Dr. Warren Reed: You're a successful marketing professional. You know how to build a brand, you know how to connect with a customer. But what happens when your next product isn't a thing, but a thought? When it's not a feature, but a prediction? How do you manage a product that learns?

Mitchell: That question is everything, Warren. It's the question that keeps me up at night, but in an exciting way. It feels like standing at the edge of a new frontier where all my skills are still relevant, but the language and the landscape are completely different.

Dr. Warren Reed: Exactly. That's the central challenge for anyone moving into AI Product Management, and it's why we're diving into Michael Pejner's book, "The AI Product Manager," today. It's a fantastic field guide for this new frontier. And Mitchell, with your deep experience in marketing and your ambition to make this exact transition, you're the perfect person to explore this with.

Mitchell: I appreciate that. I'm ready to get my hands dirty. I've spent years figuring out what customers want, and now I need to figure out what the wants, or rather, what it can tell us.

Dr. Warren Reed: A perfect way to put it. So, we'll tackle this from two angles today. First, we'll explore that crucial mindset shift—redefining what a 'product' even is in the world of AI. Then, we'll break down the essential, non-technical toolkit you need to build to effectively lead AI projects and teams. Sound good?

Mitchell: Let's do it. I'm ready.

Dr. Warren Reed: Alright. Let's start with that mindset shift. Mitchell, from your marketing perspective, a product has a clear value proposition, features, a user. You can touch it, or at least clearly define its function. The book argues an AI product is fundamentally different. It's probabilistic. It's not always right. How does that idea land with you?

Mitchell: It's a little unsettling, to be honest. In marketing, predictability is king. We run a campaign, we expect a certain range of results. If a feature on our website is supposed to take a user from A to B, it needs to do that 100% of the time. The idea of launching a product that is, by design, sometimes wrong... that's a big mental hurdle.

Dr. Warren Reed: It is. And that's the first major lesson from the book. You have to stop thinking about the product as a static object and start thinking of it as a living system. The classic example is Netflix's recommendation engine.

Mitchell: Of course. The thing that knows what I want to watch better than I do.

Dr. Warren Reed: Precisely. Now, most people think the product is the library of movies and shows. But the book clarifies that the product, the one that creates immense value and retention, is the "what you should watch next" prediction. The job of the AI Product Manager at Netflix wasn't just to get more content, but to improve that prediction.

Mitchell: So, how did they do that?

Dr. Warren Reed: Through data. The PM's focus shifted to acquiring the right data. It wasn't just what you watched. It was your entire behavior. What you rated, what you abandoned after five minutes, what you re-watched, what time of day you watched it, on what device. All of that data became the raw material. The product's quality wasn't measured in uptime or bug counts, but in predictive accuracy. The product the prediction.

Mitchell: Wow. Okay, that clicks. So, my job as a PM would shift. It's like... we're not just selling the car anymore. We're selling the 'best route to work' service that the car's navigation system provides. And that service only gets better with more data—traffic patterns, road closures, my own driving habits.

Dr. Warren Reed: You've got it. The data itself becomes the core asset, more valuable than the physical hardware it runs on.

Mitchell: That completely reframes the job. My guiding question changes from 'What features does the user want?' to 'What data do we need to answer the user's next question before they even ask it?' It's a move from being reactive to user needs to being predictive of them.

Dr. Warren Reed: And that has huge implications. It means your roadmap isn't just a list of features to build. It's a list of hypotheses to test. 'We believe that by incorporating location data, we can improve our restaurant recommendations by 15%.' The work becomes a series of experiments.

Mitchell: Which, in a way, is very similar to marketing. We run A/B tests on ad copy all the time. 'We believe this headline will get more clicks.' The difference here is the scale, and the fact that the system itself is learning and changing, not just the user's response. It's a dynamic feedback loop. That’s a huge shift in thinking.

Dr. Warren Reed: Exactly. And to manage that dynamic feedback loop, you need to lead a very technical team of data scientists and machine learning engineers. This is the part where many aspiring PMs from business backgrounds get nervous. They think, 'I can't code, I can't build a neural network, so how can I possibly lead these people?'

Mitchell: You're reading my mind. That's the single biggest point of anxiety for me. Impostor syndrome is a real threat.

Dr. Warren Reed: Well, the book has a very empowering message on this. It argues you don't need to be a coder; you need to be a translator and a strategist. This brings us to our second point: the essential, non-technical AI PM toolkit.

Mitchell: Okay, I'm all ears. What's in this toolkit?

Dr. Warren Reed: The book highlights many skills, but let's focus on two that are critical for someone with your background. First: Data Strategy and Intuition. Second: Technical Literacy.

Mitchell: Let's break those down. Data Strategy. What does that mean in practice?

Dr. Warren Reed: It means going beyond just knowing you need data. It's about asking the hard, strategic questions before a single line of code is written. Is this the data for the problem we're solving? Is the data clean and reliable? And most importantly, is there hidden bias in it?

