Exactly 10 years ago I published my original lecture at The Wharton School entitled The Art of Product Management. This video has garnered over half a million views on YouTube and still grows to this day.

On the 10th anniversary of the video, it felt like the right time to completely remake it for the age of AI.

Watch on: YouTube

In 2016, I defined the role of the product manager as driving the vision, strategy, design, and execution of their product. I think that definitions holds as true today as it did back then.

But interestingly, in the intervening 10 years, the role started to move away from the true craft of product management into a role dominated by coordination. As we created specialized roles to scale the product organization, the PM ended up paying the high tax of keeping all these team members coordinated and aligned.

But now, thanks to AI, which can significantly speed up and take over our coordination efforts, we can return to the original craft of product management. And frankly return to what made product management fun in the first place.

In the video, I share my vision for how we can move away from all of the coordination and execution tasks that have come to dominate the product role and replace them with the far more strategic and customer oriented tasks that product management should have always been about.

I also revisit each of the four dimensions of product management and share exactly how AI is making us faster, smarter, and giving us super powers we never had before.

• For vision, I cover how AI is making it easier than ever to visualize your product vision through a vision prototype.

• For strategy, I cover how you can now leverage AI to deeply research your market and critique your product strategy.

• For design, I share how to use AI for synthesizing customer surveys & interviews, conducting AI moderated interviews, analyzing metrics, and producing AI prototypes.

• For execution, I detail how to leverage AI to increase your productivity around communication, whether it’s managing meetings, generating status updates, beautifying slides, or writing execution communication.

But in addition to sharing where AI shines, I call out throughout the four dimensions where human taste remains absolutely critical in the role of product management.

I absolutely believe we are entering the golden age of product thanks to AI and I hope this video inspires you to feel the same and provides an overview of how to get there.

Watch now: YouTube.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

The role of product management is undoubtedly evolving faster than ever thanks to AI. When I talk to PM’s about this rapid change, though, their reactions have generally fallen into two distinct camps.

For me, and others like me, it feels like the golden age of product: I’m able to accomplish far more than ever, far faster than ever, and without the traditional bottleneck of waiting on cross-functional partners to get things done.

But for others, the rapid change in the role has been overwhelming. With a busy day job, many have found it hard to find the time to learn how to best apply AI to their PM role and where they can get the biggest bang for their buck.

This guide is a practical map for those in the second camp seeking the promised land. I’m going to walk through all the product work PMs are responsible for and showcase where AI is creating the most leverage for me today.

I’m not here to overhype AI though. I’ll share where I’m getting real value that’s worth spending your time on and at the same time, highlight where AI fails to deliver quality output and human effort remains critical.

So let’s dive in…

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The Deliverables of Product Managers

I’ve always defined the role of the product manager as driving the vision, strategy, design, and execution of their product:

• Vision is about articulating how the world will be a better place if your product succeeds

• Strategy is about defining how your product will win in the market it plays in

• Design is all about crafting a useful, usable, and delightful product experience

• Execution encompasses all the work necessary to bring your product to market

While this high-level view of the product role is helpful, I find breaking down each dimension further into the individual deliverables product managers are responsible for makes it even more actionable.

These ten deliverables represent the majority of product work that PMs spend their time on:

Vision

1. Product vision - A vision narrative that articulates how the world will be a better place if your product succeeds

Strategy

2. Product strategy - A detailed product strategy map that defines how your product will win in the market it plays in

Design

3. Customer insights - The results of your customer research: actionable customer insights that inform your product strategy, roadmap, specs, and designs

4. Data insights - The results of your analytical research: data insights that equally inform your product direction

5. Product roadmaps - A detailed timeline of features, initiatives, or releases that the team intends to deliver to achieve it’s objectives

6. Product specs - Detailed requirements that inform the design & engineering of a specific feature set

7. Prototypes - Interactive designs for a particular feature set

Execution

8. Execution tasks - A broad bucket for all the tasks required to get a product over the finish line

9. Product decisions - The many micro and macro decisions that you need to make along the way to build the product

10. Team OKRs - The monthly, quarterly, or annual goals that you set for the team to drive focus, alignment, and accountability

Now that we have a holistic sense of the product deliverables PMs spend their time on, let’s now go through each in detail to understand where AI can provide you the most leverage today.

Vision

When product managers do their job well, they not only focus on the short-term roadmap, but also paint a broad product vision for where they are ultimately headed. Doing so not only raises the ambition of the team but also helps to guide a multi-year product evolution.

If you want to understand what truly ambitious product visions look like, here are my favorites:

• PayPal - Speech to Employees, 1999 - Peter Thiel

• Tesla - The Secret Tesla Motors Master Plan, 2006 - Elon Musk

• Lyft - The Third Transportation Revolution, 2016 - John Zimmer

• Blue Origin - Going to Space to Benefit Earth, 2019 [Video] - Jeff Bezos

• 6 more exemplary product visions

When ideating a product vision concept, so far I’ve found AI to be minimally helpful. Because the very goal is to articulate a future that does not yet exist, AI often struggles to come up with truly ambitious ideas that actually map to your specific product realm. I’ve either ended up with fairly incremental feature suggestions or pure science fiction that doesn’t map to reality.

Now, where I am getting plenty of leverage out of AI is when I want to bring my vision to life as a visual vision walkthrough. Before I used to have to get dedicated design time from my product designer to put together vision concept mockups that painted the picture as a connected series of mocks of where we were going with our product vision.

Now with AI, I can put together this same vision walkthrough myself as an interactive AI prototype. We’ll talk more about AI prototyping when it comes to design deliverables, but AI prototyping enables me to leverage simple prompts to create a high fidelity fully interactive click-through prototype with gestures, animations, and full functionality showcasing my vision for the future product direction.

I’ve found these vision walkthroughs to be far more compelling and inspiring then written narratives alone and now I’m able to put these together, thanks to AI prototyping, without the need for securing additional resources.

My favorite example of an AI powered vision walkthrough comes from the Duolingo team. Most people know Duolingo as a fantastic language learning app, but what most people probably don’t know is that they also recently launched a chess learning app.

The origin story of this new app is fascinating: An enterprising product manager and designer at Duolingo believed the company should get into chess, but as you can imagine, it was a controversial idea. What does chess have to do with language learning?

Rather than writing a spec or a product strategy document, they built a vision prototype using Cursor. They demonstrated that Duolingo’s core design concepts of puzzles and spaced repetition could be equally applied to learning chess. When the exec team saw the vision prototype, they had the same aha moment: this does look and feels like a Duolingo product and it leverages our unique secret sauce. They also compared it to the top chess learning apps in the App Store and quickly realized this experience felt entirely different.

The executive team quickly greenlit the initiative after experiencing the AI powered vision walkthrough. I believe they would have never achieved that level of executive alignment as quickly without it.

Strategy

Developing a compelling product strategy is critical for a product manager as it puts your product on the path to winning in the market it plays in. Without one, it’s too easy to end up with a feature factory, diligently developing and releasing features yet failing to actually meet your business objectives.

Before we can discuss how we can leverage AI to develop a product strategy, it’s important to have an understanding of what a compelling product strategy actually looks like.

A product strategy answers three critical questions for your product:

• Where to play — In what market does your product compete in? This breaks down into:

  • Problem to solve — What specific problem are you solving?

  • Target audience — What specific customer segment are you going after?

• How to win — How will your product differentiate itself and win in it’s market? There are three core questions to answer when it comes to winning against the competition:

  • Value proposition — How are you uniquely solving the problem for the customer?

  • Business model — How are you receiving value for your product (ads, transactions, subscriptions) and what is your specific pricing strategy?

  • Growth strategy — How will you attract customers to your product?

• How to endure — How will you ensure you maintain your edge against competition? This boils down to:

  • Competitive advantage — What unique moat can you construct to ensure your competitors can’t easily replicate your success?

While answering each of these questions is necessary for constructing a product strategy, it’s certainly not sufficient for crafting a compelling strategy.

The most compelling product strategies have the additional attributes of being insight-driven, distinctive, focused, cohesive, market aware, and non-consensus and right.

If you want to go deep on product strategy, check out my essays summarizing the thought leadership of the world’s leading strategists:

• A Primer on Business Strategy From Hamilton Helmer’s 7 Powers

• Playing to Win by Roger Martin

• Good Strategy, Bad Strategy by Richard Rumelt

• Michael Porter on Developing a Compelling Strategy

Now let me explain the typical approach that is often leveraged to write a product strategy with AI: you provide an LLM context about your product, give it a product strategy template like the 6 dimensions we just discussed, and then ask it to put together a product strategy.

What you’ll get back is a solid B product strategy. It looks decent because it comprehensively addresses each of the six dimensions of your product strategy template, is well written, and has zero grammatical errors. But it will typically lack any novel insights, offer no strong opinions on what direction to pursue, and fail to challenge conventional wisdom. It basically fails to adhere to each of those additional attributes we just discussed that make a strategy actually compelling. (And believe me, simply telling it to craft a strategy adhering to those attributes doesn’t help either).

Let me illustrate why this actually happens. Let’s take the attribute of non-consensus and right. A great product strategy takes an opinionated bet on a strategic direction that not a lot of other people agree with but also happens to be right. When you do this effectively, you get a significant head start on the market and it allows you to pursue the space initially with limited competition.

Take, for example, Elon Musk and his early focus at Tesla on building a luxury performance electric vehicle. At the time, this was not where the market was headed. Instead all the existing EV manufactures were building cars like the Chevy Bolt and Toyota Prius — low-end EVs focused on saving gas and being economical. Elon’s contrarian bet ultimately paid off as a market did in-fact exist for such a luxury EV and it allowed him to get years ahead of the competition. But such contrarian non-consensus bets are difficult for AI to develop due to their probabilistic nature, which quickly steers you back to consensus solutions.

I hope you can now see why you’re unlikely to be able to leverage AI to one-shot a compelling product strategy.

A SIDEBAR ON TASTE

In our discussion of both product vision and product strategy, you’ll notice I pointed out exactly where AI fails to deliver quality output. I could have only done that if I had a deep sense of what great work actually looks like. That’s taste. And that actually matters more than ever in the age of AI. Read my primer on developing taste.

