What Steps Should You Take to Become an AI Product Manager?

TL;DR: Transitioning to AI product management requires a focused effort, as it differs from traditional PM roles by being heavily data-driven, involving uncertain R&D cycles, demanding close collaboration with technical teams, and requiring ethical considerations. To make the shift, focus on one AI technology like LLMs or computer vision, gain a solid grasp of AI basics such as machine learning and deep learning, establish a structured learning framework, and master AI-specific metrics like model accuracy, precision, and recall. By focusing on mastering specific areas and working on practical projects, PMs can develop the essential skills to thrive in this fast-paced and ever-changing field.

I've noticed that many early-career product managers are eager to transition into AI, and I truly admire this enthusiasm! However, it's important to understand that if you have strong PM skills but lack direct AI experience, breaking into AI product management requires intentional skill development, strategic networking, and hands-on experience in AI domains that align with your career aspirations.

In other words, it will take dedicated effort and focus to make this shift—but the rewards will be well worth it.

To make this journey a bit smoother, I've put together this guide to help you navigate the transition. From grasping AI fundamentals to positioning yourself as a standout AI PM candidate, this guide will provide the insights you need.

What Sets AI Product Management Apart?

As a product manager, you’re already accustomed to overseeing development cycles, coordinating with stakeholders, and delivering results based on customer needs. These core skills remain just as important when working with AI-driven products.

However, AI product management comes with unique challenges that set it apart. Recognizing these key differences is crucial to thriving in this space.

AI Products Are Built Around Data

Unlike traditional products that follow feature-driven roadmaps and user feedback loops, AI products revolve around data. Data isn’t just a supporting element—it’s the backbone of the product itself.

As an AI PM, you must deeply understand the data pipelines that power your AI models. This includes knowing how data is collected, labeled, and processed. Close collaboration with data engineers and scientists is essential to ensure the data is clean, relevant, and unbiased, as poor-quality data can severely impact model performance and user experience.

One of the biggest surprises for new AI PMs is how much time is spent on data preparation, management, and validation. Before prioritizing features, you need to focus on the data—it directly affects model accuracy and overall product success.

In reality, a significant portion of AI product management involves hands-on data work. You might find yourself validating datasets, fixing incorrect labels, or instructing data labelers to redo entire batches due to misinterpretations.

In fact, you’ll make far more progress by being involved early in the data labeling process rather than simply reviewing the final output. By setting the right standard from the beginning, you can train your labeling team to follow best practices—just as you would.

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AI Models Involve Uncertain R&D Cycles

Unlike traditional software, AI models aren’t built and deployed in a single sprint. They require ongoing retraining and refinement as new data becomes available.

As an AI PM, you must recognize that AI products are not static—they evolve over time through continuous iteration. High-quality data and constant feedback loops are essential for improving model predictions and classifications.

Managing iterations in an environment with unpredictable outcomes requires setting clear, realistic expectations. AI models will make mistakes, and failure is part of the process. What matters is how you respond to those failures and refine the model accordingly.

To navigate this uncertainty, collaborate closely with your data science team to establish a robust framework for model evaluation. Define success criteria that account for experimentation rather than aiming for perfect accuracy. Align product goals with the technical nuances of AI improvement, particularly when it comes to key performance metrics like precision, recall, and accuracy.

Collaboration with Technical Teams is Mission-Critical

One of the biggest adjustments you'll face as an AI PM is the level of technical collaboration required.

While product managers always work cross-functionally, AI PMs must develop strong relationships with data scientists and machine learning engineers. Understanding their terminology is key to facilitating meaningful discussions about model architectures, data needs, and performance metrics.

Be prepared to work alongside PhDs—you might be the least academically qualified person in the room, and that’s intentional. Your role is to represent the business, while the scientists handle the technical complexities.

However, guiding a team of experts also means making tough calls. You may have to stop experiments early, redirect research efforts, or decide that certain AI approaches are not viable for business goals. It can be intimidating, but it’s a crucial part of the job.

How technical do you need to be? You don’t need to code, but you must understand AI concepts well enough to ask insightful questions and make informed decisions. You should be comfortable discussing algorithms, evaluating model performance, and weighing trade-offs between explainability and complexity.

