical context for ongoing projects. For example, AI can show how a specific decision evolved over time and what factors influenced it. This is especially useful for product teams working on complex features that span multiple sprints or quarters, as it tracks requirement changes and highlights lessons from previous projects.

Integration with Existing Tools and Workflows

Platforms like BuildBetter seamlessly integrate with tools teams already use - such as Zoom, Microsoft Teams, Slack, and Jira - making knowledge sharing effortless. For instance, when a product manager joins a customer call, the platform can automatically record, transcribe, and summarize the conversation. It then shares actionable insights with the team via Slack, updates project documentation in Notion, and creates action items in Jira - all without disrupting the team’s workflow.

Additionally, the platform respects existing permission settings, ensuring sensitive information stays secure while making general knowledge accessible to the right people.

Support for Actionable Insights and Decision-Making

AI doesn’t just store information - it connects the dots. It links current challenges with past insights, enabling teams to make better, faster decisions.

For example, when planning a new feature, AI can retrieve similar past projects, highlighting what worked, what didn’t, and why. It can surface patterns in customer feedback that align with current priorities or remind teams of earlier discussions about engineering constraints. This ability to bridge past and present insights ensures continuity, especially during team transitions, by providing detailed context on ongoing projects, past decisions, and team dynamics. The result? Faster onboarding, improved productivity, and smarter, data-driven decisions across the board.

5. AI for Planning and Prioritization

After improving decision-making, operations, and customer insights, the next step in AI-driven product management is aligning planning and prioritization. This stage often feels like juggling multiple complex factors. Product managers must weigh user impact, business value, development effort, and strategic alignment - all while trying to predict customer needs months ahead. Traditional methods rely heavily on gut instinct and basic scoring models. AI, however, transforms this uncertainty into precise, data-backed decision-making, enabling a more cohesive, data-driven approach to planning.

Impact on Efficiency and Time Savings

Prioritization can be a massive time drain. Product managers frequently spend countless hours sorting through spreadsheets and attending stakeholder meetings. Take Sarah, for example - a PM at a fintech startup. She used AI to analyze over 10,000 customer support tickets in just a few hours, a task that would have taken weeks manually. This analysis uncovered key insights, leading to the development of a new feature that reduced customer churn by 15%. AI-powered tools streamline this process by leveraging historical data, trends, and predictive models, freeing up time for PMs to focus on strategic initiatives and collaboration.

Processing and Analyzing Unstructured Data

One of AI’s standout capabilities is its ability to unify and analyze diverse feedback sources, such as emails, customer support tickets, and research notes, into a single, actionable dataset. Traditional methods often fall short when it comes to integrating such varied inputs effectively. AI excels by extracting meaningful patterns and insights from unstructured data, uncovering opportunities that might otherwise go unnoticed.

Seamless Integration with Existing Tools

AI’s impact is amplified when integrated into current workflows. Modern AI prioritization tools work effortlessly with platforms teams already rely on. For instance, BuildBetter connects with tools like Jira for project tracking, Slack for communication, Salesforce for customer data, and Zendesk for support insights. Imagine planning a quarterly roadmap: AI can pull user feedback from Zendesk, feature requests from Salesforce, analytics from usage tools, and discussions from Slack. It then synthesizes this data into prioritized recommendations, feeding directly into project management systems for immediate action.

Actionable Insights for Better Decision-Making

AI doesn’t just rank features - it provides the reasoning behind its recommendations, enabling more informed decisions and smoother stakeholder communication. For example, Mike, a product leader at an e-commerce company, used AI to analyze A/B tests. The AI identified a subtle UX adjustment that boosted conversion rates by 7%. It also flagged a user segment that responded negatively to the change, allowing for tailored experiences that ultimately led to a 12% overall increase in conversions. By offering clear, data-driven explanations, AI equips product managers to anticipate market trends and drive smarter product development.

The result? Prioritization becomes faster, more strategic, and backed by comprehensive data. Product managers can make decisions with confidence, aligning stakeholders and delivering impactful outcomes.

Traditional vs AI-Powered Methods

The transition from traditional to AI-powered product management marks a major shift in how product managers handle their responsibilities. Traditional methods rely heavily on manual processes and intuition, whereas AI introduces speed and data-driven precision. This evolution highlights why AI is becoming a cornerstone in modern product management.

