Updated: August 21, 2025- 16 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 (1), 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.
AI PRD Template
Plan, strategize, and align stakeholders around the key requirements unique to AI products.
By sharing your email, you agree to our Privacy Policy and Terms of Service
What Is AI Product Management?
AI product management refers to the development and overseeing of AI products throughout their entire lifecycle. AI products are unique because they evolve with the data they process, unlike traditional software, which is deterministic and based on set algorithms. AI products learn. This dynamic nature makes the development process for AI products continually adaptive. What sets them apart:
High-Performance (the “wow” factor): AI 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: AI products can expand to serve broader audiences without losing performance.
Competitive Advantage: Mastering AI gives a business a sustainable edge in the market.
What Is an AI Product Manager, really?
An AI Product Manager is a PM who drives the end-to-end development and lifecycle of AI-powered products by bridging user needs, business goals, and machine learning capabilities. As I said above, that's most product managers nowadays. Some people also get fussy about the difference between AI products and features. But I think that misses the point.
The point is, users now expect a product experience that only AI can provide. Whether it's powering the data analysis that makes the product possible or a standalone AI product like ChatGPT, products need AI product managers.
What AI Product Managers Do
1. Frame Problems for AI-Specific Solutions
Identify opportunities where machine learning or generative AI is the right fit.
Translate business and user problems into tasks like classification, ranking, prediction, or generation.
2. Work with Probabilistic, Not Deterministic Systems
Understand and communicate that AI systems are inherently probabilistic.
Set clear expectations around model failure modes, confidence levels, and fallback strategies.
3. Scope Products Around Data and Models
Assess the data quality, quantity, and labeling feasibility before scoping.
Collaborate with ML teams to validate whether the problem is learnable and what model architectures are viable.
4. Define and Run AI Evals
Design and oversee AI evaluations to measure system performance beyond accuracy—especially for generative outputs (e.g. text, images).
Use human-in-the-loop evaluation, rubric-based grading, or LLM-as-a-judge techniques to score outputs on dimensions like relevance, coherence, bias, and helpfulness.
Continuously test models through offline evals, A/B tests, and real-world feedback loops.
5. Define Success Beyond Traditional KPIs
Track both product KPIs (adoption, engagement) and model metrics (e.g. F1, BLEU, ROUGE, or hallucination rate).
Consider AI-specific concerns like model drift, latency, and toxicity filtering.
6. Enable Continuous Learning and Improvement
Implement feedback loops to retrain models, fine-tune outputs, or shift prompts.
Plan for model versioning, rollback mechanisms, and real-time monitoring of AI performance.
7. Manage AI Risk and Ethics
Identify risks like bias, fairness, hallucination, privacy, and explainability.
Ensure alignment with responsible AI practices and compliance requirements.
8. Act as a Translator Across Teams
Bridge understanding between AI practitioners (data scientists, ML engineers) and non-technical stakeholders (execs, customers).
Communicate tradeoffs in model choices, explainability, and ethical considerations clearly and confidently.
AI Product Manager Skills
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 the potential of AI agents for PMs, 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:
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.
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
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.
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.
Challenges for AI Product Managers
Specialized Knowledge: Developing AI products requires skilled data scientists and engineers. AI/ML 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: AI models need significant computational resources and data storage for large-scale deployment.
Development Cycles: AI products often have longer development timelines, requiring careful planning and patience.
Transparency Issues: The complex nature of AI models can make them difficult to explain and justify.
Ongoing Maintenance: Continuous monitoring and updating are essential to keep AI products effective and relevant.
What Types of AI Technology Do AI Product Managers Use?
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. In the age of artificial intelligence, this means using AI throughout the product management process and integrating it into products and features.
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.
By sharing your email, you agree to our Privacy Policy and Terms of Service
Developing AI products involves incorporating different types of AI technology:
Generative AI: Large Language Models (LLMs), like GPT-5 or Gemini, which are capable of understanding and generating text, images, and more.
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: 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 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:
AI Prompt Template
Engage effectively with natural language processing chatbots to ensure quality results.
GET THE TEMPLATE
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.
Prototyping with AI: Product managers can use AI to rapidly generate prototypes, from wireframes to interactive mockups. These tools allow PMs to validate ideas early by simulating user flows, testing variations, and gathering feedback before investing in development. AI-powered prototyping speeds up iteration cycles, reduces design bottlenecks, and helps teams align on a clear vision of the product experience.
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.
Level up on your AI knowledge
Take a deep dive into the world of AI with Product School's definitive guide.
Download Guide
What Are the Steps to Creating an AI Product Strategy?
Developing an AI product strategy is one of the core responsibilities of AI PMs.
Anchor on Business Goals & UVPs
Clarify the business outcomes you’re targeting and inventory your unique advantages (e.g., proprietary data, market position, subject-matter expertise). Your AI bets should compound what your company already does well.
Identify High-Value AI Applications
Map pain points and opportunities where AI can drive innovation or efficiency. Prioritize use cases where impact is clear and feasibility is high given your data, constraints, and risk profile.Define User-Facing Features & Workflows
Translate chosen AI capabilities into concrete features that deliver tangible value. Design for seamless fit within existing user workflows, with clear success metrics and guardrails.Co-Design with Data & ML Teams
Partner closely with data engineers and scientists to ensure data integrity, bias mitigation, and model interpretability. Align on data pipelines, governance, and ethical guidelines from day one.Train, Deploy, and Scale Responsibly
Oversee model training, validation, and deployment to ensure accuracy, reliability, and scalability. Optimize for real-world performance (latency, cost, drift) and set up monitoring for continuous improvement.
What Tools Do AI Product Managers Use?
Product managers today have access to a growing ecosystem of AI-powered tools that can streamline workflows, uncover insights, and accelerate decision-making. From Gen AI for drafting user stories or generating ideas to automating routine workflows using AI agents for product managers, AI/ML for product managers is fundamentally changing how PMs tackle strategy and execution. When integrated thoughtfully, they free up bandwidth for high-impact work, enabling product leaders to stay ahead of the curve.
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.
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.
How to Prepare 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 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 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.
enroll now
(1) source: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-gen-ai-skills-revolution-rethinking-your-talent-strategy
Updated: August 21, 2025