To build trust with your technical team, demonstrate a genuine willingness to learn. Set up regular check-ins to discuss their work and connect it to real user impact. The more you understand their challenges, the better you can align on shared objectives and drive meaningful progress.

Ethics in AI: A Critical Responsibility

AI products have the potential to unintentionally reinforce biases or make decisions with significant ethical implications.

As an AI PM, it’s your duty to ensure that fairness, accountability, and transparency are embedded in your product’s AI models. Bias in training data can lead to skewed predictions, disproportionately affecting certain user groups—something that can be both harmful to individuals and damaging to your brand’s reputation.

So, how do you ensure ethical AI? Start by scrutinizing your data. Where does it come from? Does it reflect the diversity of your user base? Work closely with your data science team to implement fairness checks and conduct bias audits to catch potential issues early.

Ethical considerations shouldn’t be an afterthought. They need to be integrated from the very beginning of product development. Ask the tough questions about data sources and model behavior upfront, and when in doubt, always prioritize caution over convenience.

Key AI Technologies and Their Use Cases

AI isn’t a single, unified system—it’s a collection of specialized technologies, each designed for different use cases and challenges. As an AI PM, understanding these distinctions is crucial to selecting the right approach for your product.

However, you don’t need to become an expert in every AI domain. Instead, identify the area most relevant to your work and focus your learning there.

Why is this important? AI fields can be vastly different. For instance, my wife specializes in large language models (LLMs), while I work in computer vision—two areas that have minimal overlap, except for shared concepts like transformers (which, as a PM, you don’t need to master in-depth).

As you go through this section, pinpoint the AI technology that aligns with your interests or product goals. Product management is all about prioritization—don’t try to absorb everything at once. Instead, focus on what will make the biggest impact in your role.

Large Language Models (LLMs)

LLMs, such as GPT-4, are designed to process and generate human-like text, making them highly effective for natural language processing (NLP) applications. These models are commonly used in chatbots, content creation tools, and AI-powered assistants. Some notable examples include:

• Customer support chatbots (e.g., Google's Dialogflow, Intercom)

• Content generation tools (e.g., OpenAI's GPT-4, Jasper)

• Engineering copilots (e.g., GitHub Copilot)

However, it’s important to recognize the limitations of LLMs. Since they generate responses based on patterns in their training data, they can sometimes produce misleading or incorrect information. When integrating LLMs into products, it’s crucial to have mechanisms in place to verify the accuracy of their outputs.

Computer Vision

Computer vision (CV) allows machines to analyze and interpret visual data from images and videos. This technology plays a crucial role in various industries, including autonomous driving, healthcare, and security. Some key applications include:

• Wildfire detection (e.g., Pano)

• Self-driving cars (e.g., Waymo, Tesla Autopilot)

• Healthcare imaging

For many computer vision applications, real-time performance is essential. In scenarios like self-driving cars or medical diagnostics, even a slight delay can have significant consequences. As an AI PM, you’ll need to collaborate closely with engineers to optimize model efficiency, reduce latency, and ensure that visual processing happens within milliseconds for mission-critical use cases.

Recommendation Algorithms

Recommendation algorithms are designed to analyze user behavior and suggest personalized content, products, or services. These systems are prevalent across various industries, including e-commerce, social media, and entertainment. Some common use cases are:

• E-commerce product recommendations (e.g., Amazon, Shopify)

• Social media content feeds (e.g., TikTok, Instagram)

• Streaming services (e.g., Netflix, Spotify)

These algorithms depend heavily on feedback loops, where user interactions shape future suggestions. It's crucial to continually monitor and fine-tune these models to prevent negative feedback loops, such as promoting irrelevant or low-quality content. Ensuring the quality of recommendations is key to maintaining a positive user experience.

Other AI/ML Technologies

In addition to the core AI technologies, there are several other AI/ML techniques that are applied across a variety of industries:

• Speech recognition: Converts spoken language into text, widely used in virtual assistants (e.g., Google Assistant, Alexa) and transcription services (e.g., Otter.ai).

• Reinforcement learning: Trains agents to make decisions by rewarding desired behaviors. It’s commonly used in robotics and gaming.

• Predictive analytics: Uses historical data to predict future outcomes, useful in industries like finance and healthcare.

