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Hello fellow humans!

Here’s a little secret about product management: there’s no such thing as product management. There are skills and tools that product managers use to do various jobs that we tend to call product because we don’t always know what to call such a strange collection of jobs. A product manager does data analysis, but not so much as to be called an analyst. They do prioritization, but that’s the result of negotiation, market research, and strategy. PMs write user stories, but they’re someone else’s stories, and we just report on it. PMs solve problems, but usually don’t build solutions. The main skill that a PM can offer is collecting, organizing, and structuring information in a way that it becomes meaningful for people who build solutions. The same structured thinking that makes PMs effective with AI — defining clear problems, setting success criteria, iterating based on feedback — are valuable skills for any professional role.

Today, I have two use cases for how core PM skills translate across different functions, with real-world scenarios showing how non-PMs can use AI as part of their own personal product team.

It can be helpful to step out of our own shoes for a minute and live in the world of whoever our customer or downstream user is so that we can explore their role and gain insight into why they do what they do. This is entirely a human domain; It will be a long time before AI will be able to examine a human role like this, but we can direct an AI to help us. Context is the secret sauce here; if we can provide the AI with all the critical context information, then it can be a useful ally. But that requires you to get that context and think carefully about which parts matter.

The Scenario

Imagine a Customer Success Manager needs to create onboarding materials for a new enterprise client in the healthcare industry.

Without PM Thinking

"AI, create an onboarding checklist for our new customer."

With PM Thinking

The CSM structures it like a user story with details about the environment and user context:

"As a healthcare IT administrator at a 500-person hospital who just purchased our data analytics platform, I need an onboarding checklist that:

Result

AI produces a comprehensive, healthcare-specific onboarding plan with 23 tasks sequenced across 6 weeks, compliance checkpoints, and stakeholder assignments. The CSM validates it against similar customer onboardings and customizes 3 tasks. Time saved: 3 hours. The client completes onboarding in 5 weeks versus the typical 9-week timeline because nothing was missed or done out of sequence.

The AI may or may not get it right the first time, but use your skill, intelligence, and human senses to have a conversation with the AI. Prompt it to ask you clarifying questions. Ask it if adding some detail makes a difference. Be willing to revise your thinking and your prompt. And that may help the Customer Success Manager to have a deeper and smarter conversation the IT Administrator that ultimately leads to a better solution.

Resources

Problem-solving might be one of the most important, and yet most generic jobs that we have to do. Being good at problem-solving is a really valuable skill, but problems almost never come up in a way that it’s clear what the problem is. So when we hit roadblocks or challenges, we usually have to break them down into smaller parts to figure out how to approach them. We call this process decomposition. It’s helpful if you have a deep understanding of the work domain and the product or service that your organization provides. The AI may not have that context, so the more you can provide, the better.

The Scenario

A Marketing Manager needs to launch a thought leadership campaign but feels overwhelmed by the scope.

Without Product Thinking

"AI, create a thought leadership campaign plan" → Gets a generic, overwhelming 50-page document.

With Product Thinking

Break it into sequenced sub-problems.

Session 1: "What are the 5 topics our target audience (CIOs at mid-market companies) is most actively searching for and discussing related to digital transformation? Analyze: search trends, LinkedIn discussion volume, competitor content performance. Provide reference links."

Session 2: "For the #1 topic identified (cloud migration strategy), what are the 3 most common mistakes or misconceptions based on: industry research, analyst reports, customer conversations we've documented?"

Session 3: "For each misconception, draft a contrarian but defensible thesis our CEO could credibly argue. Include: the conventional wisdom, why it's wrong, our alternative perspective, one data point that supports it."

Session 4: "For the strongest thesis, create a multi-format content plan: LinkedIn post (under 200 words), blog article (1200 words), webinar outline (45 minutes), and downloadable guide (one-pager)."

Session 5: "Create a 90-day distribution plan: which channels, what cadence, what CTAs, what metrics define success."

Result

Notice how each session gets gradually more specific, detailed, and narrower in scope. the campaign cannot possibly cover every pain point; but by breaking it down into smaller, more manageable pieces. Each session produces more focused part of the overall problem. If you can make sure each piece is well-defined, the final campaign will be more coherent. You can then have the your leadership review focused key points and objectives rather than an entire campaign plan. A tighter focus accelerates your process.