Feature
Artificial intelligence (AI) is overhauling the tools and strategies product teams use in their work. With applications spanning operational efficiency, decision-making and customer experience, AI offers product teams opportunities to refine their processes and better meet user demands. But alongside these benefits come challenges: AI adoption requires skill development, organizational commitment and clear goals.
For today’s product leaders, the question is no longer if AI should be used — it’s why and how.
Internally, AI reduces inefficiencies, manages complexity and supports platform evolution. Externally, it improves customer engagement, delivers actionable insights and drives measurable value. These aren’t hypothetical benefits; they reflect how AI in product management is actively shaping strategies across industries.
AI Eliminates Busywork and Increases Productivity
Product leaders often find themselves bogged down by essential yet lower-value tasks such as document creation, status tracking and scheduling, reducing the time for strategic product development. AI tools automate these repetitive tasks, giving teams more time to think creatively, solve problems and innovate.
This automation creates space for exploring ideas that drive long-term value. A study from The Adecco Group, surveying 35,000 workers across 27 countries, found that modern AI tools save an average of one hour per day across industries. For product leaders, this time savings provides more bandwidth for strategic initiatives.
Examples in Practice
Robin Patra, head of data at ARCO Construction Company, described how automation improves their efficiency:
“What excites me most about AI today is its ability to bridge the gap between vision and execution. Tools like LLMs and embeddings democratize expertise, allowing us to scale intelligent decision-making across the organization.”
Kelwin Fernandes, CEO of NILG.AI, explained how AI supports complex problem-solving:
“AI becomes the obvious choice when we’re tackling complex, uncertain challenges. It helps us scale solutions and explore opportunities traditional tools can’t match.”
For smaller-scale tasks, AI can still deliver meaningful improvements. CEO Michael Namju Kim of Sungwha Co Ltd added:
“We use AI to generate meeting minutes automatically. It’s a small change, but it saves time and ensures better participation because no one dreads the admin work. These small productivity boosts add up to larger organizational gains."
Strategic Insight
By automating routine tasks, AI allows product teams to shift their focus to activities that add real value. Leaders should identify operational bottlenecks where automation can have the highest impact and implement solutions that free up time without over-complicating workflows.
Related Article: 10 Top AI Certifications in Product Management
AI Analytics Powers Smarter Decisions
Product leaders need reliable, actionable insights to make better decisions in less time. Traditional analytics tools often fall short, requiring manual effort to interpret data or identify trends. AI analytics tools, powered by machine learning (ML), uncover patterns, predict outcomes and make insights available faster and at a greater scale.
With AI analytics increasingly viewed as table stakes, platforms without these capabilities risk falling behind. Increasing venture capital investment in data analytics ($20.7B deployed in the first half of 2024, according to Pitchbook) reflects its importance, as leaders prioritize tools that help teams and customers make smarter decisions.
Examples in Practice
According to Namju Kim, AI-powered analytics helps the team align with shifting customer preferences:
“We use AI to analyze trends like sock patterns and color preferences. This helps us align our designs with what customers want — even uncovering trends we wouldn’t have thought to investigate ourselves.”
Sheetal Jaitly, CEO of TribalScale, highlighted AI’s ability to deliver novel insights:
“Tools that offer insights that are unique and draw upon cross sections that wouldn't be typically considered will create the most customer value and delight. It's all about what can we do now that we couldn't do before, not how can we do the same thing differently.”
Strategic Insight
AI analytics is only as valuable as the systems and processes supporting it. Product leaders must prioritize clean, well-structured data while aligning AI tools with their teams’ decision-making workflows. Success depends on integrating analytics into the organizational flow of product planning — not just relying on raw outputs.
AI Makes Customer Interactions Faster and Smarter
Today’s customers expect platforms to offer seamless, personalized assistance that anticipates their needs. Integrating AI into platforms for product teams isn’t just about meeting user expectations — it’s about maintaining relevance in a market where competitors are embedding machine learning and large language models (LLMs) to deliver more intuitive, predictive experiences.