By understanding which AI technology your product utilizes and how it’s typically applied, you can better define product features, set performance metrics, and work effectively with your technical teams to achieve your product goals.

Acquiring AI-Specific Knowledge and Skills

Now that you've chosen the specific AI technology to focus on (and remember, don't try to tackle everything at once—prioritize one area!), it's time to build the knowledge and skills necessary to excel as an AI product manager.

While you don’t need to become an expert machine learning engineer, you should acquire a solid understanding of the fundamentals of AI and ML. This will allow you to collaborate effectively with technical teams, make informed decisions, and help shape the development of AI-driven products. Here’s how to get started:

1. Grasp AI basics

2. Establish a framework for additional learning

3. Master AI-specific metrics

Grasp AI Basics

To get started in AI, focus on a few key concepts that will lay the foundation for your work as an AI product manager. Here's a breakdown of core areas you should familiarize yourself with:

Machine learning: Learn the different types of machine learning (supervised, unsupervised, and reinforcement learning) and their respective applications. Understand key concepts such as training data, model overfitting, generalization, and cross-validation. Knowing how models learn from data and make predictions is essential for any AI PM.

Deep learning: Dive into neural networks and deep learning, especially if you’re interested in areas like computer vision or natural language processing. Deep learning models, which mimic the structure of the human brain, are behind some of the most significant advances in AI, from facial recognition to speech synthesis.

Model lifecycle: AI models follow a lifecycle that includes data preparation, model training, validation, deployment, and continuous monitoring. As an AI PM, you’ll need to understand how models are developed and maintained over time. AI models, unlike traditional software products, require retraining and adjustments based on the changing nature of data inputs and user behavior.

Data pipelines: Since AI depends on high-quality data, learn about data ingestion, transformation, labeling, and storage. Get familiar with the challenges of ensuring data cleanliness and robustness.

Here are some resources to help you get started:

• Coursera’s AI for Everyone: A great starting point for understanding AI at a high level.

• Google's Machine Learning Crash Course: Provides a more hands-on introduction to ML concepts.

• Fast.ai: Offers practical deep learning courses tailored for non-experts.

• BlueDot Impact: Courses that support people to develop the knowledge, skills and connections to pursue a high-impact career in AI

How deep do you need to go with AI concepts? Start by learning enough to participate in technical discussions confidently. While you won’t be building models yourself, having a strong understanding of how AI works will allow you to ask the right questions and ensure product decisions are data-informed.

The most effective AI PMs are those who understand the "why" behind model performance. When a model’s predictions fall short, your role is to dive into the data, collaborate with data scientists to troubleshoot, and adjust the product roadmap based on model limitations. It’s a continual process of learning and iteration!

Establish a framework for additional learning

When you’re learning AI as a PM, it’s important to structure your education strategically so you can build on each concept incrementally. Here’s a framework that can help guide your learning:

1. Start with a High-Level Overview: Begin by taking foundational courses. These will give you a broad understanding of how AI works without getting lost in technical details.

2. Choose a Domain to Deep Dive Into: Pick one specific area of AI that interests you—such as NLP, computer vision, or recommendation systems—and immerse yourself in learning about that area. Take a hands-on course, read relevant research papers, and work through real-world use cases.

3. Build a Side Project: Apply what you’ve learned by building a simple AI-driven project. This could be a chatbot using an open-source NLP library or a basic image classifier using pre-trained models. Building a side project will give you practical experience in the model lifecycle, data management, and deployment challenges.

4. Repeat Steps 2 and 3: After your first deep dive, repeat the process for another AI domain. This iterative approach will help you gain a well-rounded understanding of various AI applications, making you more versatile as an AI PM.

A well-structured learning journey is key to transitioning into AI. Many PMs try to learn everything at once and get overwhelmed. Instead, focus on understanding one area deeply before moving on to the next.

Depth of knowledge in AI is far more valuable than breadth.

Mastering AI Product Metrics

In AI product management, understanding and utilizing the right metrics is essential to measure your product’s success and its impact on users.

Here are some key AI-specific metrics you’ll need to master:

• Model accuracy: This measures how often your model’s predictions are correct. However, accuracy alone can be misleading, especially when dealing with imbalanced datasets.