But that certainly doesn’t mean we can’t leverage AI to help us put together a product strategy. We just have to do so in far more nuanced ways.

I’ve found four highly effective uses of AI when it comes to crafting a compelling product strategy:

Researching your market

The Deep Research capabilities of AI tools are incredibly effective for helping to research your market, including competitors, customers, and industry trends.

Whenever I’m looking to refresh my product strategy, this is where I start. I’ll put together an extensive deep research prompt, seeding it with key competitors, asking for specific traction signals, and asking for evidence to monitor certain trends. These prompts will typically take 15 - 30 minutes to get a response. I’ll typically simultaneously fire off the same prompt to several different AI tools (say Claude, ChatGPT, and Gemini) and then spend my time reading through the reports. I always come away with insights I didn’t realize, especially on what’s working for particular players in the market.

Crafting strategic dimensions

I’ve also found AI to be quite effective at helping me to craft very specific dimensions of my product strategy. But here’s the nuance — I’m not asking it to one shot my entire strategy. Instead I’m providing context on the dimensions I already have deep insights and strong conviction on. And then leveraging AI to help me explore options for a particular dimension I have low conviction on.

For example, let’s say I’ve got a solid problem to solve and target audience, but I’m not quite sure what growth strategy would be most effective to reach my target segment. In that case, I’ll give AI the rich context on the dimensions I already have and begin a brainstorming and exploration exercise for the growth strategy. I’ll have it brainstorm ideas and provide pros and cons for each. What I find particularly helpful is asking it to come up with analogs (examples of others successfully employing the strategy) as well as antilogs (examples of where the strategy failed for others). By doing so, I can build much stronger conviction on whether that strategic approach would work for us.

Critiquing strategy

While AI isn’t particularly good at drafting an entire strategy, it’s surprisingly good at critiquing a strategy once written.

I’ve done this in multiple different ways. First, I built a CustomGPT, called Product Strategy Critique, that takes your product strategy and provides a detailed critique. Feel free to give it a try on your own product strategy. I’ve created the same thing as a project in Claude as well as a custom /critique-product-strategy skill in Claude Code.

Each approach takes all the best practices on product strategy from my Mastering Product Management course and specifically applies them directly to your product strategy document. You’ll get a detailed critique on each of the strategy dimensions which you’ll find to be quite actionable.

I honestly wish I had this when I was at LinkedIn, as it would have significantly up-leveled all my PM’s strategies before meeting with me and the rest of the exec team.

If I was still at LinkedIn, here is how I would have made it even better: I would have recorded and transcribed all the executive product review meetings we had using a tool like Granola, then extracted via AI all the key strategic questions asked as well as strategy best practices shared by the leadership team, and then added that to my custom GPT as additional best practices to improve the critique. In this way, I could have incorporated the specific strategic wisdom of the executive team. I’d encourage you to do this too for your own team!

Tackling strategic challenges

The final way I use AI for strategy is as a thought partner when tackling specific strategic challenges. Ultimately every product comes to a point where it faces a strategic challenge, whether it’s related to competition, loss of product/market fit, reduced growth rate, or a myriad of other challenges.

I’ve found AI to be an extremely helpful thought partner when diagnosing, brainstorming, and weighing options to address such challenges. For example, at Notejoy, we recently realized we were having a pricing challenge. Over the last 5 years all of our competitors had significantly increased their pricing, along with many others in the SaaS space. We hadn’t touched our pricing at all and suddenly found ourselves to be one of the cheapest offerings on the market. The question was, should we also increase our prices since we were leaving so much money on the table? Or had our current low prices become a valuable differentiator for us relative to the competition?

I presented AI with this challenge, gave it the appropriate context, and asked it to help brainstorm potential pricing directions. Not only did it make strong recommendations, but ultimately gave me a detailed plan on how to even A/B test the price change.

Are you interested in step-by-step training on how to develop each one of these AI powered deliverables? Then my AI Productivity course is for you. I go through each PM deliverable and share detailed demos of what AI tool to leverage and how to leverage it most effectively to develop that deliverable. Join 2,5000 product managers who have already become AI native. Learn more.

Design

When it comes to design, product managers are responsible for delivering customer insights, data insights, roadmaps, specs, and increasingly, prototypes. Let’s talk about the impact AI is having on each of them.

Read more

Hey there 👋 This guide is a preview of some of the content from my course, AI Productivity, which is designed to help product managers bring AI fluency to every aspect of their role, whether it’s customer discovery, prototyping, product strategy, or execution. Next cohort starts April 7th. Learn more.

Andrew Ng, the founder of Coursera and DeepLearning.ai, recently made a provocative statement: the bottleneck in product development is no longer engineering, it’s now product management. For the entire history of product management, we’ve been waiting on engineers to build the product. But with AI coding tools like Claude Code, Cursor, and Codex, engineering teams now have extraordinary leverage. The delivery side of product development has been dramatically accelerated.

The challenge is that building great products has always been about both delivering the solution and discovering what’s worth building. So far, product teams are not seeing the same acceleration on the discovery side as we are on delivery. In fact, I’m now seeing product managers who simply can’t keep up with their engineering counterparts. When that happens, one of two bad outcomes typically transpires. Progress is either meaningfully constrained or engineering teams are simply shipping features that haven’t actually been validated with customers. No one is happy with either outcome.

The exciting news is that AI is now emerging as a powerful accelerant for customer discovery as well. These tools and capabilities aren’t yet as advanced as the coding tools on the delivery side, but they are rapidly improving.

Given the discovery constraint, I conducted a deep dive to understand just how teams could leverage these new AI powered discovery workflows. I’ve been experimenting with these approaches on my own product management process at Notejoy and I’ve found 10 AI workflows that are genuinely transforming how I approach customer discovery.

In this comprehensive guide I’ll walk you through each of the workflows, share how they meaningfully accelerate my discovery efforts, the AI tools I’ve found most useful for them, as well as best practices for getting great results.

My hope is that you’ll walk away with a handful of new AI powered workflows that you can immediately bring back to your team to accelerate your own discovery efforts.

Analyzing Customer Surveys

The first workflow is using AI to synthesize feedback from customer surveys. You can now take thousands of survey responses, have AI synthesize the most frequent themes, and visualize the results in minutes.

I think sometimes, because I now use AI every day, I have to remind myself just how revolutionary this actually is. Just a couple of years ago, the best technology we had for analyzing surveys was sentiment analysis, which could tell you whether a response was positive, neutral, or negative. Not particularly actionable. We then moved to tag clouds, which surfaced frequent keywords across the surveys. Again, still not actionable enough. It still meant that someone had to read every survey response, collate the feedback, and manually create themes. I did this work all the time.

But now generative AI has actually become incredibly good at taking raw survey responses and generating thematic insights. If you tried this a year ago and thought the results weren’t very good, it’s night and day improved. I’ve compared my own manual synthesis of customer survey themes against AI-generated synthesis and not only does it find very similar themes, but has even surfaced valuable insights I hadn’t initially spotted myself.

The practical impact of this capability is enormous. When I ran NPS surveys at LinkedIn, I could only do it quarterly because I had a team of marketers spending an entire week gathering results and reading through a thousand survey responses. Now I can run NPS surveys far more frequently.

What’s equally powerful is that AI allows you to run unlimited segmentation analyses. At LinkedIn, I might ask my team for two or three segmentation cuts. Now I can run 15 segmentations because it only costs me another line of typing. I can segment by usage patterns, by subscription type, by email domain, and even ask for statistical significance calculations on each comparison. All of this is automatically done for me.

All of the popular AI chatbots — ChatGPT, Claude, Gemini, and Copilot — are great for analyzing customer surveys, so you can easily leverage whatever your team is already using to accomplish this.

One word of caution here. As product managers, we need to continually build our product intuition to enable us to make smart decisions every day in the absence of data. I like to think of product intuition as our own machine learning model that we build by reading customer feedback, pattern matching across it, and synthesizing those patterns into working insights. AI is great at this, but we need to stay in the loop. That means when AI generates themes, I always ask it to show me the exact customer verbatims underneath each theme. I’m not just relying on its commentary and summarization. I want to directly hear and interrogate the voice of the customer. By consuming that direct feedback, I continue to build my own intuition while relying on AI to do the heavy lifting of aggregation and summarization.

Automating Customer Survey Programs

The ad hoc analysis I described above is incredibly powerful and useful, but I’ve taken it a step further by fully automating the process with an autonomous agent that produces weekly NPS survey reports for me without any manual intervention.

To understand this approach, it’s helpful to think about the full spectrum of AI interfaces that now exist. We started with chatbots, which have now become ubiquitous. With these tools, you give them a prompt and get back a quick answer. We then innovated with the introduction of copilots, where AI shows up as a sidebar to help you directly manipulate an artifact, whether that’s code in Cursor, a Notion document, or a Google Sheet. The third category, which is really taking off right now, are agents: autonomous AI workflows that can run end to end without requiring your input.

I’ve built an agent using Claude Code that takes my NPS survey results file, runs the full numerical analysis, generates promoter and detractor verbatim themes, produces an interactive HTML report, and even creates an executive presentation using Gamma, an AI presentation tool. The entire workflow is defined in a skill file written in plain English that describes each step the agent should follow.

The output is a fully automated executive report that includes NPS scores, interactive trend graphs, segmentation analysis, verbatim themes, voice of customer quotes, and even product improvement recommendations. I used to produce this kind of report quarterly at LinkedIn because it took a team of real people a week to put together. Now I do this weekly. I just download the latest NPS results, kick off the agent, and get a polished report delivered automatically.

I’ve come to prefer coding agents like Claude Code and Codex as my preferred way of building agents these days. But an equally viable approach is to leverage the workflow automation tools like Relay.app, Zapier, and n8n for building end-to-end autonomous agents. I’d encourage you to try building a workflow both ways and deciding which approach feels easier to you.

Automating Feedback Rivers

There’s a new category of tools that have recently emerged that are fantastic at customer discovery that I call feedback rivers. The idea is simple: aggregate customer feedback from a variety of sources, put it all in one place, and continuously monitor it. Maybe it’s pulling in your App Store reviews, Google Play reviews, G2 reviews, Zendesk tickets, or even Reddit discussions. The tool aggregates all of it and uses AI to automatically synthesize themes across all that feedback.