AI tools like generative chatbots or contextual assistants provide scalability that traditional customer support structures often cannot match. According to Gartner, only 14% of customer product issues are resolved through self-service today.
At the same time, integrating AI into a platform offers an opportunity to modernize its core capabilities — building the foundation for products that better compete with AI-first alternatives.
Examples in Practice
Jaitly described how customer expectations have evolved in their work:
“People expect AI-driven tools to deliver outcomes seamlessly, avoiding traditional interfaces. Simplicity now equates to sophistication.”
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This push for simplicity has led product leaders to adopt features like GitHub’s Copilot, where LLMs integrate into workflows to guide users step-by-step, or Intercom’s Claude-powered chatbot, which claims it resolves 86% of customer self-service queries.
Strategic Insight
For AI to succeed in personalizing customer experiences, it must deliver results that feel effortless and contextual to users. However, poorly implemented AI tools create frustration, eroding trust instead of improving usability. Product leaders should treat personalization tools as both immediate upgrades to customer satisfaction and long-term investments in creating AI-first platforms capable of competing in the future.
AI Connects Product Teams and Workflows
AI isn’t limited to improving single processes — it has the potential to deliver benefits across multiple teams and applications. Product leaders are leveraging AI to bridge silos, streamline collaboration and create systems where AI agents can act across functions autonomously.
Cross-functional AI adoption drives stronger results because it leverages diverse perspectives and skill sets. According to Deloitte, these initiatives are more likely to yield high-impact outcomes, as improvements in one area often ripple into others. However, achieving these impacts depends on a product leader’s ability to align AI projects with the broader organizational context.
Examples in Practice
Patra described the unexpected benefits of his company’s AI-powered safety systems:
“When we implemented AI-powered safety cameras, our goal was to improve construction site safety. What we didn’t expect was a 30% reduction in our insurance costs — from $5 million per month to $3.5 million. That alone demonstrated how AI can deliver benefits beyond the initial objective.”
Fernandes underscored the importance of focusing on people and processes — not just technology:
“Most AI projects fail because too much attention is placed on technical aspects like data, models and infrastructure. Instead, leaders should approach AI from a process and people perspective. How will the AI integrate into the business? How will the organization need to adapt? Who are the affected stakeholders, and how can they be involved in the process?”
Strategic Insight
AI initiatives that span multiple teams create compounded benefits. Leaders must think beyond isolated technical tools and instead focus on the human and organizational aspects of AI adoption. By aligning AI projects with business processes, involving key stakeholders early and fostering a culture of collaboration, product teams can establish a foundation for long-term success.
Related Article: AI-First Strategy: The Risks, Rewards and Realities of Going All In
AI Unlocks New Ideas and Creative Solutions
AI doesn’t just make processes faster — it changes how teams work and think. By reducing bottlenecks and automating routine tasks, AI creates space for teams to focus on solving high-impact challenges. For product leaders, this means more time for exploration, experimentation and creative problem-solving — essential elements for driving innovation and disrupting traditional business models.
AI-first modules and systems also open new possibilities, enabling teams to build products that weren’t possible with traditional approaches. For example, Google's "20% time" rule for engineers, a practice that spawned major innovations, is a model for how time freed by AI tools can empower creativity across industries.
Examples in Practice
Fernandes highlighted how AI fosters collaboration by simplifying workflows:
“AI doesn’t just help us solve complex problems — it simplifies workflows and ensures everyone has access to insights they can act on. That’s key to scaling innovation.”
Patra described AI’s transformative potential:
“AI is not just a tool for efficiency — it’s a transformative partner helping us rethink what’s possible in our operations.”
Strategic Insight
AI’s ability to accelerate collaboration and remove barriers to experimentation makes it a foundational tool for long-term innovation. Product leaders should prioritize systems that democratize access to insights and encourage experimentation. This approach supports disruptive innovation by empowering teams to challenge traditional models and deliver breakthrough solutions.