Take workflow automation, for instance. Traditional automation operates on fixed rules - software robots excel at repetitive tasks with predictable patterns. However, these systems falter when faced with dynamic or complex tasks. They often require manual reprogramming for new patterns, involve substantial investments in hardware and software, and depend on static rules with ongoing manual quality checks.

AI-powered workflow automation, on the other hand, is a game-changer. Machine learning allows AI to adapt to changes, process intricate data, and improve continuously without human intervention. This capability significantly enhances how product managers handle their workflows.

The benefits are clear. AI enables less experienced staff to work 35% faster, and organizations adopting AI report cost savings ranging from 20% to 28%. Furthermore, AI's presence in the workplace is growing, with 20% to 40% of employees already using it in some capacity. What was once experimental has now become essential.

Traditional methods often involve manually sifting through user feedback, which can overlook subtle but important patterns. In contrast, AI-powered tools like BuildBetter analyze unstructured data quickly, spotting trends and actionable insights that would likely go unnoticed with manual methods.

Planning also sees a dramatic upgrade with AI. Traditional planning tends to be slow and labor-intensive, while AI leverages historical data, user behavior, and market trends to generate prioritized recommendations based on thorough analysis.

Moreover, traditional tools often function in isolation, requiring manual coordination. AI-powered systems integrate seamlessly with platforms like Jira, Slack, Salesforce, and Zendesk, creating a unified view of data that would be challenging to achieve manually.

This shift liberates product managers from time-consuming data collection, allowing them to focus on strategic decision-making. The result is a proactive, rather than reactive, approach to managing products.

Conclusion

AI is transforming the way product management operates by speeding up data-driven decisions, uncovering valuable customer insights from unstructured feedback, and bringing together scattered team knowledge. The use cases we've explored highlight how AI can enhance every aspect of product management. By automating time-consuming manual tasks, product managers can redirect their energy toward creating outstanding products.

With tools like BuildBetter, product managers can analyze both internal and external data at the same time. These tools integrate effortlessly with platforms like Zoom, Slack, Jira, and Salesforce, cutting down on the need for manual coordination and saving precious time.

These advancements mark a turning point in how product management is practiced. AI is no longer optional - it’s becoming a core part of the process. The real question is: will you start using these tools now? Embrace AI solutions today and move from merely reacting to problems to building proactive, data-driven strategies.

FAQs

How can AI help product managers make better decisions about prioritizing features and planning roadmaps?

AI gives product managers the tools to make more informed decisions by processing and analyzing massive amounts of data, including customer feedback, user behavior patterns, and market trends. This approach ensures that product roadmaps are aligned with both customer expectations and business objectives, focusing on features that deliver the most value.

By cutting down on guesswork, AI simplifies the prioritization process through actionable insights and predictive analytics. It helps product managers allocate resources wisely, concentrate on key priorities, and maintain a competitive edge in the market.

How does integrating AI with tools like Jira, Slack, and Salesforce benefit product managers?

Integrating AI with platforms like Jira, Slack, and Salesforce can transform how product managers handle their day-to-day responsibilities. By automating repetitive tasks - like updating project statuses, assigning tasks, or generating reports - AI reduces the need for manual input. This frees up valuable time, allowing teams to concentrate on high-level strategic goals.

Beyond automation, AI delivers real-time insights by analyzing data across these tools. It helps product managers spot trends, anticipate outcomes, and make quicker, data-driven decisions. On top of that, AI enhances team collaboration by providing smart recommendations and streamlining communication, ensuring everyone stays on the same page and works efficiently.

How can AI help product managers gain deeper insights from unstructured customer data?

AI empowers product managers to tap into unstructured customer data - like reviews, emails, and social media posts - using natural language processing (NLP). With NLP, they can analyze sentiments, spot key themes, and detect recurring patterns, giving them a clearer picture of customer needs and preferences.

On top of that, generative AI takes this a step further by turning complex, unstructured data into organized, actionable insights. It can reveal hidden trends, forecast customer behavior, and offer recommendations. This helps product managers make smarter, more strategic decisions while cutting down on time and effort.

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