There are many tools in this category, including Reforge Insights, Enterpret, Kraftful, Birdie, and Productboard Pulse. They all have different features, but the core value proposition is the same: automated, continuous insight into customer sentiment across every channel.

What I find particularly valuable about these tools is the ability to see themes trending over time. If I see an issue trending up, I might decide to then take it more seriously. After we implement a fix, I can see in real time whether the volume of related complaints actually is going down. It gives me a continuous pulse on what’s happening with my customers.

I can also click into any theme and see exactly which customers said what. If we decide to investigate an issue further, I know exactly who to reach out to. I have their email address and can send a targeted outreach to 20 customers who experienced the same pain point. In this way, these tools act as a feedback CRM, allowing me to understand exactly who is experiencing particular issues, the value of those particular customers to our business, and the ability to close the loop to turn a feature implementation into a customer win.

Read more

Hey there 👋 This guide is a preview of some of the content from my course, AI Productivity, which is designed to help product managers bring AI fluency to every aspect of their role, whether it’s prototyping, customer & data insights, product strategy, or execution. Next cohort starts April 7th. Learn more.

AI prototyping is fundamentally reshaping the way the best product teams design, validate, and prioritize what to build.

What’s been equally fascinating to watch is just how quickly this approach has gained adoption amongst Silicon Valley technology firms. We are now at the point where Meta, for example, expects PM candidates to use popular AI prototyping tools like Figma Make, Lovable, or Claude Code to prototype a feature live during their interview process.

When I talk to product managers though, it’s become clear that while many of them may have played around with these AI prototyping tools and potentially built a few prototypes, rarely have they really developed a strong mastery of AI prototyping. Their prototypes often generically solve the customer problem, lack branding, lack meaningful differentiation, and often lack depth.

As AI prototyping has become an essential capability of today’s AI native product managers, I realized we needed a far more rigorous approach to teaching PMs how to truly master this new capability. That’s why I developed the AI Prototyping Mastery Ladder, which catalogs the 15 distinct skills PM need to master to prototyping effectively.

In this comprehensive guide I will motivate why AI prototyping matters, introduce the AI Prototyping Mastery Ladder, and guide you through learning each of the requisite skills to take your prototyping to the next level.

The Case for Prototyping

Before diving into the skills, I want to make the case for why every product team should be prototyping. There are two primary arguments I want to make, the first an evolutionary one and the second a revolutionary one.

The Evolutionary Argument

When I started my career at Microsoft as a product manager, I used to write 30-page specs for relatively small features. That was just the name of the game. As an industry, we’ve evolved quite a bit since then. We gained maturity on our design tools, from Photoshop to Figma, and we realized that a picture is worth a thousand words. Today, teams rely heavily on annotated design mockups to communicate what they envision building because those designs are far higher fidelity than you can achieve in words alone.

Prototyping is simply the next evolution of that progression. A prototype goes beyond a mockup by adding interactivity and even functionality, increasing the fidelity even further.

We’re now seeing organizations move fast to adopt this new medium. Jin Su Park, a lead designer at Notion, recently shared that he attended a design crit where no one shared work via Figma. It was all AI prototypes. Design reviews, exec meetings, customer validation sessions are all moving toward vibecoded prototypes as the primary medium of communication.

The Revolutionary Argument

Now this is where I get particularly excited about the potential of prototyping: I believe prototyping has the potential to change how product teams fundamentally prioritize their roadmaps.

Traditionally, teams start by prioritizing customer problems on the roadmap. Once a problem is prioritized, the team then goes about building a potential solution, going through design reviews and some customer validation.

But the best teams don’t actually work this way. They do something I call product shaping. The idea is that instead of prioritizing problems and then building solutions, these teams build a bunch of prototypes for potential problems, test those prototypes with customers, and then prioritize on the roadmap the most successful prototypes. This might sound subtle, but the difference is profound. You’re prioritizing problem-solution pairs that you know have worked because you’ve already prototyped and tested them.

My favorite example of this is Apple. Johnny Ive was in the lab working on a multi-touch interface for a tablet. He shows it to Steve Jobs, who sees the prototype, and rather than pursuing a tablet, they shelved the entire tablet project, went on to create the iPhone, and ultimately change the world. The prioritization decision was driven by the prototype and what it revealed was possible.

Now, why doesn’t everyone do this? It turns out running an expensive prototyping lab where you’re going to throw away 90% of your prototypes is insanely expensive. Most organizations could never justify that. But AI prototyping completely changes that equation. Now we can all be making throwaway prototypes, testing them, and picking the best ones, as fast as we currently wait for our designers to come up with mockups. That’s the revolutionary potential of AI prototyping.

Now, this isn’t some theoretical future state. This is already happening today. Take, for example, the Claude Code team at Anthropic. They are already operating in this exact fashion. The team produces a variety of prototypes of new features for Claude Code and then ships them internally for dog-fooding. If people love the feature internally, they ship it. If the feature gets limited usage or constructive feedback, they go back to the drawing table to improve it or scrap it. With this approach the prototypes effectively drive their roadmap, not the other way around.

The AI Prototyping Mastery Ladder

Hopefully I’ve now convinced you of why your team should be prototyping. The essential question then comes how do you actually become great at it?

After spending months working with PMs to improve their prototypes, I’ve identified 15 essential skills that you need to master to become a truly effective AI prototyper.

I’ve organized them into a hierarchy of skills because it turns out you have to master the basics before moving on to intermediate and advanced levels, much like the progression of craftsmen from apprentice to journeyman to master.

Let me walk you through each of the three levels:

Apprentice

At the apprentice level, we are really focused on mastering the foundational skills of prototyping, which include the following:

• Prompting - The way prototyping tools work is we spend most of our time prompting in English to have them generate code on our behalf. So the first set of skills is around effective prompting.

• Editing - We then spend most of our time editing our apps, refining and improving what was generated. There are multiple ways to edit effectively (prompting, prompting w/ selection, visual editing, and code editing), and learning the trade-offs between each is important.

• Design consistency - Beyond that, design consistency is critical. The default output of these tools doesn’t look anything like the products we build. We need to learn how to get our prototypes to incorporate our design guidelines and design systems so they look and feel like products from our company.

• Designer collaboration - We also shouldn’t be building prototypes in silos, so learning to collaborate with designers is essential.

• Limitations - Equally important is learning the real-world limitations of AI prototyping so we understand when not to reach for a prototype for the task at hand.

• Tools - Finally, we want a deep understanding of the landscape of prototyping tools and which ones are best suited for our prototyping use case.

Once you’ve mastered these skills, you are well equipped to start building a few prototypes for your team.

Journeyman

At the journeyman level, we are learning advanced prototyping techniques that enable us to deeply incorporate prototyping into our daily product development workflow. The critical skills at this stage include:

• Versioning - Versioning refers to keeping track of different iterations of your prototype so you can compare, roll back, or fork your work without losing progress. Knowing how and when to fork, in particular, becomes critical for managing a rich prototyping workflow.

• Debugging - As you start prototyping in earnest, you’ll inevitably hit bugs. If you’re non-technical, this can be overwhelming. So learning how to debug as a non-developer is a crucial skill.

• Diverging - And then there’s diverging, which is this idea that we shouldn’t just take the first idea in our head and build it. Instead, we should learn how to use AI as a thought partner to come up with multiple design variations, just like we would with a human design partner.

• Customer validation - One of the primary use cases for prototypes is validating them with customers. But prototypes offer so much more in terms of validation opportunities than static mockups. So we need to learn how best to leverage these new opportunities to maximize our validation.

• Executive reviews - Prototypes are quickly replacing design mockups as the primary presented deliverable in executive reviews, so it’s important to learn how to use them effectively in this setting.

Master

At the master level, we are fundamentally reshaping our product development process due to our new AI prototyping capability. To unlock this, we need to master the following skills:

• Technical editing - I’ve found those that are most successful at prototyping get technical enough to gain far more precise control over their prototypes. Technical editing is all about getting technical enough to wield this control.

• Functional prototyping - We can make our prototypes go beyond experience prototypes and make them fully functional, where they call real APIs, store data, and support actual user behavior. This skill is all about learning exactly how to do so.

• Engineering handoffs - Once we’ve developed our prototype, we need to hand it off to engineering for turning it into a production app. We are starting to see some best practices emerge on how to do this most effectively.

• Product shaping - As I mentioned, the real unlock for prototyping isn’t just improving our customer validation process, but fundamentally reshaping our roadmap prioritization process. There are some crucial best practices for making the transition to this new prioritization approach.

Skills In Action

To help you get started on your journey to becoming a master prototyper, I want to go deep on four of these skills that I think are particularly transformative: design consistency, diverging, functional prototyping, and customer validation. (Better yet, watch the video to see each of these demos live).

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Hey there 👋 It’s Sachin Rekhi, your favorite product management writer. I recently migrated my blog, which you are a subscriber of, to Substack and this is my first post on the new platform. My new focus is helping product managers master their craft in the age of AI.

This first post, Claude Code for Product Managers, is a preview of some of the content from my course, AI Productivity, which is designed to help product managers bring AI fluency to every aspect of their role, whether it’s prototyping, customer & data insights, product strategy, or execution. Next cohort starts April 7th. Learn more.

Over the last few months I’ve become convinced that Claude Code is now the most productive AI platform for product managers.

I know most product managers are already using AI tools on a daily basis, whether it’s ChatGPT, Claude, Gemini, or Microsoft Copilot. These chatbot-style tools where you ask a question and get a response are undeniably useful. But I’ve come to realize that the real unlock for product managers isn’t in simply using AI to answer your questions. But it’s instead in building AI-powered systems and workflows that actually automate the work we do every day. This is exactly where Claude Code shines and why I’ve gone deep down the rabbit hole on getting the most out of Claude Code as a PM.

In this guide, I’ll share why I believe every product manager should be using it, what’s now possible with this agentic platform, how to get started, and a step-by-step guide for building your own AI workflows.

Why Claude Code

Claude Code was originally built as an agentic coding tool, enabling developers to leverage AI to accomplish far more coding tasks autonomously on larger and more complex code bases.

