🤔 Dear Lewis, AI is reshaping product management. How do I help my team adapt?
AI is transforming product management, but many teams struggle to use it effectively. In today’s edition, a VP of Product moves beyond the buzzwords to unlock real productivity gains.
Here we are again, my friends, back for another installment where I dive into the leadership challenges that keep executives up at night.
TL;DR: Five Ways AI Transforms Product Management
Task-Specific Prompting: Match AI tools to specific PM activities
Visual Acceleration: Generate concepts and mockups in minutes
Decision Frameworks: Structure better choices, don't automate them
Insight Mining: Extract patterns from mountains of user feedback
Expertise Amplification: Enhance human judgment, don't replace it
Today, I'm sharing the story of Ralph (not his real name), a VP of Product who came to me with what he called "AI anxiety" – not the existential kind, but the practical kind where everyone's talking about AI transforming product management, and he's not sure if his team is falling behind.
"I've got seven product managers drowning in JIRA tickets and stakeholder politics," Ralph told me. "Meanwhile, I keep reading about AI revolutionizing product work, but I can't figure out how to get from buzzwords to actual productivity."
"Let me guess," I said. "You've tried telling your team to 'leverage AI' in their workflows?"
Ralph winced. "Worse. I bought everyone Claude and ChatGPT subscriptions and told them to 'figure it out.' That was three months ago. As far as I can tell, they're using it to write emails slightly faster."
Here's what I told Ralph: When it comes to AI adoption in product teams, throwing subscriptions at people is like handing someone keys to a Formula 1 car when they've only ever driven automatic sedans. They might figure out how to make it move, but they'll never unlock its potential without a deliberate approach.
"Ralph," I said, "you're focused on the wrong problem. You're thinking about AI as a standalone initiative when you should be thinking about it as a force multiplier for the work your team already does."
That's when we developed a framework for AI in product management. But first, let me share a breakthrough that showed us we were on the right track.
I suggested Ralph run an experiment: the next product review meeting would be done entirely with AI-generated materials, with just 30 minutes of prep time allowed.
"That's insane," he protested. "Those decks take hours to build."
"Exactly," I smiled.
The meeting that followed was messy but revelatory. One PM used Claude to synthesize user feedback into themes. Another had Vercel V0 generate wireframe designs for a feature they were considering. A third used GPT to draft user stories from requirements.
None of it was perfect. Some was laughably bad. But afterward, Ralph's normally reserved team couldn't stop talking about what they'd try next time.
"I've never seen them this energized about process," Ralph told me, looking dazed.
"That's because you've turned AI from a vague directive into a practical challenge," I explained.
Let me break down the framework we developed:
1. Task-Specific Prompting
"I started thinking like a chef with different knives," Ralph said. "Different tools for different ingredients."
Ralph's team created a "prompt library" organized by PM activity – specific prompts for user story writing, competitive analysis, and prioritization frameworks. They identified their three most time-consuming documentation tasks and built AI workflows for each.
During one planning session, a PM pulled up Claude and typed: "Based on the last three sprint retrospectives I've shared, what patterns of technical debt should we prioritize?" Within seconds, they had a synthesized view that would have taken hours to compile manually.
2. Visual Acceleration
Product managers don't struggle with ideas – they struggle with visualization and validation.
Ralph's team started using Vercel V0 and Claude Artifacts to transform text descriptions into interactive mockups in minutes:
"Show me three different approaches to a dashboard that highlights user engagement metrics."
For every product concept, they now generate multiple variations before committing to a direction. They've started creating visual concepts for early stakeholder alignment before involving design.
"It was like watching a traffic jam suddenly clear," Ralph said. "Ideas that used to take days to visualize were being explored in hours. We're not replacing designers – we're having much better conversations with them because we've already eliminated the obviously wrong directions."
3. Decision Frameworks
Instead of using AI to make decisions, Ralph's team started using it to structure decision-making processes.
Before major decisions, they prompt AI to identify hidden assumptions:
"Based on this product strategy, what implicit assumptions are we making about user behavior, technology trends, and competitor actions?"
For key strategic questions, they use AI to expand the solution space, generating multiple approaches to problems. They even use AI to model potential outcomes across different scenarios.
"It's like having a product strategy team on call 24/7," Ralph explained. "We're making more nuanced decisions because we're considering angles we would have overlooked in our rush to build."
4. Insight Mining
Ralph's team was drowning in qualitative data – support tickets, user interviews, sales call notes – but struggling to extract insights quickly enough.
They built a systematic approach to pattern detection, feeding all customer feedback through AI:
"Analyze these 200 support tickets and identify the top 5 pain points by frequency and emotional intensity."
The breakthrough came during what we now call "The Hidden Feature Incident." Ralph's team had been debating a particular feature for months. On a whim, one PM fed six months of customer interviews, support tickets, and sales calls into Claude with the prompt: "Find evidence of users asking for this specific functionality, either directly or indirectly."
What came back stunned them. Users were indeed asking for the capability, but using completely different terminology than the product team. They had been missing the signals because they were looking for the wrong words.
"We had been talking past our users for months," Ralph told me. "That single prompt probably saved us from building the wrong solution."
5. Expertise Amplification
The final piece focused on ensuring that AI augmented the team's expertise rather than replacing their critical thinking.
Ralph's team started using AI to pressure-test their thinking:
"Here's the conclusion we've reached. What are the three strongest arguments against this approach?"
They built feedback loops to continuously improve their AI use – weekly sharing of effective prompts, documentation of where AI recommendations failed, and regular refinement of their context libraries.
Six months in, the change was remarkable. During an executive review, Ralph's team presented a comprehensive product strategy developed in half the time their previous efforts had taken.
"What's different this time?" the CEO asked.
Ralph's most junior PM answered: "We spent less time pushing pixels and more time thinking. AI handles the first draft; we focus on the insightful revisions."
"Two quarters ago," Ralph told me in our final session, "I was worried AI would make product managers obsolete. Now I see it's making mediocre product management obsolete while elevating the truly strategic work."
The lesson is clear: AI isn't replacing product management – it's redefining what's possible when you free your team from the drudgery that has historically consumed their days.
Product teams that intentionally apply AI to their workflow aren't just keeping pace with technology. They're rediscovering why they fell in love with product management in the first place: the joy of solving meaningful problems without getting bogged down in the mechanics.
Keep striving for greatness,
Lewis C. Lin
Simple, right? Well, not always
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