Updated: May 19, 2025- 17 min read
The AI Product Manager role drives me crazy. I don’t really believe in it. I know that may come as a surprise, given that AI Product Manager is right in the title of this article! And if you go on LinkedIn, you’ll see plenty of companies hiring for the role. But here’s the thing: all Product Managers are AI Product Managers. If they’re not, they’re already behind (and better catch up soon).
I host The Product Podcast, where I get to interview the brilliant minds behind products like Slack, Calendly, and HubSpot. We talk in nearly every episode about how all types of Product people are using AI, but we don’t talk about AI PMs. We don’t need to—at this point, at top companies, every Product Manager is up to their elbows in AI: using it to prototype, integrating it into existing products, and building brand new features that leverage AI in a way that only their products can.
According to McKinsey, Gen AI has increased Product Manager productivity by 40%. PMs who are adopting AI now will replace PMs who don’t. Why? Because Product Managers who use AI are able to do more, do it faster, and do it better than PMs who don’t. Keep reading to find out how.
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Why Product Managers Need to Understand AI and ML
Product managers must appreciate the potential, and potential pitfalls, of AI and machine learning to drive innovation and competitive advantage. Understanding these technologies empowers PMs to create adaptable, high-performance products that meet dynamic market demands and provide exceptional user experiences.
What Sets AI/ML Products Apart?
Artificial Intelligence (AI) broadly aims to mimic human thought, while Machine Learning (ML), a subset of AI, focuses on learning from data. ML products can be central to their function, like Google's search or GPT models, or enhance experiences, such as Amazon’s recommendations.
Features that Define ML Products:
Machine learning (ML) products, a subset of AI products, are unique because they evolve with the data they process, unlike traditional software, which is deterministic and based on set algorithms. This dynamic nature makes the development process for ML products continually adaptive.
High-Performance (the “wow” factor): ML products deliver exceptional user experiences, providing capabilities that traditional methods cannot achieve.
Adaptability: These products adjust to changes in user behavior and conditions, maintaining their effectiveness over time.
Scalability: ML products can expand to serve broader audiences without losing performance.
Competitive Advantage: Mastering ML can give a business a sustainable edge in the market.
Challenges for AI/ML Product Managers
Specialized Knowledge: Developing ML products requires skilled data scientists and engineers. AI Product Managers need to have and maintain a deep understanding of data science in order to effectively guide the development of AI-powered products.
Infrastructure Demands: ML models need significant computational resources and data storage for large-scale deployment.
Development Cycles: ML products often have longer development timelines, requiring careful planning and patience.
Transparency Issues: The complex nature of ML models can make them difficult to explain and justify.
Ongoing Maintenance: Continuous monitoring and updating are essential to keep ML products effective and relevant.
Artificial Intelligence Product Manager: Role and Responsibilities
What are the responsibilities of AI PMs? As I said above, the same as any other Product Manager! So-called AI Product Managers are doing what any product manager should do: leveraging the most impactful technology and strategies to develop and deliver outstanding products.
Developing AI Products
At the heart of AI product management lies the challenge of transforming cutting-edge technologies into user-friendly, market-ready solutions.
AI PRD Template
Plan, strategize, and align stakeholders around the key requirements unique to AI products.
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Developing AI products involves incorporating different types of AI technology:
Generative AI: Large Language Models (LLMs), like GPT-4 or Google’s Gemini, which are capable of understanding and generating human-like text.
Multimodal Models: Combine text, images, audio, and other data types to enhance AI capabilities, providing richer interactions and more intuitive user experiences.
Autonomous Agents: AI systems capable of independently planning, making decisions, and performing tasks without continuous human oversight, improving productivity and automation.
Advanced Retrieval-Augmented Generation (RAG): Enhances AI accuracy by dynamically retrieving and integrating real-time external knowledge, ensuring contextually relevant responses.
Synthetic Data Generation: Produces artificial yet realistic datasets to accelerate AI training, especially useful where real-world data is limited, sensitive, or subject to privacy constraints.
AI-Driven Cybersecurity: Utilizes machine learning for proactive threat detection, anomaly identification, and real-time response to strengthen security and prevent cyberattacks.
AI product strategy
AI product strategy involves several layers:
Identifying suitable AI applications requires a deep understanding of both the business context and the potential of AI to drive innovation and solve specific challenges.