• Precision and recall: These metrics help you understand the trade-off between false positives and false negatives. In products where the cost of a false positive is high (e.g., fraud detection), precision is more important. In others, where missing a key event is costly (e.g., medical diagnosis), recall takes priority.

• F1 score: This is a harmonic mean of precision and recall and is especially useful when your data is imbalanced. A high F1 score ensures that both precision and recall are optimized.

• Model interpretability: Can your model's outputs be easily understood by non-technical stakeholders? Interpretability is particularly important in high-stakes domains like healthcare or finance, where users and regulators need to trust the model’s decisions.

• Latency and throughput: In real-time AI applications (e.g., voice assistants or autonomous driving), these metrics measure how quickly the model can process inputs and deliver outputs. Latency refers to the time it takes for the model to return a result, while throughput measures how many operations the model can perform in a given timeframe.

Which metrics matter most? It depends on your product’s use case.

For instance, in a social media recommendation engine, precision and recall are vital for personalizing content.

In a real-time application like self-driving cars, low latency is crucial. Understand your product’s specific needs before determining which metrics to prioritize.

AI PMs often struggle to balance accuracy with interpretability. Highly accurate models may be too complex for end-users to understand. As a PM, you need to make trade-offs between accuracy, interpretability, and speed based on your product’s user experience and goals.

Build AI-Specific Experience

Once you have a solid understanding of AI principles, the next step is to gain hands-on experience. Transitioning into AI product management is not just about learning concepts; you need to demonstrate that you’ve applied those concepts in real-world scenarios.

Work on AI-Adjacent Projects

Leverage your current role to find projects that involve AI or data science. Even if you’re not an AI PM yet, there are plenty of opportunities to contribute to AI-driven initiatives in most companies. This could involve scoping out a feature that uses machine learning, collaborating with a data science team, or working on a recommendation algorithm for personalized content.

How can you find AI projects in your current role? Start by identifying any products or features that rely on data or automation. For example, you might work on improving personalization in a user interface or optimizing search results using a recommendation engine.

Focus on the business value of AI features, not just the technology. Many AI initiatives fail because they don’t directly tie into customer or business outcomes. Make sure your AI efforts are grounded in solving real user problems and driving measurable impact.

Some AI-related projects within your current organization might include:

• Collaborating with a data science team on a feature that uses machine learning (e.g., recommendation engines or personalization algorithms).

• Helping to scope AI features, such as chatbots or image recognition systems, for your existing product.

• Running user research to understand how AI-driven insights could improve user experiences.

By working on projects that involve AI or ML, even peripherally, you’ll build valuable experience and create stories for future interviews.

Participate in Hackathons and AI Competitions

AI hackathons and competitions like those on Kaggle offer a great way to work on real-world AI problems in a team environment. These experiences provide hands-on learning and show future employers you’re serious about breaking into AI.

Participating in hackathons can give you a crash course in AI development. You’ll learn how to work under pressure, make quick decisions about data and models, and deliver an MVP (Minimum Viable Product) in a short amount of time. The experience you gain from these events is invaluable when transitioning into AI product management.

Launch Your Own AI Side Project

One of the most effective ways to demonstrate your readiness for AI product management is to build and launch your own AI-driven side project. This is where your hands-on learning comes to life. A side project allows you to explore the product lifecycle of an AI system, from idea generation to deployment, and demonstrates your initiative to potential employers.

What kind of AI side project should you build?

Start small.

Pick a domain you’re passionate about and leverage existing pre-trained models to speed up development. For example, you could create a personalized movie recommendation bot, an image classifier for identifying plant species, or even a chatbot that answers customer service queries.

By launching even a small AI product, you’ll learn the nuances of deploying machine learning models, including testing, feedback loops, and iteration.

Step 1: Identify a Problem: Look for an everyday problem that could be solved using AI. Maybe you notice inefficiencies in how certain tasks are performed in your daily life or within your organization.

Step 2: Choose a Pre-trained Model: Use platforms like Hugging Face or Google Cloud’s AutoML to access pre-trained models. These allow you to focus on building a product without needing to develop a model from scratch.

Step 3: Develop the Product: Build a simple UI and integrate the AI model into it. For example, if you’re building a recommendation system, you can use a pre-trained NLP model to analyze user preferences and suggest items.

