Partnering with AI to Become a Better Product Leader

A few days ago, a friend who’s a lawyer asked me something I’ve been hearing a lot lately: “Is AI going to take my job?” It’s an honest question that many professionals are wrestling with. My response surprised him: “One day, it probably will.” But before he could process that, I added something more important: “Long before that happens though, a lawyer who collaborates with AI will put you out of business if you don’t do the same.”

This idea is at the core of how I think about AI in product management. AI isn’t here to replace product leaders, at least not yet, but those who embrace it as a partner will outpace those who don’t in the near future. AI offers us the opportunity to be faster, more innovative, and more effective by taking over time-consuming tasks like drafting documents, generating ideas, and helping to accelerate product delivery.

In this post, I’ll explore how to approach AI as a partner, learn how to effectively communicate with this new partner, extract value through iteration and ideation, and leverage AI to speed up delivery cycles.

AI as a Partner, Not a Replacement

Many people fear that AI will replace entire professions, but in reality, it’s far more valuable as a tool to augment human capabilities. For product leaders, AI can handle the tedious work, like writing the first draft of a product requirement document (PRD), analyzing feedback, or summarizing meetings. This gives us more room to focus on strategic tasks, such as roadmap planning, customer engagement, and problem-solving.

But remember: AI is only as good as the instructions you give it. A vague prompt like “write a PRD” will result in generic content that’s likely unusable. The more specific you are, the better the result. For example:

“Create a product requirement document for a cloud-based inventory management platform targeted at small businesses. Include sections for product overview, key features, two user personas, technical specifications, and a 200-word market analysis. Keep the tone professional, and ensure the document is 1,200 to 1,500 words.”

By giving AI clear structure, tone, and length, you can receive a first draft that’s ready for refinement—saving you hours of work.

Prompt Engineering: Pitfalls and Best Practices

To maximize the value of AI, you need to be precise in how you prompt it. Crafting clear, detailed instructions is the key to getting high-quality results. However, there are several common pitfalls you should avoid when working with AI.

Common Pitfalls to Avoid

1. Vagueness: Asking for something broad, like “write a feature list,” without specifying details will often lead to generic output. AI needs context and direction to produce useful content.

Best Practice: Be specific. Instead of saying, “Write a feature list,” try: “Generate five features for a collaboration tool aimed at remote tech startups. Focus on improving communication, task tracking, and file sharing, and explain how each feature solves common remote work challenges.”

2. Overloading with Too Much Information: Asking AI to handle too many complex tasks in one go can overwhelm it, leading to scattered or incomplete responses.

Best Practice: Break larger tasks into smaller, more manageable prompts. For example, instead of asking for a full PRD at once, ask AI to draft sections individually, such as user personas or a market analysis, and then compile them.

3. Lack of Context: Assuming AI understands your specific business or market without proper context can lead to irrelevant or misaligned responses.

Best Practice: Always provide background details around your role, your industry, your goals and specify your audience. For instance, instead of “Summarize our product,” say: “Write a 150-word product summary for a cloud-based inventory tool aimed at small retail businesses, focusing on features that help with real-time inventory tracking and automated reports.”

4. Overly Rigid Prompts: Being too strict about the format or tone can stifle the AI’s ability to provide nuanced or creative output. This is another area where an iterative approach is best. If you allow AI a degree of creativity in its initial pass you may find some value you otherwise would not have. And you can always ask AI to rewrite the prompt by saying things like, “make this more concise,” “expand this idea a bit more” or “make the structure like this instead”

Best Practice: Allow some flexibility within clear guidelines. For example, instead of demanding exactly 100 words, you might say: “Write a 100-150 word product overview for a cloud-based inventory platform. Highlight the ease of use and the real-time reporting features.”

By following these best practices and avoiding common pitfalls, you can significantly improve the quality of AI’s output and make it a more effective partner in your work.

Using AI for Iteration, Ideation, and Roleplaying

Once you’ve mastered prompt engineering, you can start using AI for more creative and iterative tasks, such as brainstorming ideas, roleplaying customer scenarios, and refining your work through multiple drafts.

Iterating for Continuous Improvement

As mentioned in the previous section, AI shines when used for iteration. Instead of expecting perfection in the first draft, treat AI’s initial output as a starting point. You can prompt AI to improve on its own work by giving specific feedback after the first pass. For example, after AI generates a PRD, you could say:

“Revise the technical specifications section to focus more on scalability and integration with third-party tools. Make sure to highlight how these features benefit businesses with limited IT resources.”

By continuously refining the AI’s output, you’ll reach a version that’s closer to what you need, much faster than if you were doing everything manually.

Ideating for Creative Solutions

Sometimes, staring at a blank page when you’re tasked with brainstorming new product features can be overwhelming. AI can help kickstart the creative process by suggesting ideas based on the criteria you provide.

For instance, if you’re developing a new feature for a collaboration tool, you could prompt:

“Generate three new feature ideas for a collaboration app designed for tech startups. Focus on improving real-time communication and reducing task management overhead. Provide a short explanation of how each feature addresses these issues.”

This approach helps spark creativity and allows you to build on the AI’s suggestions, creating better, more refined features faster. It can also be helpful to ask AI to provide you these ideas in a specific structure. If your goal is to deliver a mind map of your idea then let AI know this and it will breakdown its ideas in a way that will make the creation of a mind map easier.

Roleplaying for Customer Insights

AI can also help you simulate customer interactions, which is especially useful when you’re trying to understand customer pain points or refine your messaging. You can use AI to roleplay as different user personas, helping you gain insights into how potential customers might respond to your product.

For example:

“Pretend to be a small business owner struggling with inventory management. Describe your pain points in 200 words and explain what features you’d want from a new inventory management system.”

Roleplaying with AI can give you a clearer sense of your customers' challenges, which can inform your product decisions and marketing strategies.

Accelerating Delivery with AI

One of the most impactful ways to use AI is in accelerating product delivery. By automating repetitive tasks and improving decision-making processes, AI can help you stay on top of deadlines and keep projects moving forward.

Optimizing Sprint Planning

Tools like Jira Automation can analyze your past sprints to identify bottlenecks. For example, if your last sprint stalled due to delayed API integrations, Jira could flag that and recommend breaking those tasks into smaller, more manageable parts to avoid future delays.

Trello’s Butler AI can automate sprint task management, identifying recurring issues and suggesting resource adjustments. Similarly, Jira Align helps ensure your sprints are aligned with strategic goals, optimizing your workflow by keeping long-term objectives in focus.

Prioritizing Features Based on Data

AI tools like Productboard and Mixpanel analyze user feedback and behavior to help prioritize features. For instance, Productboard can highlight that 60% of users are struggling with file-sharing, suggesting it as a top feature to prioritize. Similarly, Mixpanel can analyze user interactions, showing which features drive engagement or conversions, like identifying that users who collaborate more are 40% more likely to convert to paid accounts.

Finally, Aha! Roadmaps can use predictive analytics to forecast how prioritized features, such as a recommendation engine, will impact metrics like user retention or revenue, helping you make more data-driven decisions.

Conclusion: Collaborating with AI for Product Leadership Excellence

As I told my friend at the start of this post, AI isn’t here to replace us—at least not yet. For product leaders, the real opportunity lies in collaborating with AI to become more efficient, innovative, and effective. By understanding how to avoid common pitfalls, mastering prompt engineering, and using AI for iteration, ideation, and accelerated delivery, you’ll be positioned to lead more successfully in an AI-enhanced future.

Will AI take over product management one day? Maybe. But for now, those who learn to collaborate with AI will be around long enough to help shape that future and that’s where I intend to be.