Feature
Artificial intelligence (AI) appears poised to transform product development, offering tools to augment decision-making, streamline workflows and improve team collaboration.
Yet, AI adoption in the field remains limited. Only 10% of product managers actively use AI tools, according to a study by Ginux. The gap between AI’s promise and its current use in product management reflects barriers, such as limited awareness, accessibility and the specialized skills required to integrate AI.
Here, we explore how AI is being applied in several key areas of product development, including examples, challenges and opportunities shaping the field.
1. Analyzing Customer Feedback
Customer insights form the foundation of product management. AI enhances this process by helping product managers identify behavioral patterns, analyze feedback and synthesize qualitative data from user interviews. These capabilities save hours of manual work and uncover trends that might otherwise go unnoticed, allowing product managers (PMs) to focus on strategic priorities, like feature development and pain-point resolution.
Zilvinas Lesinskas, product director at Omnisend, an e-commerce email and SMS platform, highlighted how AI increases his team’s efficiency.
“We take automatic transcriptions from multiple interviews, feed them into GPT and ask it to highlight emerging topics, specific quotes and patterns,” Lesinskas said. “This helps us analyze problems more deeply and act on them faster.”
At Ucraft, a website builder, AI-driven analytics showed a usability issue with a feature that initially appeared underutilized, according to Founder Gev Balyan.
“We noticed users abandoning a feature we thought would be a big hit,” Balyan said. “AI revealed the issue wasn’t a lack of interest — it was a confusing interface element. After redesigning that part of the interface, usage went up 40% and retention improved.”
The ease of AI-powered analysis can lead to analysis paralysis or overemphasis on vanity metrics, which are common PM pitfalls. Product managers must focus on asking the right questions and aligning insights with business goals to avoid acting on irrelevant data.
2. Automating Product Documentation and QA Testing
Product managers balance high-level responsibilities with time-consuming tasks, like overseeing backlog, maintaining documentation and ensuring quality assurance (QA). These tasks, though critical, often compete with other, higher-incentivized priorities. AI tools can reduce this workload by automating documentation and accelerating QA processes.
QA automation sped up release cycles and improved product stability at Ucraft, Balyan said.
“AI tools can run through tests and detect bugs much faster than manual testing,” Baylan said. “This allows us to fix issues before they impact users, cutting down on development time and ensuring more stable updates.”
Similarly, generative AI tools are helping product managers overcome creative blocks. Product Manager Enan Hoque of Fluxon, a product development company, said AI tools, like GPT, help the business draft user stories, technical documents or UML sequence diagrams.
“This saves hundreds of hours per year and allows teams to focus on refining ideas rather than starting from scratch,” Hoque said.
Automation can uncover issues faster, but product managers still need to interpret results thoughtfully and maintain accountability for final decisions. Several PMs highlighted the importance of effective prompt engineering to avoid costly rewrites.
3. Conducting Market Research
Staying informed about market trends and competitor activity is a core responsibility for product managers. AI tools transform market research by providing dynamic competitive insights and predictive analytics, enabling PMs to track market changes and competitor strategies more effectively than traditional research methods.
Product managers said they’re using AI tools, like Perplexity and You.com, to track competitor updates and shifts in customer preferences.
Predictive analytics tools, such as Pecan, are also helping teams anticipate market changes and prioritize features based on future needs.
The abundance of real-time data can quickly become overwhelming, leading to decision fatigue or focusing on irrelevant signals. Product managers must ensure their AI tools are fine-tuned to prioritize relevant insights and align outputs with the organization’s broader strategy.
4. Communicating the Product
Effective communication is at the heart of product management, whether it’s drafting user interface (UI) copy, summarizing meetings or producing content for customers or stakeholders. Poor communication can lead to inefficiencies, with an Inc. study estimating up to eight hours of productivity lost per employee per week due to unclear messaging or misalignment.
Generative AI models, like GPT, are useful to draft initial versions of UI text or internal updates.
AI-powered voice tools, like Eleven Labs and Murf AI, also enhance accessibility by turning product walk-through scripts into clear and engaging voice-overs.
While AI accelerates content creation, product managers must ensure that the tone, clarity and brand voice remain consistent. Carefully reviewing final outputs can be essential to maintaining organizational standards and matching user expectations.
Related Article: Is Generative AI the Future of Product Content Generation?
5. Strategic Thinking and Collaboration
AI is increasingly being used as a tool for strategic thinking and team collaboration. By clustering feedback, uncovering hidden trends and simulating scenarios, AI supports product managers in making more informed, long-term decisions.
Scenario-planning models, particularly those designed with chain-of-thought reasoning are helping PMs explore multiple outcomes side by side. Vision and strategy work is likely to be one of the most profound opportunities of AI, according to angel investor and adviser Lenny Rachitsky.
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Additionally, AI can facilitate collaboration by transforming brainstorming sessions into actionable plans or visualizing data in ways that make insights clearer for stakeholders.
Nick McEvily, a fractional product leader, said AI is part of his process.
“AI takes information, clusters ideas and mashes up concepts,” McEvily said. “We then put those concepts into Miro boards for internal sharing and collaboration, turning raw brainstorming into actionable projects and experimentation.”
McEvily often generates multiple creative mash-ups to explore alternative scenarios and experiment with different approaches, a process that would be far more time-intensive without AI.
Bias in AI outputs is a critical concern, particularly when strategic decisions involve long-term consequences. Equally, AI input can act as a counterweight to individual or internal biases. Finding a balance between these two is essential for PMs to counter potential blind spots in decision making.
Adopting AI in Product Management
AI adoption is still low across various fields, including product management. Successful AI integration in product management requires addressing team resistance, providing adequate training and establishing clear strategic priorities.
For organizations where AI adoption is a top-down process, transparency and clarity of purpose are essential to overcoming skepticism as well as hands-on training to ensure teams could effectively use the tools.
Many product leaders noted they began experimenting with AI independently before introducing its benefits to their teams. Often, finding a single ah-ha moment, like revealing an unknown insight, was a catalyst for widespread adoption.
The challenge is balancing AI’s potential with thoughtful implementation. Product managers must foster a culture of accountability to ensure AI-driven decisions remain aligned with organizational goals and values.
Related Article: 10 Top AI Certifications in Product Management