Defining product features involves translating AI capabilities into functionalities that deliver tangible value to end-users while ensuring seamless integration into existing workflows.
Overseeing the model training and deployment process is crucial to ensure that AI models are not only accurate and reliable but also scalable and optimized for real-world performance.
Collaborating closely with data engineers to leverage data science principles such as bias mitigation, data integrity, and model interpretability is crucial for translating complex AI capabilities into user-friendly, ethical, and valuable product features.
Leveraging UVPs in AI Products
The AI product you should build tomorrow depends on what your company does well today. Whether it's leveraging proprietary data or subject matter expertise, the Unique Value Propositions of AI products ideally build on the existing products, data, or market position.
AI in Product Management Workflows
AI is not just the end product; it’s also a powerful tool that AI PMs utilize in their workflows to enhance decision-making and streamline processes. Here’s how AI can revolutionize various aspects of product management:
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Session Replays: AI PMs use AI-powered products to analyze session replays, providing deep insights into how users interact with their products. These tools can automatically identify patterns, pain points, and areas for improvement by interpreting vast amounts of user behavior data.
Turning User Feedback into Feature Ideas: AI goes beyond merely summarizing user feedback; it can prioritize feature development by analyzing trends and sentiments within the feedback. This ensures the product roadmap is based on the most valuable and desired features.
Turning Data into Insights: With AI, product managers can interact with data in intuitive ways, without needing to run SQL queries or complex commands. This democratization of data analytics empowers PMs to make data-driven decisions swiftly and efficiently.
There are a lot of AI products for Product Managers and other AI tools that can be implemented today in the life of any PM to make them an AI PM.
Using AI tools
There are a lot of AI products for Product Managers and other AI tools that can be implemented today in the life of any PM to make them an AI PM.
Some of my favorite use cases include:
AI prototyping with Vercel
Turning session replays into insights with LogRocket
Connect user feedback to feature ideas with Productboard
Chat with your data with Mixpanel
Build surveys and gather insights with Sprig
Accelerate product development with Airfocus
Check out our latest Proddy Award winners for more ideas.
Essential Skills for AI Product Managers
The AI product manager job description is as varied as any other focus area. Each day comes with its own challenges, many of which depend on the type of products and features that are in the pipeline. McKinsey identifies four critical skills that product managers need to develop AI products successfully:
1. Low-Code Prototyping
As generative AI tools become increasingly sophisticated, Product Managers need proficiency in low-code and no-code development platforms. Mastery of these tools allows PMs to quickly prototype, experiment, and iterate, significantly accelerating product development cycles.
2. Agentic Framework Planning
Future-facing Product Managers must also understand "agentic frameworks," systems where multiple large language models (LLMs) collaborate to complete complex tasks autonomously. This skill involves strategic planning around the coordination, orchestration, and integration of various AI agents to achieve goals more efficiently than a single model could alone.
3. Empathy & Trust-Building
In a world where generative AI increasingly interacts directly with users, empathy becomes a vital skill for Product Managers. This involves deeply understanding user perspectives, fears, and hesitations around AI-driven solutions, including both explicit concerns and implicit barriers to adoption. By practicing empathy, PMs can proactively address these issues, design interfaces and experiences that foster trust, and ultimately create AI products that resonate with users and encourage confident engagement.
4. Risk Management & Compliance
As the complexity of AI-powered products grows, so does the importance of comprehensive risk management and compliance. Product Managers must closely collaborate with risk specialists and compliance experts to embed rigorous governance and safety measures throughout every stage of the Product Development Lifecycle (PDLC). This proactive approach ensures ethical usage, legal compliance, and robust risk mitigation—protecting users, maintaining brand integrity, and securing sustained product success in a rapidly evolving regulatory environment.
Other key AI product management skills include:
Understanding the tech
To be effective in directing and guiding your team, you should grasp the basics of how algorithms work, the data science principles involved, and the challenges in training and deploying models. The clearer you are about the mechanics of ML, the better you can steer the product direction, set realistic expectations, and handle stakeholder interactions
Leveraging data science
When solving business problems with AI and machine learning, we encounter various challenges. The first challenge revolves around data accessibility. Do you have access to the data you need? Is it labeled and ready? Relevant and up-to-date data is crucial for accurate predictions, but not all data at your disposal may be pertinent to the specific problem you're attempting to solve.
