Intentional Leadership: Shaping How AI Serves Product Teams, Not Vice Versa
Recently, I had the pleasure of joining Michael and Evie on the Product Confidential Podcast to discuss how AI is transforming product management practices and what this means for product leaders. Below are my expanded thoughts on this rapidly evolving landscape.
Listen to the full conversation on the Product Confidential Podcast:
AI in Product Management: Here to Stay
When discussing AI in product management, it's important to clarify what we're talking about. While some product managers are building foundational AI products and others are implementing AI features into existing products, my focus is on the AI-based tooling that practically every product professional can and is using today.
The adoption statistics are striking. In a recent survey of 6,500 professionals conducted by Lenny's Podcast, ChatGPT was the most commonly used software with a staggering 90% usage rate—even surpassing Gmail and Slack. Similarly, in a small survey I ran on LinkedIn, over 50% of respondents indicated they use either product-specific AI tools or AI solutions integrated into their everyday software like Jira or airfocus.
The conclusion is inescapable: AI integration into product management workflows is not a passing trend—it's transforming how we work, whether we embrace it or not.
Balancing AI Enhancement with Human Qualities
As AI tools become more deeply embedded in our daily work, product leaders need to be intentional about preserving and emphasising distinctly human qualities. These AI tools can be thought of as "virtual interns"—smart and eager to help, but lacking experience and sometimes overconfident.
Here are three human qualities I believe product leaders should particularly emphasise:
1. Navigating Context and Building Trust
Product management operates within complex organisational ecosystems filled with informal dynamics and intricate human relationships. To be effective, we need to be "in the room where it happens" (to borrow from Hamilton). Product management is fundamentally a team sport that requires building alliances, creating alignment, and establishing trust—activities where human presence and interaction remain irreplaceable.
As one cautionary example from my experience, in a business meeting, a colleague came overprepared with materials that seemed suspiciously comprehensive. My neighbour whispered, "It's from Chatgpt," creating an atmosphere of distrust that undermined effective collaboration. When AI usage isn't transparent, it can erode the foundation of trust essential to teamwork.
2. Maintaining Curiosity and Humility
Good product management has always been about asking the right questions and genuinely listening to learn from the answers. This becomes even more critical when working with AI tools.
Think about working with a CEO who believes they know everything and is enamoured with their own ideas. This attitude wastes potential and causes frustration. Similarly, over-reliance on AI without maintaining our curiosity and challenging its outputs can lead to complacency and missed insights.
3. Developing Real Understanding and Product Sense
There's a profound difference between knowing and understanding. AI tools can know a vast amount of information, but understanding requires deeper engagement. As a leader, I need my product managers to truly understand what they're working on so I can trust their decisions, particularly when those decisions direct how we use our team's valuable time.
Building real understanding still requires "getting your hands dirty." Similarly, developing product sense that guides good decision-making requires direct experience and deep engagement with the material, which can't be outsourced to AI.
Practical Leadership in an AI-Enhanced World
How can product leaders effectively navigate this changing landscape? Here are some practical approaches:
Make AI Usage Transparent
Create a culture where people can openly acknowledge when they've used AI tools: "Here are some suggestions I got from [tool name], and here's my thinking about them." This transparency prevents the erosion of trust that occurs when people are unsure if they are interacting with a colleague's genuine thoughts or AI-generated content. By the way, Claude helped me write this blog post based on my original thoughts from the podcast.
Challenge More Rigorously
Leaders need to be more critical and challenging of their team's outputs, helping them avoid "falling asleep behind the wheel" or cognitive offloading. This isn't about blame but about creating a safe environment where you can say: "I expect you to be behind what you're saying. I expect you to be the voice of your product and the representative of your team."
Move from Individual to Team Enhancement
Currently, most AI usage in product management focuses on individual enhancement—each person working with their own "AI intern." The next frontier is developing approaches for fruitful AI usage across entire teams. This requires thoughtful consideration of where to establish standards and where to allow personalisation.
Preserving Customer Empathy in an AI-Mediated World
One legitimate concern is how product managers can maintain deep customer empathy when more interactions become AI-mediated. Here are some approaches:
Seek rich experiences over summaries: Don't just rely on AI-generated summaries of customer feedback. Have real customer conversations or at least immerse yourself in the original material—particularly video clips that show users expressing their thoughts and emotions.
Complement AI insights with human interaction: There's still no substitute for direct human interaction. Even with perfect AI analysis tools, spending time with actual users creates a different quality of understanding.
Use AI as an interview trainer: AI can help you practice asking better questions and following up effectively in user interviews, helping you get to the underlying "why" rather than just capturing feature requests.
As Michael pointed out during our podcast conversation, there is also tremendous value in engaging deeply with research materials. When you're personally analysing user research or combing through a report, connections form that lead to those "lightbulb moments" that might never emerge if you simply ask AI to summarise everything.
The Future Balance
Looking ahead, I see two distinct trajectories developing and one big open question:
Different Speeds of Adoption
In larger established organisations, AI integration will likely be slowed by organisational inertia and the complex nature of changing established systems. In contrast, smaller "AI-native" startups forming right now can integrate AI much more deeply into their workflows from the beginning. It will be fascinating to see how these different approaches develop and scale.
Human Interaction as a Premium Experience
As we interact more with AI, I believe we'll come to value genuine human interactions even more, much like how we appreciated in-person connections after the pandemic's isolation. Even as AI responses become increasingly human-like, there remains something uniquely valuable about real human connection.
Product sense for future generations of product people?
I believe that successful product decisions require a healthy mix of insights and product sense. Product sense also allows seasoned product people challenge the output of AI. For product people of my generation our product sense will typically be the result of many years of “rich experiences” (aka: a lot of leg work) of product work. With AI the temptation to skip the tedious parts of the job is very strong. This makes me wonder how emerging product people will develop product sense - and how we as product leaders can help them. If you have any thoughts on this I’m REALLY curious to hear them!
Conclusion
The rise of AI tools in product management presents tremendous opportunities to enhance our work and focus more on what matters. However, realising this potential requires thoughtful leadership that emphasises what makes us human while leveraging what machines do best.
We need to be conscious about what tasks we keep for ourselves, what we delegate (explicitly via prompting or implicitly via agentic solutions), and what we entirely automate. This isn't about resisting change but about steering it thoughtfully toward outcomes that truly enhance both our products and our work as product professionals.
While I see more potential than harm in these developments, autopilot is not an option. As individuals and leaders, we need to actively navigate this transformation to ensure it elevates our work rather than diminishes it.
What has been your experience with AI tools in product management? I'd love to hear your thoughts in the comments below.
I’m currently working on a conference talk with the working title “Beyond Autopilot: Human-led Product Leadership in the AI age” - in case you are a conference organiser and curious about this topic: Let’s talk! :O)