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The AI Divide in Product Management

Why some product managers are pulling ahead while others fall behind

A fascinating new study on AI's impact in scientific research has caught my attention, and it should catch yours too. Scientists using AI tools for materials discovery showed dramatically different results: top performers nearly doubled their output, while the bottom third saw minimal gains. While scientific research isn't product management, these findings might offer a glimpse into our future.



The Emerging Pattern and Its Implications

Here's what's got me thinking: AI isn't creating an equal playing field – it appears to be amplifying existing differences in capability and adaptability. The study showed that AI automated 57% of "idea generation" tasks, but success hinged entirely on the scientists' ability to evaluate AI suggestions effectively.


For product managers, this raises some provocative questions. As AI tools become more prevalent in our work, we'll likely see similar patterns emerge. What's fascinating about the study is that success wasn't about technical AI expertise – it was about the ability to evaluate and prioritize AI-generated suggestions. This is where "product sense" becomes crucial. Just as there's no substitute for putting in the work in traditional product management, there's no shortcut to developing strong evaluation skills for AI outputs.


This isn't about becoming an AI expert - it's about developing a systematic approach to incorporating AI tools into your product management practice while building the judgement to use them effectively.

Making AI Work For You

The good news is that developing strong AI evaluation skills isn't a mysterious process. Start by using AI tools daily in your work - not just occasionally or for simple tasks. Try using AI to analyze customer feedback, draft product requirements, optimize stakeholder communications, or evaluate market opportunities. But here's the key: for each output, develop the habit of asking "How does this align with what I know about our users? Our market? Our strategic goals?"


Just as product sense comes from immersing yourself in customer understanding and market dynamics, AI evaluation skills come from regular, thoughtful engagement with these tools. The goal isn't to rely on AI for answers, but to develop the judgment to know when and how to best leverage its capabilities. This isn't about becoming an AI expert – it's about developing a systematic approach to incorporating AI tools into your product management practice while building the judgment to use them effectively.


The implications are clear: a digital divide is emerging between those who can effectively leverage AI and those who can't.

Looking Ahead

The implications are clear: a digital divide is emerging between those who can effectively leverage AI and those who can't. As we move into 2025, taking an intentional approach to AI learning and integration isn't just important – it's crucial for survival. Yet many product managers find themselves paralyzed, not by external barriers, but by what Richard Rumelt calls "entropy and inertia." In his book "Good Strategy/Bad Strategy," Rumelt argues that an organization's greatest challenge often isn't external threats or opportunities, but rather the effects of entropy and inertia. The same applies to our careers as product leaders. Our own internal resistance to change, combined with the daily chaos of our work, can make developing AI skills feel overwhelming. But this is precisely why an intentional, strategic approach is crucial.


The study raises as many questions as it answers, but one thing seems clear: AI isn't going away, and under the right conditions, it can dramatically increase productivity. The question isn't whether to engage with AI, but how to overcome our own inertia to do so effectively.

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