Gathering feedback
In the context of machine learning, gathering customer feedback becomes even more crucial due to the probabilistic nature of machine learning systems. Probabilistic systems inherently require more data to enhance the quality of their outputs over time. The challenge lies in how to effectively collect this valuable data and create customer feedback loops.
One effective approach to gathering feedback is by embedding a feedback mechanism directly into the core customer workflow. For example, Uber prompts its customers for feedback every time they log into the app. By doing so, Uber can accumulate a wealth of valuable customer signals. If a customer expresses dissatisfaction with the number of pickups, Uber can use this feedback to optimize its pickup algorithm.
Apply UX best practices to AI products
“On the bus, everyone gets the same stops - the same predetermined experience. In a taxi, each user puts in their own destination, and the driver creates a personalized journey for them. Similarly, products will evolve from being deterministic as they are today, to non-deterministic, allowing for a range of different experiences for every user.”
– Sam Stevens, AI Product Leader & CEO at CatalistAI. Ex-Google, YouTube, Tinder
Many AI systems influence user experiences in unconscious ways.
A practical illustration of this concept can be seen in search engines. Consider two users searching for the same word, "shoes." While one user is shown men's shoes, another is shown women's shoes. This difference is due to the AI tailoring the results based on the user's gender information, creating an unconscious experience change.
As a product manager, you're leveraging user information to enhance their experience without requiring explicit input. However, it's crucial to consider what information you use to improve the customer experience and be mindful of potential privacy concerns and ethical considerations.
To guide user actions effectively, you must design your interface to nudge users in the direction you want them to take. This involves simplifying the user interface and ensuring there are at most one or two clear actions for users to follow. By being opinionated and aligning the interface with your desired outcomes, you can enhance the user experience and achieve your product's objectives.
Collaboration with cross-functional teams
As a PM, you’re already accustomed to working with multiple stakeholders, but when dealing with AI products, this complexity increases significantly. In a traditional product management lifecycle, you collaborate with engineering, UX, customer success, marketing, and possibly sales teams to define requirements and iterate to build and ship the product.
When you're working on AI products, numerous new teams come into play. These teams may include data scientists, data engineers, machine learning scientists, machine learning engineers, applied scientists, and business intelligence professionals. This influx of stakeholders adds complexity to the product development process, as coordination and communication demands increase. The coordination time with these teams rises significantly, impacting the time required for product iterations.
In AI product development, if you receive feedback that necessitates changes to the AI models, the process can reset. This involves revisiting data requirements, addressing missing data, model building by the science team, and collaborating again with engineering for deployment. Therefore, more stakeholders, increased complexity, and longer iteration cycles are characteristic of AI product development.
How to Become an AI Product Manager
Eager to venture into ML/AI product management? The route isn't set in stone. Whether you're tech-savvy, data-oriented, or business-driven, there's room for you. To begin, dive into data, grasp AI/ML basics, partner with data scientists, and prioritize user needs.
1. Immerse yourself in an AI-driven company
To truly grasp artificial intelligence, immerse yourself in an innovative company. Leading AI companies offer rich learning experiences and mentorship. Begin as a PM, show interest in AI, and soon you'll be collaborating with data scientists and shaping AI advancements.
2. Advocate for AI within your current role
Why seek externally when you can pioneer internally? If your current role doesn't involve AI, introduce it. Find areas where AI can enhance value, research thoroughly, and pitch with conviction. This not only highlights your leadership but also positions you as the company's AI expert. Your initiative might spur new projects or even spawn dedicated AI teams.
3. Forge your own path with an AI startup
If you're entrepreneurial and see an AI solution to a unique problem, consider starting a venture. Assemble a team and create from the ground up. This challenging route provides deep insights into AI, business strategy, and fundraising. Leading your own startup gives you the chance to directly influence its trajectory and leave a lasting impact in the AI realm.
Whichever path you choose, remember that the world of AI is vast and ever-evolving. Continuous learning, curiosity, and adaptability are your best allies. Dive in, embrace the challenges, and watch as you transform from a product manager into an AI Product Management maestro. Your journey begins now!
4. Learn how to master AI products from an experienced instructor
Breaking into AI Product Management requires a unique set of skills, focusing on generative AI, data-driven decision-making, and user experience innovation. It goes without saying that artificial intelligence is a complex topic to understand, let alone apply to AI products. Honing your skills through a course or certification is a great way to set yourself up in the world of AI product management.
