The AI Transformation Imperative, Part 1: Using AI to Enhance Your Product Leadership

How to use AI to build evidence-based product strategies, continuously discover and deliver great products, and build impactful product teams

In "The Narrowing Path: Navigating Product Leadership in 2025," I highlighted how AI is reshaping product leadership across various industries.

The AI transformation falls into two distinct dimensions:

1. Using AI to enhance your existing product practice: Tools and approaches that make you more effective regardless of whether you're building AI-powered products.

2. Building AI-native products and features: Strategies and skills for creating products where AI is central to the value proposition.

In this article, we will focus on the first dimension, which is relevant to every product leader: how to enhance your existing product practice with AI using the Product Science Principles. Our next article will address the second dimension of building AI-native products.

Note on Data Privacy and Enterprise AI Usage

Before diving into the specifics, I want to address an important consideration: Always check with your employer regarding their policies around usage of generative AI, LLMs, and other AI tools. One of the many reasons I use Claude.ai is that their default policy is not to train their models on information users have provided.

Many large enterprise organizations have developed acceptable use policies for AI and set up access for their employees to tools like Microsoft's Copilot. If you work at an organization that has not given employees permission to use AI, consider using this article to help you get a sense of how to make the case for why your organization will fall behind if you don't adopt some form of allowed AI usage.

Using AI to Implement the Product Science Principles

The Product Science Principles consist of three core principles that I've observed in every successful growth organization I've worked with:

1. Evidence-Based Product Strategy: Using data and insights to drive product decisions

2. Continuous Discovery and Delivery: Maintaining tight feedback loops with users

3. Empowered Teams: Creating an environment where teams can do their best work

Let's explore how AI can help you implement each of these principles more effectively.

AI for Evidence-Based Product Strategy

Evidence-based product strategy requires gathering and synthesizing information from multiple sources - market data, user research, competitive analysis, and business metrics. AI excels at helping product leaders process and extract insights from this information overload.

AI as Thought Partner and Writing Coach

As product leaders, we spend significant time developing strategies, communicating with stakeholders and teams, and talking to users and customers. The key to successful AI use in strategy development is using AI as a true collaborator rather than a replacement for our thinking.

My approach to working with generative AI as a thought partner goes like this:

1. Provide contextual foundation: I always begin by sharing existing materials - previous writing, transcripts from interviews, data sets, or other relevant context. This ensures the AI understands some of the depth and nuance of the subject matter.

2. Define clear communication goals: I explicitly state what kind of artifact we're creating and who the target audience is. For example, "We're creating a summary of recent customer insights for key stakeholders" or "We're drafting a product strategy document for executive leadership."

3. Use AI to deepen my thinking: Instead of jumping straight to drafting once the AI has the context ingested, I have the AI ask me probing questions about my ideas. This forces me to articulate my thoughts more clearly and often reveals gaps in my communication.

4. Collaborative drafting: Once I've thoroughly explored the concept, the AI creates an initial draft based on our dialogue. This draft incorporates my insights while providing structure and clarity. This is one of my favorite steps because it quickly gives me a strawman to respond to and improve upon.

5. Fact-checking: I review the draft critically, suggest edits, and have the AI implement changes. I look closely for any assertions or quotes that might not be backed up by the context I gave it. Several times, I've asked the AI where it got a statistic and received its apology for making it up.

6. Human feedback: Finally, I move the draft to collaborative tools like Google Docs where colleagues can provide additional feedback. I ask the readers to approach it the way I guide research participants - tell me what they are thinking, when they are confused, when they have questions, and when they see something insightful.

This approach has dramatically increased not only my productivity and the quality of my writing but also the depth of my thinking. The AI doesn't replace my expertise and insights - it amplifies them by serving as an intellectual sparring partner that challenges assumptions and helps clarify complex ideas.

Beyond documents, this collaborative approach works equally well for developing meeting agendas, creating frameworks for difficult conversations, or creating easily digested summaries for messages or social media.

AI for Continuous Discovery and Delivery

Continuous discovery requires regular interaction with users and quick iteration on ideas. AI tools are transforming how product leaders conduct discovery by making it faster and more accessible.

Developing and Refining Research Plans

Before diving into prototyping or user interviews, we need a solid research plan. This critical first step in discovery can benefit tremendously from the same collaborative approach with AI that we use for strategy development.

The process I guide my teams to follow for research planning typically involves:

1. Generating a list of knowledge gaps: What do I wish I knew about our users, their problems, or our potential solutions? This is where AI can help expand our thinking by suggesting additional angles we might not have considered.

2. Crafting a research plan: Based on those knowledge gaps, I first create a draft research plan outlining the methods and approaches I think will best help us get answers. Then I share this draft with AI, asking it to identify potential gaps, blind spots, or improvements. This review process often reveals assumptions I didn't realize I was making or alternative approaches I hadn't considered.

3. Developing research scripts and protocols: For qualitative research, creating effective discussion guides that avoid leading questions and prompt rich insights. AI can help refine question phrasing and suggest probes for deeper exploration.

By feeding existing data and contexts into generative AI, we can ask it to identify potential blind spots in our research approach. This collaborative approach doesn't replace the human judgment needed to design effective research, but it can help ensure our research plans are more comprehensive and less biased by our existing perspectives.

Supercharging Prototype Generation and Refinement

One element of product discovery that is changing quickly is the time and resources required to create interactive prototypes. AI is making it easier to bring ideas to life in ways that weren't as easily accessible before.

In my work, I've recently rebuilt my own business website using AI-assisted development through Replit. What amazed me was the speed at which I could go from concept to functioning prototype. This "vibe coding" approach - using conversational AI to generate code - dramatically accelerated the development process.

