AI is Changing How Product Managers Learn, For the Better

6 min readDec 12, 2024

Imagine teaching grade-schoolers simple arithmetic before calculators, or teaching them spelling and grammar before smart text editors. In a similar fashion, AI is changing how product managers learn best practices, and it will all be for the better when you see the results.

Common Scenarios — Before & After AI

Here are a few common product management scenarios that exemplify the stark difference between learning best practices before and with AI.

Market Analysis Before and With AI

Customers are clamoring for a number of improvements to your product and you’re on a mission to get them funded and on the roadmap. However, just because customers want them doesn’t mean they’re delivering the level of differentiating value your organization needs to meet its own goals. Executive stakeholders want things that drive growth.

So you’re asked to do a market analysis to make sure stakeholders are confident in your plan. If you’ve never done a market analysis, you probably don’t know what a good one looks like. After a thorough Google search, you view some videos, get a few templates and you at least have a starting point.

But there’s still a ton of legwork to do to fill in the blanks and make sure all the dots connect. Then there’s the presentation and communication of your market analysis to stakeholders. Is it bulletproof?

Now bring AI into the picture, same scenario.

With very few specifics on your instructions (prompts), AI can tell you exactly what a strong market analysis looks like for your products and/or markets. Not only that, but it will also create it for you in a matter of minutes. It’ll probably take you longer to verify the information and the sources than it took to complete the market analysis.

Now all you have to do is ask AI to format the information into something that’s easily consumable for your stakeholder audience and do it in the proper tone, for example, formal corporate speak, or a more conversational voice.

In a matter of a few hours (or less) versus weeks or months, you have a stronger market analysis than you’d ever have if you’d done it yourself, and it’ll be packaged in the best way possible to communicate with your audience.

But here’s why we still need product managers. You, the product manager, have to sell it. AI has put you in the best position to do that in a fraction of the time it would take you otherwise.

You want to be a strategic product manager? This is where it starts.

Product Prioritization Before and With AI

Let’s assume stakeholders are on board with your assessment of the market. Let’s also assume you never get all the funding you ask for. Now it’s time to establish priorities for all your proposed product investments and quantify their value.

This is no less than a nightmare because you’re forced into a situation where you have to be everything to everyone — customers, sales, tech support, client services, customer success, executives, etc. Everything is a #1 priority for someone.

The squeakiest wheels and the best politicians usually come out on top! Not exactly a great approach to establishing strategic priorities.

Let’s bring AI into this scenario where it’s going to be even more valuable.

Using your market analysis as the foundation, AI already knows what’s most important to your customers, why, the competitive landscape and the size of your market opportunity. All you need to do now is give AI a little direction in terms of your corporate goals and it’ll return a suggested prioritization that simultaneously optimizes value relative to the customer’s goals and yours.

Once again, AI can format the artifacts for your audience, and once again, we still need product managers, not just to sell it, but to weave in variables AI knows nothing about and probably never will.

AI has no idea which sales deals are hinging on your roadmap. AI has no idea if key customers are likely to defect because of product deficiencies. AI doesn’t know if anything on your proposed roadmap will open up new markets, etc. AI may never know these things.

Product managers, AI isn’t going to change what you’re ultimately responsible for, but it sure is going to change how you get there, and it’s all for the better because it can do certain things better and faster than you ever could the old-fashioned way. Always verify the information AI gives you!

The Impact on Our Training Courses

When I think about the above scenarios and a whole slew of others, I can’t help but think how AI will significantly change the way we facilitate our training courses. Our clients will reap the rewards because the learning and adoption curve just got seriously flattened, not that it was that steep to begin with.

To set the stage, the following graphic illustrates the way we’ve facilitated our training courses since day one.

Our Training Courses Before AI

The goal from inception was to make sure our clients walked away knowing exactly how to apply the best practices of our framework to their specific products, markets, business model and the culture of their organization such that they knew what good looked like for their unique circumstances.

To that end, every client went through a series of real-world repetitions for each best practice. During the readouts for each breakout team, we’d make sure they were applying our best practices in a way most beneficial to their individual situation, completely personalized.

The typical assignment at the end of the course is to complete the artifacts we started in the classroom that already have personalized content.

Our Training Courses With AI

The biggest difference will be the breakout sessions, the hands-on exercises and associated artifacts.

Instead of just doing a few repetitions for each best practice due to time constraints, the teams will use AI (if permitted by their organization) to complete the artifacts in full, right there in the classroom.

In other words, our students will leave a typical two-day course with a completed market analysis, a proposed value-based roadmap with suggested priorities, job-based backlogs that quantify the value of improving user jobs, outcome-based personas, quantified enhancement requests, etc.

The homework assignment changes from completing the artifacts we started in the classroom, to simply tweaking and verifying the information. Then our clients can start using them to direct their products from more of a leadership position versus reactionary. In other words, it’ll be a much shorter adoption curve at an organizational level because the legwork to get there is a fraction of what it used to be.

Positioning Yourself to Direct vs. Manage Your Products

The best thing about AI is the productivity gain that helps product managers do critical, time-consuming parts of their job way faster and way better than they could otherwise, even if they had the time.

AI is finally going to put product managers in the position of strength they’ve long coveted. They no longer have to be bogged down with the legwork they either never have time to do or just don’t like to do to get there.

AI will have product managers playing offense a whole lot more than defense, and it’s going to inject the fun factor back into the job at a much higher level.

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