There’s no denying it—AI is reshaping the landscape of technology, and with it, the roles that drive innovation. Among these roles, the AI Product Manager has emerged as one of the most sought-after positions in the industry. Companies are eager to find talent that can define the requirements for complex products and features that scale AI systems and push the boundaries of innovation.
But here’s the thing: despite all the buzz, the core of AI product management isn’t as new or different as it might seem. The fundamentals of product management remain the same—building great products that solve real problems for customers. What’s changed are the tools and technologies you’ll need to use to bring those AI products to life.
Having led product teams at Amazon, Chewy, and now Udacity, I can tell you that the foundations of product management haven’t changed. What has shifted is the landscape—where AI technologies introduce new possibilities and challenges. If you can adapt to these changes and learn the right skills, you’ll be well-positioned to succeed in the field.
So, what does this mean if you’re looking to step into the role of an AI Product Manager? Let’s break it down.
What Hasn’t Changed: The Fundamentals of Product Management
At its core, product management is still about solving problems and delivering value to customers. Whether you’re building traditional products or AI-driven solutions, the key elements of your role remain the same. You need to deeply understand your customers and their pain points, determine a roadmap that solves their needs and drives the business forward, prioritize that roadmap, and guide your team through the development process to deliver a product that hits the mark. Then track your product performance, learn from it, and improve.
The essence of product management—like customer focus, data-driven decision-making, and iterative development—still forms the foundation of your work. You’re responsible for building a product vision, aligning your team around that vision, and driving execution. These are the pillars that will continue to support your success, whether you’re working on legacy or AI products.
For three years I led the mobile product team for Amazon Halo, Amazon’s entry into the health and wellness space with a wrist-worn wearable device. My remit was broad. Not only did I own the app platform, but I also owned the development of our AI and Computer Vision based feature, Body, a tool that measured body composition using the smartphone camera. My approach to product development was consistent across product areas. For example, I used customer feedback and my understanding of the business needs to define what was required for onboarding workflow. Similarly, I used customer feedback and business needs to define the requirements for building the customer experience that enabled us to measure body composition.
What Hasn’t Changed: The Soft Skills of Product Management
The soft skills that define a great Product Manager are just as important in AI product management as they’ve always been. Communication, leadership, and empathy remain at the heart of your role, enabling you to bridge the gap between technical teams and business stakeholders.
As an AI Product Manager, you’ll need to articulate complex technical concepts in a way that’s accessible to non-technical stakeholders. You’ll be the one who ensures that everyone—from engineers to executives—understands the product vision and works towards the same goal. Leadership is key, as you’ll guide cross-functional teams through the challenges of AI development, ensuring that everyone stays aligned and motivated.
Empathy, too, is crucial. Great Product Managers understand their customers deeply—they know their pain points (even the ones that their customers may not yet realize!), their desires, and their motivations. In the context of AI, this user-centric approach is more important than ever. Your challenge will be to ensure that AI-driven products don’t just meet technical specifications or the whims of board members but also deliver real, meaningful value to the customers who use them. For example, when I was with Amazon Kids, I helped define the requirements for a new user interface that was personalized for each customer through machine learning based recommendations. Not only did this resonate with my leadership team who saw value in scaling our user interface to support more use cases, but it also increased engagement among our customers by making our content more discoverable.
What’s New: The Hard Skills of AI Product Management
While the fundamentals and soft skills remain consistent, the hard skills required for AI product management are where things get interesting. To excel in this role, you’ll need to get comfortable with AI technologies, machine learning fundamentals, and data science concepts.
AI products differ significantly from traditional software. They’re built on algorithms that learn and adapt over time, require massive amounts of data, and often involve complex technologies like deep learning and natural language processing. As an AI Product Manager, you don’t need to be a data scientist, but you do need to understand how AI models work, how to evaluate their performance, and how to leverage existing data to create AI-driven product features. More than anything, you need to be curious. You should be interested in the technology your team is building and hungry to learn. I’ve made a habit out of getting closer to my engineering and science partners throughout my career. A good way to do this is to schedule a dedicated hour-long session with your technical team and have them whiteboard the technology stack for you. What data is required? Where is it coming from? How are you processing it? What are the constraints? Getting familiar with your own tech stack will help you define better requirements and understand technical trade-offs.
AI can be a loaded term, so it’s important to unpack what it means with respect to product development. Understanding machine learning basics is a good start. You’ll need to know how to frame problems that AI can solve, work with data teams to gather and prepare the data needed for training models, and interpret the results of those models. This means getting comfortable with concepts like training data, model accuracy, precision and recall, and overfitting.
You’ll also need to be familiar with data annotation and how to develop a roadmap for creating bespoke AI products. Data is the fuel that powers AI, and as a Product Manager, you’ll need to ensure that your AI models are trained on high-quality data that accurately represents the problems you’re trying to solve.
Modern AI technologies like large language models (LLMs) and Generative AI are also reshaping the landscape. Knowing how to integrate these tools into your product strategy will set you apart as a forward-thinking AI Product Manager. Whether it’s as simple as incorporating AI chatbots into your customer service platform or using GenAI to create personalized user experiences, these technologies are becoming crucial parts of the AI product management toolkit.
The pace of change in AI is rapid, and the best AI Product Managers are those who stay curious, continuously learn, and adapt to new technologies. By mastering these hard skills, you’ll be equipped to navigate the complexities of AI product management and deliver innovative solutions that make a real impact.
Ready to Become an In-demand AI Product Manager?
If you’re excited about stepping into this role, the good news is that you can learn everything you need through Udacity’s newly updated AI Product Manager Nanodegree program. The curriculum covers everything from understanding the basics of AI and machine learning to developing a strategic vision for AI products.
You’ll learn how to work with data teams, how to evaluate AI models, and how to integrate AI technologies into your product roadmap. And with the program’s hands-on projects, you’ll get the practical experience you need to apply these skills in the real world. I can’t overstate the effectiveness of using real-world projects to build these skills. By the end of the program, you’ll have the confidence and expertise you need to lead AI product initiatives that drive innovation and deliver real value to your organization.