What makes it fundamentally different from chatbot-style AI tools is that it’s designed to be truly agentic, meaning it is optimized for working autonomously, executing multi-step workflows on your behalf rather than simply responding to individual prompts.

It turns out these same capabilities that make it powerful for autonomously manipulating code make it equally powerful for product work: creating documents, product specs, reports, and analyses.

I’ve discovered six unique capabilities that really help set Claude Code apart for product managers:

Artifact generation. Claude Code excels at creating, improving, and modifying artifacts. Whether it’s a product strategy critique, a competitive analysis, or a set of release notes, it’s designed to produce polished output, not just conversational responses.

Rich local context. You can store all of your product data and documentation in local markdown files, and Claude Code can read them quickly and reliably. This is faster than calling third-party APIs or using MCP integrations, which means you can give it a ton of context for your product workflows and it can consume that context efficiently.

Workflow automation. Whether you’re building skills, agents, or commands, Claude Code provides the primitives to automate entire workflows that run autonomously. You can fire off a skill, grab a coffee, and come back to finished work.

Command line tools. Claude Code can run any command on your computer, giving it access to virtually any capability you need, from transcribing audio with Whisper, to querying databases with MySQL, to browsing competitor websites.

Code as a tool. It can even write code on your behalf to accomplish tasks, such as accessing third-party APIs or analyzing data with Python scripts. You don’t even need to know how to code for it to do so.

Ultimate portability. Because everything is stored locally in markdown files, you avoid vendor lock-in entirely. Even the skills you build rely on an open standard. So at any time you can take your data and workflows with you to the next agentic tool that becomes popular. Compare that to something like Notion’s AI capabilities, which are powerful but lock your data into their restricted ecosystem.

Alternative Agentic Platforms

Now, Claude Code isn’t the only agentic platform that has emerged:

• Anthropic also offers Claude Cowork, which is more user-friendly, but more limited in scope

• OpenAI offers it’s own version with Codex App

• OpenClaw is also getting a lot of attention as a highly capable open-source alternative, though it’s also being highly scrutinized for its serious security shortcomings.

• Pi.dev is the underlying agent harness used by OpenClaw, which some folks are adopting directly

• Perplexity recently launched Computer, it’s own take on an agentic platform

Despite these alternatives, the reason I’m betting on Claude Code is I’ve found it to have the rare combination of both being incredibly powerful and also enjoying broad enterprise adoption today. You can see in the chart that Claude’s enterprise adoption has accelerated as of late making it the enterprise market leader.

It’s clear that learning this tool isn’t just useful now, but you’re also very likely to encounter it in your current or future roles as well.

What’s Now Possible

To show you just how powerful this platform is, I want to walk you through the dozen different PM workflows that I now use Claude Code for on a regular basis.

As you may know, I like to think about the product role as dividing into four key responsibilities: vision, strategy, design, and execution. I’ve already been able to build powerful AI workflows across strategy, design, and execution in particular:

• For strategy, I use Claude Code to critique my product strategy drafts, update competitor pricing matrixes, and generate competitive teardowns.

• For design, I use it to generate interview scripts, summarize customer interviews, conduct NPS analyses, answer data questions, and generate dashboards.

• For execution, I use it for managing meetings, drafting meeting agendas, and generating release notes.

Let me walk through five of these workflows to give you a sense of what’s now possible. (Or better yet, watch the video to see each of these demos in action).

1. Critiquing Product Strategy

I’ve found that while AI tools aren’t particularly good at generating a product strategy from scratch, they are incredibly good at critiquing one. I built a skill that takes a product strategy document as input and provides a rigorous critique based on a specific set of best practices.

The key to making this work is what I call “showing it what great looks like.” I took all of my course content on product strategy, frameworks for evaluating target audience, value proposition, strategic differentiation, and more, and saved them as local knowledge files. The skill reads the product strategy, then leverages all of those best practices to systematically critique each dimension.

For example, when I ran it against a product strategy I’d been developing for a personal finance product, it told me that my target audience definition was too broad and lacked the rigor of a true “bullseye narrowing.” It flagged that my problem statement was surface-level and failed to apply the outcome-motivation-gap framework. These are critiques grounded in very specific strategic frameworks that I’d fed into the system, not generic AI platitudes.

2. Updating Competitor Pricing

Keeping tabs on competitor pricing is one of those tasks that’s tedious, time-consuming, and easy to let slip. I built a skill that uses Claude Code’s browser agent to automatically visit each competitor’s pricing page, extract the pricing data, and compile it into a comprehensive competitive analysis.

The reason I chose the browser approach is important. In the past, anytime I asked Deep Research or ChatGPT to get competitor pricing, the results were woefully out of date. The tools would inevitably find old pages with stale information. I realized the only way to get accurate pricing data is to have the AI actually navigate to each pricing page in real time and extract the information directly. It’s slower than other approaches, but it’s the most accurate way to get the data.

The output is an executive summary with every competitor’s plan and pricing, which I’ve hand-verified for accuracy. It even provides strategic commentary -- noting, for instance, that my product, Notejoy offers one of the most generous free plans among competitors, or that Evernote’s free tier is the most restrictive.

3. Summarizing Customer Interviews

This is one of the workflows I find most valuable. I have a folder of video recordings from customer interviews -- just raw Zoom meeting files. The skill takes each video, transcribes it to text using Whisper (a free transcription tool from OpenAI), and then summarizes each interview using a prescriptive template I’ve designed.

The template is critical. I’m not just telling it to “summarize the interview.” I’ve specified exactly what I want: details about who we interviewed, key takeaways, problems and pain points, their current workflow, alternative tools tried, feature requests, and direct quotes from the customer. This level of specificity in the template is what makes the output genuinely useful rather than a vague summary.

But it doesn’t stop at individual summaries. I then have the skill generate a cross-interview patterns document that synthesizes feedback from all interviews into a single analysis. It identifies pain points by prevalence -- for instance, “feedback fragmentation” appeared in 10 out of 10 interviews -- and includes supporting quotes for each pattern. This kind of synthesis across a large number of interviews is exactly where AI shines. It can do this comprehensively in a way that would take me hours to accomplish manually.

4. Answering Data Questions

I built a skill called “answer data curiosity” that takes any natural language data question, writes the appropriate SQL query, executes it against my database, and then generates a formatted HTML report with tabular results, visualizations, and auditable SQL.

I never have to write SQL anymore. I just fire off a question in plain English and get an answer. What I love most about this is before I relied heavily on my data team, which meant there was significant time before I got answers and I always had to prioritize what were the most important data questions.

Now no data curiosity ever goes unanswered.

5. Generating Release Notes

Writing release notes is one of those tasks I find myself doing repeatedly. I built a skill that takes a GitHub commit URL, inspects the title, description, and changed code files, and produces a user-facing release note with a title and a one-to-five paragraph description written from the user’s perspective.

The power of this became clear when I tested it on a recent commit where my commit message was simply “web clipper added article summaries.” From that minimal input plus the actual code changes, it produced a polished release note explaining that the Notejoy web clipper’s AI summary feature now works with articles and LinkedIn posts, not just YouTube videos, and describing the user experience of the new capability.

Since I gave it the past 20 release notes as examples, it always follows the voice and tone of our previously hand-written release notes, ensuring a quality output.

Getting Started

Now that you have an understanding of what’s possible with Claude Code, let’s get you setup to take advantage of it for your own PM workflows.

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AI prototyping is fundamentally reshaping the way product managers discover, validate, and prioritize what products and features to build. But the knowledge of how to actually create AI prototypes remains amongst a very small cohort of PMs.

Watch on: YouTube

In this video, I hope to change that by providing a primer on AI prototyping to get every product manager up-to-speed on this latest innovation. I cover:

• Why prototyping is an absolute must for every PM

• Bust the myths that often cloud prototyping

• Showcase a live demo on creating a prototype from scratch using Bolt

• Offer insights into today’s top prototyping tools

• Explore the AI prototyping mastery ladder — the essential skills to elevate you to master prototyper status

If you’ve ever been curious about learning more about AI prototyping, this video is for you.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.
   

Strategy is an integrated set of choices that uniquely positions the firm in its industry so as to create sustainable advantage and superior value relative to the competition.

Roger Martin knows strategy. Both from his experience as a strategic advisor at the Monitor Company to clients like Procter & Gamble, Lego, and Verizon as well as former business professor at the Rotman School of Management at University of Toronto. He ultimately captured his decades of lessons learned on strategy in his popular book, Playing to Win.

Martin's core thesis is that an effective strategy requires a coordinated and integrated set of five choices: a winning aspiration, where to play, how to win, core capabilities, and management systems.

Winning aspiration

A winning aspiration defines the purpose of your organization and it's motivating aspiration. It's a statement about the ideal future. Aspirations shouldn't change day to day, since they exist to consistently align activities within the firm, and so should be designed to last for some time.

At the brand Olay, the winning aspirations were defined as market share leadership in North America, $1 billion in sales, and a global share that put the brand among the market leaders.

Martin believes that having a winning aspiration is critical for success. In his view, when companies set out to participate in a market instead of winning that market, they will inevitably fail to make the tough choices and the significant investment that would make winning even a remote possibility. Too many companies eventually die a death of modest aspirations.

Where to play

The next critical choice is picking the playing field where you can achieve that aspiration. Here you are specifically narrowing the competitive field that you will focus on. No company can be all things to all people and still win, so it's important to understand which where-to-play choices will best enable your company to win.

In particular, the choices you need to make are:

• Geography - In what countries or regions will you seek to compete?

• Product type - What kinds of products and services will you offer?

• Consumer segment - What groups of consumers will you target? In which price tier? Meeting which consumer needs?

• Distribution channel - How will you reach your customers? What channels will you use?

• Vertical stage of production - In what stages of production will you engage? Where along the value chain? How broadly or narrowly?When making your where-to-play choices, you'll not only need to leverage your deep understanding of your customers as well as your own capabilities, but also carefully consider your competition. Oftentimes it makes more sense to avoid head-to-head combat with an incumbent and instead find an under-served need or different product approach to tackling the market. At other times, head to head competition may make sense when you have new and distinctive value you can bring to bear against the incumbent.