Mitchell: Bias. That's a huge topic.

Dr. Warren Reed: Massive. And the AI PM is the first line of defense. The book gives a powerful, now-classic, case study of a major tech company that built an AI tool to screen résumés. The goal was to make hiring more efficient. But the tool ended up penalizing résumés that included the word "women's," as in "women's chess club captain," and it downgraded graduates of two all-women's colleges.

Mitchell: Oh, no. Why would it do that?

Dr. Warren Reed: Because it was trained on the company's own hiring data from the previous 10 years. And that data reflected a reality where the company had predominantly hired men. The AI learned that male-associated patterns were a predictor of being hired, and it amplified that historical bias. The model was working perfectly based on the data it was given, but the outcome was a disaster.

Mitchell: So the AI PM's job is to have the foresight to ask, 'Wait a minute, what biases exist in our historical data, and how do we prevent our new product from inheriting them?'

Dr. Warren Reed: Precisely. It's a strategic and ethical responsibility. The PM is the voice in the room that represents the user and society, asking those questions that a pure engineer might not think to ask.

Mitchell: That resonates with me deeply. As an ENFJ, a 'Protagonist' type, I'm driven by that sense of purpose and social good. In marketing, we talk about inclusive messaging and representing diverse audiences. This sounds like the technical-stack equivalent of that. My role isn't just to define the 'what' for the product, but to champion the 'how'—how we build it responsibly.

Dr. Warren Reed: That's the job. Now for the second tool: Technical Literacy. Notice I'm not saying 'technical expertise.' The book is clear: you don't need to be able to write Python code. But you do need to learn the vocabulary of data science to communicate effectively.

Mitchell: The new language for risk management, as I was thinking earlier.

Dr. Warren Reed: Exactly. It's about understanding core concepts. For example, 'precision versus recall.' In simple terms, let's say you're building an AI to detect spam emails. Precision asks: of all the emails we marked as spam, how many were actually spam? High precision means you don't have many false positives—you're not accidentally putting your mom's email in the spam folder.

Mitchell: Okay, that makes sense. Don't want that.

Dr. Warren Reed: But then there's recall. Recall asks: of all the actual spam that came in, how much of it did we successfully catch? High recall means you're catching almost all the junk, but you might have more false positives—your mom's email might get caught in the net.

Mitchell: Ah, so there's a trade-off. You can't always have both.

Dr. Warren Reed: You almost never can. And that's not a technical decision; it's a decision. As the PM, you have to decide what's more important for your user experience. Is it worse to miss a real email, or to see a few spam messages in your inbox? Your job is to have that conversation with your data scientists. You don't tell them to tune the model, but you tell them you're optimizing for. You translate business risk into technical spec.

Mitchell: That is incredibly empowering. It's not about me pretending to be an engineer. It's about me being a really, really good product manager who understands the unique constraints and levers of this new type of product. My job is to provide the context, the 'why,' and the definition of 'good,' so the brilliant technical minds can figure out the 'how.'

Dr. Warren Reed: And that is the perfect summary. So, as we wrap up, the journey from a marketing pro to an AI product leader, based on Pejner's book, is really a two-part transformation. First, you have to shift your entire mindset. You're no longer managing static features; you're cultivating a living, data-driven system.

Mitchell: Right. The product is the prediction.

Dr. Warren Reed: Second, you build your toolkit. Not by becoming a coder, but by becoming a master translator, a data strategist, and the ethical conscience of the team. You learn the language of data science so you can ask the right questions and set the right goals.

Mitchell: It feels so much more achievable when you frame it that way. It’s not about abandoning my old skills; it’s about applying them in a new, more complex context.

Dr. Warren Reed: So, for you, Mitchell, and for everyone listening who's in a similar position, what's the immediate, actionable first step?

Mitchell: You know, after this conversation, it’s crystal clear to me. The first step isn't to go sign up for a Python course, which was my initial instinct. The first step is to start training my own 'data intuition.'

Dr. Warren Reed: How would you do that?

Mitchell: I'm going to start looking at the products I already use every single day—Spotify's Discover Weekly, the route Google Maps picks for me, the things Amazon recommends I buy. And for each one, I'm going to try to reverse-engineer their product strategy. I'll ask myself: What data are they collecting from me right now? What question are they trying to answer for me with that data? And what's the business value of getting that answer right?

Dr. Warren Reed: A data diary. I love it.

Mitchell: Exactly. It's about learning to see the world through an AI PM's eyes. I think that's the most powerful and accessible first step anyone in my position can take. It’s not about the code; it’s about the curiosity.

Dr. Warren Reed: Curiosity and a new way of seeing. A perfect place to end. Mitchell, thank you for walking through this with us.

Mitchell: This was fantastic, Warren. I feel like I have a real map now. Thank you.