Get acquainted with the foundational concepts through a free micro-certification or specialize under the tutelage of an experienced AI product expert with Product School’s Artificial Intelligence Product Certification (AIPC)™.
AIPC™ is meticulously designed to empower you with these skills and help you level up as an AI Product Manager. Discover AI fundamentals, build cutting-edge AI products, craft superior user experiences, optimize product performance using AI, and much more. Our certification is your gateway to becoming an AI Product Manager.
During AIPC™, students have a chance to build a low-code LLM-powered app, and everyone leaves the course with a fully-fledged AI PRD for their portfolio.
AI Product Manager Job Description
Product Managers are becoming much more adept at AI with every passing year. A 2025 survey from McKinsey revealed that employers find it easier to hire AI product managers now than in previous years.
So what does the job entail? Every AI product manager position is different, but after combing through the recent offerings on LinkedIn, this one stood out for its averageness:
What You’ll Do
You will drive the product vision, strategy, and roadmap.
Partner closely with the engineering, sales, marketing, and customer success teams to deliver AI products and ensure their market success.
Your simple and elegant designs will delight our customers as they work with their unstructured data.
Work with customers to deeply understand their needs and bring them into the ideation process.
Perform research and competitive analysis to identify opportunities within the realm of multimodal AI.
What We Look For
4+ years of experience as a product manager.
Advanced degree in Computer Science, AI, Machine Learning, or related fields is a nice to have.
Strong understanding of data and AI technologies, including, but not limited to multimodal learning, natural language processing, computer vision, and reinforcement learning.
Excellent communication skills: ability to present and explain decisions cross-functionally.
Demonstrated ability to work with complex problems yet design powerful and simple solutions.
AI Product Manager salaries
The salary for the position described above (based in San Jose, CA) is listed between $142,000-$179,000 per year. According to Glassdoor, the average AI PM salary ranges from $148,000 to $225,000 per year.
Preparing for an AI/ML PM interview
Ok, so you've applied to AI/ML jobs, the recruiters loved your AI product manager resume, and they offered you the chance to interview. As you set your sights on a role in AI/ML product management, you may wonder how best to prepare for AI product management interview questions. Let’s break down the key areas you should focus on to ace your AI/ML PM interview.
1. Product sense
This involves a deep understanding of the product's core purpose, its target audience, and its position in the market. But it goes beyond just understanding - it's about anticipating. Can you foresee user needs and preferences? How well can you tailor your product's features and functionalities to meet these expectations?
This ability is a cornerstone in demonstrating your proficiency as a product manager, especially in the AI/ML space where user needs can be complex and ever-evolving.
2. Proficiency in statistics
When stepping into AI/ML product management, your acumen in statistics needs to be top-notch. This isn't just a cursory knowledge of basic stats but a deep dive into p-values, confidence intervals, hypothesis testing, and sampling techniques.
Your interview might delve into these areas, assessing your ability to interpret data and make data-driven decisions. The key here is to blend intuition with statistical knowledge, allowing you to navigate through vast data sets and extract meaningful insights.
3. Setting success metrics
In an AI/ML PM career, success metrics take on a new level of importance. You're expected to not only understand but also articulate the impact of a model's output in a real-world production environment.
This is where prioritization frameworks come into play. They help you structure your thinking process, enabling you to solve problems and make decisions effectively.
Mapping user actions to metrics and contextualizing why these metrics are significant is an essential skill. It's about connecting the dots – how user behavior impacts product performance and, in turn, the overall business objectives.
All PMs Will Be AI PMs One Day (Soon)
Many experts believe we are living on the cusp of the next industrial revolution. Just like in the past, new technologies changed how humans lived down to our very anatomy (that’s a callout to fire, the OG technology), AI is going to infiltrate every aspect of our lives. Artificial Intelligence, for product managers, isn’t just about another career choice. It’s about future-proofing your career. It’s about making sure that you’re a part of the next chapter of human history.
Get Certified as an AI Product Manager
To truly set yourself apart, and stay ahead, learn how to build AI products and integrate AI across the entire product lifecycle. The Artificial Intelligence for Product Certification (AIPC)™ provides hands-on instruction to help you learn the skills and knowledge required to build the next generation of products.
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Updated: May 19, 2025