Take, for example, the Bounty paper towel brand. P&G identified three core customer segments. The first segment cared about strength and absorbency, which Bounty's original product served well. They then identified a second segment that cared mostly about how soft the paper towels were. Bounty decided to pursue this segment by creating a product extension known as Bounty Extra Soft. The third segment cared primarily about price. Bounty realized it couldn't serve this segment well without devaluing it's brand so made the strategic choice to not focus on it. These specific where-to-play choices enabled the Bounty brand to continue to gain market share in its most lucrative North American market.

How to win

The third critical choice is how you'll win in your chosen playing field. It is the recipe for success in your chosen segments, categories, channels, and geographies. Your how-to-win choices are therefore intimately tied to your where-to-play choices.

All successful how-to-win choices boil down to one of the two approaches: cost leadership or differentiation.

In cost leadership, you develop a cost advantage relative to your competition. This can be done through cost leadership in sourcing, design, production, distribution, and so on. When you have that cost leadership, you may choose to leverage it to reduce your prices. But you can also use your extra margin in alternative ways that create competitive advantage. Take for example Mars candy bars, which has a cost advantage compared to Hershey's candy bars. Instead of using that cost advantage to reduce its price, it instead used it's extra margin to purchase the best shelf space in the candy bar rack in every convenience store in America.

In differentiation, your goal is to offer products or services that are perceived to be distinctively more valuable to customers than competitive offerings and are able to do so with approximately the same cost structure as your competitors. This differentiation can be driven by product design, product performance, quality, branding, advertising, distribution, and more.

It's very difficult to be both a cost leader and a differentiator, as the culture within these organizations often differ drastically. Cost leaders are forever looking to better understand the drivers of costs and are modifying their operations accordingly. Differentiators are forever attempting to deepen their holistic understanding of customers to learn how to serve them more distinctively.

It's important to understand that there isn't one "right" strategy in a given market. Instead there are typically multiple ways to win in almost any industry. So it's important to find the particular how-to-win approach that will work uniquely well for your customers, your organization, and your relative position to the competition.

Core capabilities

The next set of choices are the set and configuration of capabilities required to win in your chosen way. Capabilities are the map of activities and competencies that critically underpin specific where-to-play and how-to-win choices.

These activities are best understood as a system of reinforcing activities. Competitive advantage arises from the collection of and fit between each activity.

The reason to identify these core capabilities is to enable you to disproportionally invest in building out those capabilities that produce competitive advantage and invest less or outsource those that do not.

At Procter & Gamble the core capabilities they focused on developing were deep consumer understanding, innovation, brand building, go-to-market ability, and global scaling.

Now your core capabilities need not always be tied directly to your existing organization strengths. This is because those strengths might turn out not to support your where-to-play and how-to-win choices. In those cases the effort becomes identifying the core capabilities that you need to develop in order for your to succeed in your chosen playing field.

Management systems

The final set of choices are determining the systems that foster, support, and measure the strategy. Typically you need to establish three types of management systems:

• Robust processes for creating, reviewing, and communicating strategy - These are often carefully crafted strategy review meetings that enable building the strategic thinking capability amongst a leadership team. The ideal format creates a culture of inquiry that surfaces productive tensions to inform smarter choices.

• Structures to support core capabilities - These are systems that really help you develop your core capabilities. At P&G, for example, they invest heavily in new consumer-research methodologies in an effort to lead the industry with real in-house consumer and market research capability.

• Specific measures to ensure that the strategy is working - Measurement helps to provide both focus and feedback. Focus comes from an awareness that outcomes will be examined, creating a personal incentive to perform them well. And feedback comes from the fact that measurement allows the comparison of expected outcomes with actual outcomes and enables you to adjust your strategic choices accordingly. These measures should span financial, consumer, and internal dimensions to prevent the team from over-focusing on any one dimension.

Next time you are putting together a product strategy, I'd encourage you to define it in terms of Martin's 5 critical choices: a winning aspiration, where to play, how to win, core capabilities, and management systems.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

Renowned UCLA strategy professor Richard Rumelt argues that many business leaders have a misguided understanding of what truly constitutes great strategy. In his widely acclaimed book Good Strategy, Bad Strategy, Rumelt explores the reasons behind the prevalence of poor strategies in the business world and provides a clear framework for developing effective ones.

Why there is so much bad strategy

In Rumelt's view, leaders have been led astray by the rising popularity of the transformational leadership movement. The movement suggests that leaders can simply focus on developing a vision, inspiring the team, setting high level goals, and empowering the team to accomplish them. However, Rumelt believes that this model of leadership is missing one of the most critical responsibilities of the role: developing an effective strategy.

For Rumelt, a vision is not a strategy. While having a big picture overall direction is helpful, when you divorce it from the concrete steps to actually achieve it, you leave the organization rudderless and often unable to realize any part of that vision. Similarly, goals are not strategy. They are nothing more than aspirations if they aren't attached with a concrete achievable plan for accomplishing them.

When leaders do attempt to put together an actual strategy, they often fail to make the hard choices required to pursue an effective strategy. Strategy involves focus and, therefore, choice. And choice means setting aside some goals in favor of others. When this hard work is not done, weak amorphous strategy is the result. The enemy of an effective strategy is therefore consensus-driven cultures and a focus on achieving universal buy-in. Instead leaders should create a culture of intellectual rigor and debate, enabling colleagues to robustly make their case on what should be the chosen few areas of focus. Decision making culture isn't the only reason making choices is so hard. There is difficult psychological, political, and organization work in saying "no" to whole worlds of hopes, dreams, and aspirations. For these reasons the strategies that often come out of organizations are just a laundry list of priorities without any clear focus.

Take, for example, the story of Digital Equipment Corporation (DEC), an early leader in the minicomputer revolution. In 1992, the company was rapidly losing ground to the newer 32-bit personal computers and there were serious doubts that the company could survive without dramatic changes. In order to address the challenge, the executive team came up with three possible directions for the company: 1) continuing it's focus on developing computers, 2) transitioning to a focus on developing chips, or 3) leveraging their existing customer relationships to move into selling solutions. The CEO, Ken Olsen, mistakenly asked the leadership team to reach a consensus on the new direction to go in. But the leadership team couldn't come to any agreement on what direction to head. They instead came up with a compromise statement: "DEC is committed to providing high-quality products and services and being a leader in data processing." This fluffy, amorphous statement was, of course, not a strategy. It was a political outcome reached by individuals who, forced to reach a consensus, could not agree on which interests and concepts to forgo. Ultimately Ken Olsen was replaced given his ineffectiveness in leading DEC out of its current crisis.

What great strategy entails

A good strategy honestly acknowledges the challenges being faced and provides an approach to overcoming them.

Rumelt's prescription to this challenge is for every leader to take the job of strategy seriously. He encourages them to develop a strategy using the framework he calls the kernel. Rumelt believes that every great strategy should have these three critical components:

1. A diagnosis - explains the nature of the most pressing challenge the organization is facing

2. A guiding policy - specifies the approach to dealing with the obstacles called out in the diagnosis

3. Coherent actions - feasible coordinated policies, resource commitments, and actions designed to carry out the guiding policy Many people would define a strategy as just the guiding policy, which covers the "what". But the diagnosis tells you the "why", which then enables you to debate whether the guiding policy you have picked is likely going to be the most effective one for solving the diagnosis. Equally important are the coherent actions, which represent the "how" of the strategy. Without specific actions, its unclear whether the guiding policy can be implemented effectively.

What's makes defining a diagnosis hard is that there are invariably a whole host of challenges an organization is concurrently facing. The task for the strategist then becomes translating that complexity to a simpler story that calls attention to the most crucial of the challenges the business is facing. A diagnosis need not explain all of the challenges, but simply articulate the challenge that if solved, would have the largest potential impact on the business. In this way there isn't one right diagnosis, but a judgment that must be made to arrive at it.

The guiding policy provides a focused solution to the diagnosis. It's job is not only to point the organization in the direction of the solution, but also serves to call out everything that you are not going to do to attempt to address the diagnosis. In this way, it provides much needed focus.

Coherent actions bring the guiding policy to life through clearly articulating actions that will be taken to address the diagnosis. These actions should be coherent - that is, the resource deployments, policies, and maneuvers that are undertaken should be consistent and coordinated, amplifying the effect of each action taken.

Take, for example, the story of 7-Eleven in Japan in 2008. The company diagnosed their top challenge to be that the global model they were using in North American stores and elsewhere wasn't working in the Japanese market and they needed to rethink their approach to solve for local market needs. After studying the market carefully, they realized that in Japan, consumers are easily bored and are fond of both newness and variety in products. So they developed a guiding policy of offering broad product selection as well as rapidly releasing new products. To make this a reality, they developed several coherent actions. First, they put together a method of collecting information from store managers and employees about local tastes and forming quick-response merchandising teams to develop new product offerings. They then also developed relationships with a number of second and third-tier food manufacturers and found ways to quickly bring new offerings to market under its own private-label brand at low prices using the food manufacturers' excess capacity. The carefully constructed diagnosis, guiding policy, and coherent actions were a strategic success and ultimately turned around 7-Eleven sales in Japan.

Sources of power

Rumelt also believes that a great guiding policy draws upon some source of power. Just as a lever uses mechanical advantage to multiply force, strategic advantage multiplies the effectiveness of resources and/or actions. There are many potential sources of power that can be taken advantage of:

• Leverage - focusing minds, energy, and action at the right moment onto a pivotal objective

• Proximate objectives - simplifying the complexity of a problem into a single objective an organization can solve

• Chain-link systems - identifying the bottleneck in your organization and directing attention to it

• Design - leveraging design thinking to construct superior solutions

• Market focus - attacking a segment of the market and creating more value for that segment than other players can

• Growth - growing demand for special capabilities or of expanded or extended capabilities

• Competitive advantage - producing at a lower cost or delivering more value than competitors can

• Market dynamics - identifying and exploiting a wave of change happening in the market

• Rival inertia - taking advantage of a rival's unwillingness or inability to adapt to changing circumstances The art of strategy is finding which source of power can be leveraged by your organization in your specific market to address your organization's top challenge.