I've also seen this approach work effectively in educational settings. My students at NYU Stern use tools like Replit to create functional prototypes without needing deep technical expertise. This has transformed their ability to test product concepts with users, allowing them to see the gaps in their designs and rapidly gather more meaningful feedback.

Research Synthesis and Analysis

When it comes to qualitative research, AI tools can help with analyzing large amounts of feedback or sentiment and providing a thought partner to traditional research synthesis. When analyzing privately captured feedback or publicly shared sentiments, AI tools can help identify patterns and insights across large volumes of data without the time commitment previously required.

When we're doing product discovery to validate customer problems and solution ideas, especially in 0-1 settings, my team still finds that the act of analysis helps us deepen our understanding of the problem, our empathy with the user, and our ability to talk confidently about our findings. But just as in writing, using AI as a thought partner in the analysis can help us make sure we see as many angles as possible.

AI for Empowered Teams

Empowered teams need the space and support to do their best work. AI can help product leaders create this environment by handling routine tasks and providing infrastructure that enables teams to focus on high-value activities.

Automating Repetitive Tasks

Many product leaders are automating repetitive tasks with AI to free up more time for supporting their teams. In my own practice, I've used tools like Zapier to automate routine tasks such as:

• Generating summaries of external meetings that I can quickly review and share with my team on Slack

• Creating structured, centralized reports from disparate data sources without any coding

• Managing coordination across multiple calendars that need to respond to each other

These automations free up valuable time for authentic customer and team interaction - the aspects of product leadership that remain uniquely human.

Beyond simple automations, AI agents can now handle more complex workflows. For example, they can:

• Monitor key metrics and alert you to significant changes

• Do multi-step processes that require some analysis and decision-making

• Prepare preliminary data analyses for product reviews

I've tried playing with n8n and zapier agents to automate more complex tasks than I can do with zapier zaps alone. So far, I've found the setup to be more challenging than I'd hoped, but I'm going to keep working at it, because I believe there is huge value here.

Supporting Your Team to Adopt AI

While you might be excited about AI tools, successfully integrating them across your product team requires intentional support. Based on my experience, teams need four key elements to successfully adopt AI tools:

1. Clarity on policies and information security: Create clear guidelines on what types of information can and cannot be shared with AI tools. Document which AI tools are approved for use within your organization and establish protocols for reviewing AI-generated content before external sharing. This creates a safe framework within which your team can experiment without concerns about data security.

2. Access to purpose-built tools: Consider investing in premium versions of AI tools where it makes sense—the productivity gains often justify the cost. Look beyond general-purpose AI tools to identify domain-specific applications relevant to your product area. Some organizations are developing internal tools that connect company data with AI capabilities in a secure way, providing the benefits of AI while maintaining control over sensitive information.

3. Time for experimentation and learning: One of the biggest barriers to AI adoption is simply the time pressure teams face. Build dedicated time into work schedules for team members to explore and test AI tools. Acknowledge that there's a learning curve, and the first attempts may not yield immediate value. Creating space for experimentation signals that you value this exploration as an investment in future capabilities.

4. Supportive learning culture: Organize regular "show and tells" where team members can demonstrate effective AI use cases they've discovered. Create dedicated channels or employee groups focused on AI tool experimentation—think of it like a book club, but for testing AIs. Consider providing access to formal training opportunities; I've heard good things about AI product management courses on Maven.

Remember that your role as a product leader includes being a capability builder for your team. By thoughtfully supporting their AI adoption journey, you're helping future-proof your organization while making their day-to-day work more engaging and impactful.

Getting Started: Building Your AI Tool Stack

If you're just beginning to explore how AI can enhance your product practice, I recommend starting not with tools but with outcomes:

1. Identify what you wish you were getting done:

   • Look at your to-do list for items that consistently don't get completed

   • Consider routine tasks that you find yourself delaying or avoiding

   • For me, this was sending Slack updates to my team about business development calls

2. Identify available inputs and integrations:

   • What information sources do you already have that could feed an automation?

   • Check what APIs or integrations (like Zapier) can access this information

   • Consider what connections between tools would create the most value

3. Determine your desired level of oversight:

   • I prefer to keep a human in the loop for most automations

   • For my Slack updates, I built a Zapier flow that sends me a draft message first

   • I can review and edit before it goes to the team, ensuring quality and accuracy

4. Start small and iterate:

   • Build the first version of your automation and test it out

   • Use it consistently for a week or two before evaluating

   • Keep an "automation backlog" of ideas, just like you would with product features

5. Experiment with different tools:

   • Try sending the same prompt to different LLMs to compare responses

   • You may develop a preference for a particular AI that seems to understand your needs better

   • Consider the tradeoffs between specialized tools and general-purpose ones

The key is finding the sweet spot between what's impactful to automate and what's accessible for you to implement without excessive time investment.

Conclusion: The Human-AI Partnership

As product leaders, we're experiencing a fundamental shift in how we work. AI isn't replacing our strategic thinking or customer empathy - it's amplifying these uniquely human capabilities by handling routine tasks and expanding our creative capacity.

The most successful product leaders will be those who develop an effective human-AI partnership, leveraging technology to enhance their existing practice while maintaining focus on the core elements of great product leadership: understanding customer problems, envisioning compelling solutions, and aligning teams to deliver value.

In our next article, we'll explore the second dimension of the AI transformation imperative: building AI-native products and features. We'll dive into the strategic and organizational considerations required to successfully develop products where AI is central to the value proposition.

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I’ve got a couple of spots available to work directly with product leaders in my coaching program. If you find my content helpful, let’s hop on a call and see if my coaching is right for you.