Building your strategic muscle

Rumelt believes that deep knowledge is necessary but not sufficient for being a great strategist. He offers a few concrete approaches to developing these strategy skills.

The best strategies come from proprietary insights, not generally available knowledge. So it becomes critical to continually expose yourself to proprietary information from which those insights can manifest. Whether it is insight into industry structures and trends, insights into emerging customer needs, anticipating the actions and reactions of competitors, or insight into your own competencies and resources, it becomes critical to immerse yourself in the flow of such information to be able to glean such insights.

Rumelt also suggests getting into the practice of pre-committing to a position, recording that judgment, and then running a post-mortem to help you learn whether your judgment was right. Only when we treat strategy as a set of hypotheses, experiments, and test results, can we expect to learn and improve our own strategic thinking.

Finally, Rumelt believes that good strategy grows out of an independent and careful assessment of the situation, harnessing individual insight to carefully crafted purpose. Bad strategy follows the crowd, substituting popular slogans for insights. Therefore being independent without being eccentric and doubting without being a curmudgeon are some of the most important, though difficult, traits to cultivate.

Next time you are contemplating a product's strategy, replace the laundry list of priorities with a diagnosis, a guiding policy with a source of power, and a list of coherent actions. In doing so, you'll end up with a far more focused, actionable, and effective strategy to pursue.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

When I mention Porter's Five Forces Analysis, which is one of Michael Porter's most famous strategy frameworks, some product managers remember learning about it during business school, but most haven't used it since. Given this, I wanted to provide a refresher on the framework and when its most useful, as I do think it continues to have application to today's product managers.

Porter's core thesis behind his Five Forces Analysis is that an industry's structure will determine how the economic value created by an industry is divided amongst the companies in the industry versus it's customers, suppliers, substitutes, and potential new entrants. Understanding an industry's structure will help you understand the average profitability of firm's in the industry and therefore its attractiveness.

The Five Forces

Five unique forces determine each industry's structure. The more powerful each force is, the more pressure it will put on prices or costs or both, thereby reducing average profitability.

1. Rivalry amongst existing competitors - Competitors are always eager to try to steal market share from other players. The greater the number of industry competitors as well as the greater the intensity of rivalry amongst competitors, the more the value the company generates gets competed away, either passed to buyers in lower prices or dissipated away in higher costs of competing. When there is high rivalry amongst competitors, profitability is lower as prices fall and costs increase. Price wars in particular tend to be the most value destructive and quickly become a race to the bottom.

2. Bargaining power of buyers - Customers are always happier to pay less for products and get more for their money. When buyers are powerful, they will force prices down or demand more value in the product, thus capturing more of the value for themselves. If buyer power is high, profitability is lower because prices are lower and costs are driven up. Buyers are powerful if they are large and concentrated relative to a fragmented industry.

3. Bargaining power of suppliers - Suppliers are always happier to be paid more or to deliver less. Therefore powerful suppliers will charge higher prices or insist on more favorable terms. If supplier power is high, profitability will be lower due to the increased cost of suppliers. If switching cost is high for a supplier or if there are few alternative suppliers, supplier power will be high.

4. Threat of substitution - Substitutes, which are products that meet the same basic need as the industry's product but in a different way, limit how much you can charge. If substitutes are high, profitability is lower because prices are lower and costs are higher. To asses the threat of substitutes, it's helpful to investigate whether they offer an attractive price-performance trade-off relative to the industry's product.

5. Threat of new entrants - Threat of new entrants ultimately limits how much you can charge for your product. Entry barriers protect an industry from newcomers who would add new capacity. If threat of entry is high, profitability is lower because prices are lower and costs are higher. The most common entry barriers in an industry are economies of scale, switching costs, network effects, capital investments, proprietary technology, well-established brands, prime locations, access to distribution channels, or government regulation.

Applying Five Forces

There a two primary ways to apply Five Forces Analysis. The first is to use it as a tool to understand a given industry's attractiveness. For example, companies and investors deciding whether to exit, enter, or invest in an industry find the tool hugely valuable. For product managers, this use case might apply when you are working on new product innovation and trying to decide if a potential market for a new product is worth pursuing. Similarly, when you are looking to expand your existing product into a new market or new use case, this analysis can also help you asses the attractiveness of those options.

To use the framework for this purpose, follow these steps:

1. Define the relevant industry for analysis. This isn't as simple as it seems because you'll need to decide what products & services are in scope as well as what geographies are within the relevant market. You don't want to define the market too broadly as you'll typically find that the forces differ substantially in different markets, limiting the value of the analysis.

2. Identify the players constituting each of the five forces. You may ultimately divide those players into sub-segments if the industry is large enough.

3. Assess the power of each of the five forces. Here you're looking to understand the strength of each force relative to the industry incumbents.

4. Step back and assess which forces ultimately control profitability. You'll typically find an overarching force that is driving the lion share of profitability in the industry.

The second use of Five Forces Analysis for product managers is to brainstorm a host of potential actions you can take to improve your own product strategy within the industry. While the industry structure determines the profitability of the average firm in the industry, your own relative profitability in the industry will depend on your your own strategic positioning and competitive advantage.

To leverage Five Forces Analysis to help improve your strategy, you are looking to build defenses against the competitive forces or find a position in the industry where the forces are weakest. To find these opportunities, you would ask yourself the following questions after you've conducted the basis analysis.

1. What limiting factors must be overcome to capture more of the value you create?

2. Can you position your company where the forces are weakest?

3. What's changing in the industry structure and how can you take advantage of that change?

Next time you find yourself looking to improve your product's strategic position or are putting together a strategy for a new market opportunity, I'd encourage you to take advantage of Porter's Five Forces Analysis.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

Michael Porter, long-time professor at the Harvard Business School, is often considered the father of the modern strategy field. He published his groundbreaking classics, Competitive Strategy and Competitive Advantage, in 1980 and 1985, went on to publish 20 more books, and ultimately became the most cited author in business and economics. While product managers may have heard of Porter's popular frameworks like Porter's five forces, most don't know how to leverage his ideas in their day-to-day role. This essay on understanding Michael Porter will help product managers translate Porter's teachings into actionable insights for developing their own product strategies.

A strategy explains how an organization, faced with competition, will achieve sustainable superior performance

Product managers often end up calling their product roadmap, vision, or objectives a strategy, but to Porter, a strategy is something far more specific. Porter says a strategy explains how an organization, faced with competition, will achieve sustainable superior performance. The need for a strategy arises from the fact that every organization faces competition, either from direct competitors in your industry or from substitutes the customer can use in place of your product. This reality requires you to come up with a plan to outperform your rivals or end up outperformed by them. To Porter, superior performance doesn't refer to market share but instead to profits. Superior profits result from either being able to command a premium price point for your products & services, establishing a lower cost structure than your rivals, or some combination of the two. The final critical keyword in the definition of a strategy is that it is sustainable. A strategy is not sustainable if your rival can simply copy it and thus deteriorate your advantage. And so barriers need to exist in order to ensure you can maintain your superior performance in the face of competitive rivalry.

The reality is that simply having a strategy that meets Porter's definition doesn't necessarily make it an effective one. To help guide practitioners, Porter ultimately came up with 5 tests that he believes any strategy must pass in order to be compelling. These include a distinctive value proposition, a tailored value chain, trade-offs different from rivals, fit across value chain, and continuity over time. Understanding each of these is critical to developing your own compelling product strategy.

Distinctive Value Proposition

A value proposition answers 3 critical questions for your product:

1. Which customers are you going to serve? The reality is its impossible to build a product that will serve every customer in the market well. Given this, it's absolutely critical to be intentional on what particular segment of the market you are going to go after. While not required, it's often advantageous to find a segment of customers that are overlooked or avoided by the rest of the industry.

2. Which needs are you going to meet? This will ultimately boil down to what products, features, and services you'll offer and how they will better meet the needs of your customers.

3. At what relative price will you offer your product? You'll need to decide are you offering your product at a premium price point or a discount relative to rivals. Customers who are currently underserved by rivals will be willing to pay a premium for a product that better addresses their particular needs. Customers who are overserved by rivals will switch to cheaper products that can meet their more limited needs at a more attractive price point.

An effective strategy must have a value proposition that is different from your rivals. If you are trying to serve the same customer with the same needs and sell to them at the same relative price, it'll be nearly impossible to supplant the existing incumbents in the market.

Given the vast set of customer needs in most markets, it's also important to realize that there will likely be multiple winning value propositions in the market. Not just one "best" value proposition. Take for example the fast food market. McDonald's is a winner in fast burgers. But In-N-Out thrives on slow burgers. It's customers are happy to wait ten minutes (an eternity by McDonald's standard) to get non-processed fresh burgers cooked on home-made buns. Their distinctive value props have enabled multiple winners in the market.

Tailored Value Chain

Organizations need to perform a variety of activities to deliver their product, including developing the product, managing a supply chain, operating a sales team, etc. This sequence of activities your company performs to design, produce, sell, deliver, and support its products is called the value chain.

A great strategy is one where your value chain is tailored to best meet your specific value proposition as well as is distinctive from your rivals. If the activities you perform to develop your product is the same as your rivals, your rivals could easily replicate your value proposition and there remains nothing sustainable about it.

But when you do perform unique activities to best deliver your value proposition, rivals can't easily match them because doing so will result in them no longer serving their own existing customers well. This effectively becomes the first barrier against rivals simply replicating your strategy.

Take for example Southwest, which offered a distinctive value proposition of extremely low fares compared to rivals. To accomplish this, Southwest needed to tailor its entire value chain in order to establish its superior cost structure. For example, Southwest doesn't follow the traditional hub-and-spoke model of rivals, which is necessary to enable customers to fly anywhere. Southwest instead abandoned the fly anywhere value prop and instead built out a point-to-point network. It also removed basic amenities like meals, assigned seats, baggage transfers, all in the name of faster gate turnarounds which was necessary to create its superior cost structure. This tailored value chain makes it very difficult for rivals to copy without alienating the customer base that has come to expect a lot of these traditional services that Southwest doesn't offer, thus protecting Southwest from direct imitation.

Trade-offs Different From Rivals

Trade-offs arise when strategic choices one could make are incompatible with each other. They are the proverbial fork in the road where you must choose one path over another. Product features, for example, might be incompatible: features that best meet the needs of one customer doesn't for another customer segment. Or the configuration of activities to deliver one kind of value cannot equally deliver well another kind of value. Or simply you've built a reputation or brand with a specific image and releasing a product inconsistent with that brand or reputation would hurt that image.

Robust strategies typically incorporate multiple trade-offs. It's these trade-offs that make it difficult for competitors to imitate your same strategy. But making trade-offs is hard because it requires you to say no to a certain set of customers when so many have the incorrect desire to keep their options open and attempt to serve as many customers as possible.

One of the greatest case studies on trade-offs comes from TSMC. Prior to TSMC, most semiconductors both designed and manufactured their own chips. TSMC instead made the bold decision to focus solely on being a manufacturer for other companies designs. They would not design their own chips and therefore not compete with potential customers. This made many designers of chips eager to work with TSMC and also led TSMC to build a very different value chain than its design & manufacture competitors. While we all know how successful TSMC has been with this strategy, it was this critical trade-off that set the path for their initial success and the sustainability of their strategy.

Fit Across Value Chain

Great strategies result from how well each of the activities a firm pursues align and amplify its competitive advantage. This is because activity choices are interdependent - the value or cost of one activity is often affected by the way other activities are performed. Fit amplifies the competitive advantage by lowering cost or raising customer value and price.

There are three types of fit. The first is basic consistency, where each activity is aligned with the company's value proposition and contributes incrementally to its dominant themes. The second is complementing or reinforcing fit, where real synergy is seen between activities. And finally there is substitution, where performing one activity makes it possible to eliminate another.

When you have strong fit across your value chain, it deters competition because rivals have a hard time figuring out exactly what activity they need to match because so many activities fit well together and they would have to replicate so many of your interconnected activities in order to replicate your strategy, make it far more difficult for them to do so.

Take for example, Home Depot, whose basic value proposition boils down to huge selection, everyday low prices, and knowledgeable service. The large warehouse store format they employ is essential to offering both selection and low prices. But without the third leg of the stool, excellent service, customers would have felt lost in the warehouse stores. Knowledgeable service thus fit well with the rest of Home Depot's value proposition and reinforced its other two key value proposition pillars.

Continuity Over Time

The reality is that it takes time to develop real competitive advantage, so its essential to have a consistent strategy long enough to foster the creation of competitive advantage.

Continuity of strategy helps to reinforce the company's brand, as over time customers begin to rely on the company because of the brand it has established. Continuity also fosters continuous improvement in the activities performed and improves fit across activities. Continuity also enables suppliers, channels, and other partners to bet on you, thereby improving your value proposition.

Porter believes in this day and age, organizations shift their strategy too frequently, thinking they need to in order to adjust to the latest dynamics of the market. Porter suspects though that these shifts do more harm than good as they are often following fads that do little to advance their strategy while also losing the advantages of continuity.

These five tests make up what Porter considers are the essential components of a compelling strategy. Next time you are developing a product strategy, I'd encourage you to reflect on each of these tests in order to strengthen your own strategy.

If you are interested in exploring Porter further, I'd also encourage you to read Understanding Michael Porter by Joan Magretta, which distills Porters ideas on strategy across the corpus of his published literature.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

As the product management role has matured, specialization in the role has ultimately emerged. There is no longer a single product manager with generic responsibilities, but instead 4 distinct product roles with unique responsibilities. In this video, Sachin describes each of these roles, which he calls builders, tuners, innovators, and enablers. He shares the unique responsibilities of each role, the super powers needed to excel in the role, and real-world examples of PMs in them.

Watch on: YouTube | Spotify | Apple Podcasts

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

I've long believed that focusing on improving the deliverables that product managers are responsible for is a better way to accelerate your career than simply focusing on up-skilling.

In this video, I share the 9 essential product manager deliverables and for each, describe what it is and what great looks like for that deliverable. Finally, I cover how to best make use of the list of deliverables to accelerate your own product career.

Watch on: YouTube | Spotify | Apple Podcasts

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

Despite the product management role existing now for decades, there isn't a single well accepted definition for how to define the role of the product manager.

In this video I review the most popular definitions of the role from product luminaries like Martin Eriksson, Ben Horowitz, and Marty Cagan. I also share my own definition for the role: Product managers drive the vision, strategy, design, and execution of the product. I then dive into each of those four core responsibilities, share an exemplary product leader for each, and describe what each responsibility truly entails.

Watch on: YouTube | Spotify | Apple Podcasts

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

One of the hardest skills for product managers to master is product intuition. Too often we're told it just takes time & experience to build your intuition. But I've come to believe that we can in fact accelerate the process of building our own product intuition with a tool I call Feedback Rivers.

Watch on: YouTube

In this video, I cover:

• Why building your product intuition is so important for your success as a product leader

• Why traditional methods of customer research fail to help us build our product intuition quickly

• How you can leverage Feedback Rivers to create a daily habit for building your product intuition

• How to go about building your first Feedback River in less than an hour and how to continue to improve it from there

If you want to learn how to accelerate improving your own product intuition, I'd encourage you to watch the talk here.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

Everyone knows that a critical part of developing a new product is talking to potential customers. So most product managers and founders dutifully have customer conversations as part of their product development process. But what's insidious about the way that people talk to customers is that they often fail to glean any new insights or worse, get a false positive, causing them to over invest their cash, their time, and their team in an unvalidated product idea that ultimately doesn't sell.

This happens because our potential customers are unfortunately prone to lie to us. We are partly to blame for this, since we often lead the witness to a particular conclusion when we ask a question like "do you think it's a good product idea?" Our potential customers also tend to be overly optimistic people who want to make us happy when we pose hypothetical questions to them like "would you buy a product which did X?".

Rob Fitzpatrick has written the definitive playbook on how to ask questions in customer conversations to ensure they can't like to you. He calls these techniques The Mom Test, because if you follow his approach, even your loving mom can't lie to you. I wanted to share my three most actionable takeaways from the book which you can apply to your next customer conversation.

Talk about their life instead of your idea

The most counter-intuitive advice Rob offers is to actually avoid talking about your specific product idea at all in your early customer conversations.

This is because doing so suffers from the "Pathos Problem." It happens when you expose your ego, leading people to feel they ought to protect you by saying nice things. For example, asking questions like "I'm thinking of starting a business... so, you think it will work?" or "I had an awesome idea for an app - do you like it?" will likely garner compliments from the potential customer that are impossible to trust.

Instead of this, the most useful questions we can ask in these early customer conversations is about our customers lives: their problems, cares, constraints, and goals. We should measure the usefulness of these customer conversations by whether they give us concrete facts about our customers' lives and world views.

For example, let's say we're building a phone app to help people stay in shape. Instead of launching into what the app will do and asking whether they'd use it, we should instead start by deeply understanding whether the potential customer even has the problem we are solving for. So we might ask questions like:

• "What are your big goals and focuses right now?"

• "Is getting healthier on that list?"

• "How often do you go to the gym?"

• If not often, you might ask "Why not?"

• "When's the last time you did try? Have you ever joined a gym or taken up jogging or anything?"

These questions help us to quickly understand where fitness falls in their list of overall priorities as well as their experience to date with fitness in such a way that they can't actually lie to us about it.

Ask about specifics in the past instead of opinions about the future

Rob also warns against a very common type of question that tends to get asked in customer conversations, which are hypothetical questions about the customer's potential behavior in the future.

For example, it's common to ask a question like "would you buy a product which did X?". The challenge with this question is we are asking for an opinion on an hypothetical situation from someone who is likely to be overly optimistic and want to make us happy. So we again can't trust what the customer says.

Instead we should get very specific about their past behavior, which they certainly won't lie about. We can ask how they currently solve X and how much it costs them to do so. And how much time it takes. We can ask them to walk us through what happened the last time X came up. If they haven't solved the problem, we can ask why not? Have they tried searching for solutions and found them wanting? Or do they not even care enough to have Googled for it? This line of probing on their past behavior will result in far more insights than posing questions about their hypothetical future behavior.

Validate by asking for commitments

Now as our customer conversations progress, we will ultimately need to present our specific product idea and get feedback on it. After doing so, we often hear feedback like the following that's easy to interpret as validation:

• "That's so cool. I love it!"

• "I would definitely buy that"

• "Looks great. Let me know when it launches"

The reality is none of these statements can be trusted as actual validation because they are cheap for a potential customer to say to simply make us happy.

Instead we should ask for a very specific commitment as a next step in order to get to true validation. A commitment shows that they're serious by giving up something they value, such a time, reputation, or money. Here are example commitments that you could ask for in your customer conversations:

Time commitments

• Clear next meeting with known goals

• Sitting down to give feedback on wireframes

• Using a trial of the product for a non-trivial period

Reputation risk commitments

• Intro to peers or team

• Intro to a decision maker (boss, spouse, lawyer)

• Giving a public testimonial or case study

Financial commitments

• Letter of intent (non-legal but gentlemanly agreement to purchase)

• Pre-order

• Deposit

Strong commitments will often combine multiple of these across categories. For example, agreeing to run a pilot of the product would cost them both time as well as reputation risk in introducing the product to the rest of the team.

By ending each customer conversation with an ask for a specific commitment, you get the clearest possible signal on their interest in your product. As Rob says, it's not a real sales lead until you've given them such a concrete chance to reject you. And in doing so, avoid any possibility of them lying to you.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

It's that time of year that product managers find themselves engrossed in annual planning. But the traditional frameworks PMs have come to rely on for roadmapping, like RICE, often are ill-suited for putting together a highly strategic product roadmap for the upcoming year.

I joined Reforge recently to give a talk about a new process I developed, called 4D Roadmaps, that leverages 4 distinct lenses to develop your roadmap, including a strategy lens, vision lens, customers lens, and business lens. In the talk, I share this actionable process for putting together a more strategic, aspirational, and well articulated roadmap for your product.

Watch on: YouTube

The talk covers:

• Common feedback from product leaders on what's missing from their team's annual planning process

• RICE prioritization and the limitations that prevent it from producing highly strategic and ambitious roadmaps

• The 4D roadmapping process, including a deep discussion of each lens: strategy, vision, customer, and business

• How to craft narratives around why you are prioritizing each objective to drive buy-in from leaders

If you are looking for a better way to develop your annual plan, I'd encourage you to watch the talk recording.

The content from this talk comes from my Mastering Product Management course, which dives deep into the 8 deliverables that product managers are responsible for, including roadmaps, specs, strategy, vision, and more, and offers rigorous frameworks for doing each well. Join us for an upcoming cohort. Learn more here.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

I recently joined Productboard in my home for a conversation about all things product management and innovation.

Watch on: YouTube

We covered so many topics in this 15 minute video, including:

• My favorite books on product management, including The Creative Act by Rick Rubin, Creative Selection by Ken Kocienda, and The Everything Store by Brad Stone

• The 3 types of product managers: builders, tuners, and innovators

• Limitations of the lean startup for bringing a new product to market

• How to pick the next company to join on your career journey

• How to take advantage of the recent technology waves of crypto and generative AI

• A tour of my home office, the places I do my most creative work, and plenty of cameos from my dog Dexter!

If any of these topics interest you, I'd encourage you to check out the video.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

I've always felt that product design was a creative endeavor and the work that most moves our industry forward stems from practitioners who could easily be described as artists. Given this, I've often wondered whether one of the best ways to improve our craft as product designers would be to immerse ourselves in the best practices of artists at large. I got the chance to explore exactly that when legendary producer Rick Rubin, behind iconic artists like Adele, Linkin Park, and Red Hot Chili Peppers, published his new book. In The Creative Act, Rubin shares everything he has learned about harnessing your creativity from his decades working with some of the most successful recording artists of our time.

While I found the book most applicable to the traditional realms of music, writing, and art, I thought Rubin's perspective on where creativity comes from and how one can develop taste equally applicable to product design.

Rubin quickly dispels the notion that creativity comes from original thought. He says our mind will often deceive us into thinking this is the case, but the reality is far from it. Instead the source material from which creativity emerges is always from the outside world that surrounds us, which we sense, tune into, and remember. Sometimes we actively reference this source material, but equally often it manifests in dreams, intuitions, and subliminal fragments beyond our conscious state. What make art unique is how we uniquely combine various source material with our own interpretation of it to create something entirely new.

From this insight on the source of creativity, Rubin draws two specific implications. The first is that we need to cultivate our awareness to be able to take full advantage of the source material the world has to offer. The reality though is we all have a filter through which all source material is processed before we get to appreciate it. This filter exists because our memory space is limited and we have limited processing power to take in all the information that surrounds us. So instead we learn early to focus on information that appears essential or of particular interest to us and tune out all the rest. But as an artist, "we seek to restore our childlike perception: a more innocent state of wonder and appreciation not tethered to utility or survival." He describes artists as having a greater sensitivity to their environment and their task then becomes finding a way to share what they perceive with the rest of us as art. Rubin shares a variety of techniques for cultivating this, from not holding our belief systems too dearly, to learning to notice our own emotional state as we consume art, to actively changing our perspective to try to see something new. This all is in an effort to increase of aperture and perceive far more signal from the creative sources out there.

For product designers this means cultivating your awareness as you engage with every product around you. Not only other products in your chosen field, but each and every product you come across as you simply go about your life. Product designers who have cultivated this sensitivity can point out flaws or trade-offs made in each and every product they come across. They are also equally adept at noticing when something is a particularly pleasurable product experience and how that experience made them feel. It's important to note that often this awareness might not be easily communicated in words. For example, you might not be able to describe exactly why something is particularly pleasurable or not, but building that awareness even without the words can still improve your sensitivity at a subconscious level.

The second major implication of Rubin's insight into the source of creativity is how we should think about what we consume. Rubin encourages us to submerge ourselves in the canon of great works, whether its "reading the finest literature, watching the masterpieces of cinema, or getting up close to the most influential paintings." There is no standard list for this canon as its continually changing with the times, so you do have to do some leg work to put it together. He emphasizes that the point of doing so is not to strive to imitate the best, but instead to increase our own sensitivity to what greatness looks like. He is essentially describing using this as a method for developing our taste.

As I think about how to apply this to my own role as a product designer of digital products, I find myself thinking about where could I find the canon of great works in my field. Maybe it's immersing myself in the Apple Design Awards winners, which are the most critically acclaimed apps chosen by Apple. Or diving deep into the all-time top upvoted apps on Product Hunt. Or diving into the product experiences of the fastest growing B2B apps. All of these efforts will certainly help me cultivate my sense of taste which becomes critical as I embark on bringing my own creative product designs to market.

Immersing myself in the practices of traditional artists was an eye opening experience that gave me a new perspective on my own work as a product designer. You might find similar inspiration from reading Rick Rubin's Creative Act.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

I recently had the opportunity to sit down with Fareed Mosavat, Chief Development Officer at Reforge, for a discussion on how today's product builders find product/market fit.

Watch on: YouTube

We started by pointing out the shortcomings of the Lean Startup methodology, which remains the most popular approach to finding product/market fit today. We then discussed some of the more modern approaches that seasoned product builders have been leveraging to guide their path to product market fit, including:

• Running successful customer discovery interviews that avoid the typical trap of confirmation bias

• Deeply exploring the idea maze up-front to avoid unsuccessful paths

• Leveraging a portfolio of validation techniques beyond simply building MVPs

• Rigorously measuring product/market fit via retention & growth

• Growing users via a two-prong approach of generating early traction and then developing a sustainable growth loop

If any of these topics interest you, I'd encourage you to check out the recording of the talk.

And if you'd like to go even more in-depth on each of these topics, check out my course, Product Innovation Strategy.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.

The hardest part of bringing a new product to market is always the elusive hunt for product/market fit. Marc Andreessen describes product/market fit as "being in a good market with a product that can satisfy that market". I've dedicated my entire career to five such hunts across the three startups I co-founded as well as the new products I built at LinkedIn and Microsoft. Despite sounding so simple, I have plenty of scars from failing to find product/market fit, but also from the long and winding path it ultimately took to get there.

In 2007 I founded my first startup, Anywhere.FM, which developed a web music player that allowed you to upload your entire music collection to the web and then stream it from anywhere. Influenced by being an early Y Combinator company, we leveraged the emerging Lean Startup methodology popularized by Eric Ries. The methodology was a direct reaction to the failures of dot com era startups, including their focus on putting together elaborate business plans and enduring long product development cycles before launching to customers. Instead the Lean Startup encouraged launching a minimum viable product (MVP) as quickly as possible to maximize customer feedback from real users. So we did just that: hurried an MVP to market in a matter of months and got tons of early feedback from customers. But despite doing this, garnering glowing press reviews, and growing to over 100,000 users, we still ultimately failed to find PMF because we were never able to find a viable business model for our product. While we were ultimately acqui-hired by imeem for the impressive tech we had developed, our dreams of building an independent web music player were over.

When we were working on Anywhere.FM, we came across another small music startup founded by Daniel Ek in Sweden. We didn't think much of the startup at the time because they had spent two years just building their product before ever launching it. Given their slow pace of innovation, which contradicted the principles of Lean, we assumed they wouldn't amount to much. But as we know, Daniel Ek's little startup Spotify went on to revolutionize the entire music industry and popularize the music streaming paradigm we all take for granted today.

The experience of failing on Anywhere.FM while watching Spotify's meteoric rise led me to become obsessed with what separates those that find product/market fit from the rest? This obsession not only grew my desire to build a successful startup, but also to find a better process for doing so. I wondered whether there was something beyond the Lean Startup that would increase the odds of finding product/market fit on my next venture. I began experimenting with all sorts of alternative techniques in my next venture, Connected, which was ultimately acquired by LinkedIn. At LinkedIn I got the opportunity to incubate a new premium subscription for sales professionals, called LinkedIn Sales Navigator, where I continued to iterate on practices for finding product/market fit, this time for expanding the product portfolio of an established tech giant. When looking back at Sales Navigator, its the most successful new product I've gotten to build, now generating over $1 billion in annual sales for Linkedin. But more importantly, the process we leveraged to get there looked entirely different than Lean.

In this new course, we introduce the Deliberate Startup methodology, a modern approach to finding product/market fit that solves the top challenges with the Lean Startup. In particular, we address the following shortcomings of Lean:

• While Lean focuses you on agnostic experimentation, the Deliberate Startup encourages you to start with a thoughtful plan. We'll help you to document your initial product strategy in a short PMF narrative document that lays out each of your key assumptions across 6 critical dimensions.

• While Lean focuses you on launching successive MVPs, the Deliberate Startup encourages you to leverage a much wider set of validation techniques to more effectively validate your product idea. We'll help you identify your product's riskiest assumptions and then tailor your validation to specifically de-risk them.

• While Lean glosses over specific metrics for measuring PMF, the Deliberate Startup provides actionable and clear metrics for measuring PMF and then specific guidance on what to do next given where you are in your PMF journey.

• While Lean over-emphasizes product development over sales & marketing, the Deliberate Startup encourages you to focus on traction development alongside product development. We'll share specific strategies for determining your initial short-term traction channels as well as approaches for establishing your first growth loop for sustainable growth.

Despite the fact that seasoned product builders have all moved beyond the Lean Startup, it still remains the most popular approach leveraged by first time entrepreneurs today. With this course, we hope to offer an actionable alternative that increases your odds of finding product/market fit on your next venture.

This program is a 4 week, part-time, virtual course available globally on Reforge. It's targeted at product builders, which includes not only product managers, but also the engineers, designers, and marketers on the founding team. It's also equally applicable across early-stage startups as well as established organizations looking to expand their product portfolio.

Join us for the upcoming cohort of Finding Product/Market Fit starting the week of March 20.

Whenever you’re ready, here are 3 ways I can help:

1. AI Productivity: Learn how leading product managers use AI to become faster, smarter, and gain super powers beyond their traditional role.

2. Mastering Product Management: Accelerate your product career by learning rigorous frameworks for each PM deliverable, from crafting a strategy to prioritizing a roadmap.

3. Product Innovation Strategy: Building a new product? Learn how to leverage the Deliberate Startup methodology, a modern approach to finding product/market fit.