Step 4: Deploy and Iterate: Once your MVP is live, test it, gather feedback, and improve the model based on user inputs. AI projects are iterative by nature, so use this as a learning opportunity to refine both your product and your understanding of the model.

Building an AI side project is more than just coding. Treat it like a full product development cycle. Develop user personas, define your problem statement, set KPIs, and align model outputs with business outcomes. Even if your side project isn’t commercial, the way you think about it from a product perspective will demonstrate your readiness for an AI PM role.

Once you’ve launched your AI side project, share it widely. Showcase your project on GitHub, write about your learnings on Medium, and include it in your portfolio. You’ll not only attract attention from potential employers but also gain valuable feedback from the AI community.

Share the process, not just the result. AI products are about learning from iterations, so be transparent about what worked, what didn’t, and how you improved. Sharing this thought process makes you a stronger candidate because it highlights your problem-solving skills and willingness to learn from setbacks.

Networking Strategies for AI Product Management

Building relationships in the AI space is critical for breaking into AI product management. Networking helps you stay informed about industry trends, discover job opportunities, and build credibility within the AI community.

Here’s how you can take a strategic approach to networking in AI!

Attend AI Conferences and Meetups

AI conferences such as NeurIPS or O'Reilly AI offer excellent opportunities to network with professionals in the field. You’ll hear about the latest trends and challenges in AI, which will give you insights into what AI PMs are currently working on.

Local AI meetups or virtual events can also provide valuable networking opportunities and a chance to meet AI professionals in your area.

What should you focus on at AI conferences?

Well, first off, don’t try to attend every session.

Focus on talks and workshops that align with your interests and career goals. AI product management sits at the intersection of business and technology, so attending sessions on product strategy, ethics in AI, or the latest developments in NLP or computer vision can be incredibly beneficial.

Be proactive at events. Don’t just listen to speakers; instead, make sure you’re asking the right questions.

Prepare thoughtful questions for the speakers, introduce yourself to attendees, and don’t hesitate to approach people after sessions.

Networking at conferences is one of the best ways to get noticed in the AI community. And, the AI community is tight-knit; people know people! If someone is willing to advocate for you, you’re much more likely to break into AI product management.

Leverage LinkedIn

LinkedIn is an incredibly powerful tool for connecting with AI professionals. Use it to identify AI PMs, data scientists, and engineers in companies or sectors you’re interested in, and reach out for informational interviews.

Start with Common Ground: Mention something specific about their work, such as a recent product launch or research paper they were involved in. People are more likely to respond when they see genuine interest in what they do.

Ask for Advice, Not a Job: Focus on learning about their journey into AI, the challenges they face in AI PM, and what they believe are key skills to develop. This will help you better understand the space while also building meaningful connections.

Don’t just reach out for the sake of networking. Approach these conversations with specific questions and a learning mindset. Ask about how they handle product iterations with ML teams, how they balance ethical considerations, or how they’ve managed failures in AI projects. These questions not only show your interest but also help you learn from their experiences.

Join online communities

There are several online communities dedicated to AI, such as Reddit’s r/MachineLearning, AI-focused Slack groups, and forums like Towards Data Science. Participating in these groups allows you to engage in discussions, ask questions, and learn from a wide range of AI professionals.

Consistency is key in online communities. Don’t just post once and disappear—regular engagement is how you’ll build relationships. Share your own learnings from AI side projects, comment on discussions about emerging trends, and help others where you can. Your participation will make you visible in the community, and over time, you’ll develop a network of peers and mentors who can support your journey.

Mentorship

Seek out a mentor who works in AI product management.

A mentor can help guide you through the transition process, review your projects or resume, and offer insights on navigating the field.

Many AI PMs are open to sharing their knowledge, and formal mentoring platforms like MentorCruise can facilitate these connections.

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Tailor Your Resume and Portfolio for AI Roles

When applying for AI PM roles, your resume and portfolio need to highlight your AI experience and demonstrate that you have the technical know-how to collaborate with data scientists and ML engineers.

Emphasize Data-Driven Decision Making

AI products are built on data, so any experience you have with data analysis, predictive modeling, or personalization should be front and center on your resume. Highlight specific examples where you worked with data teams or implemented data-driven features.

Example: "Collaborated with the data science team to design and implement a recommendation engine, resulting in a 15% increase in user engagement."

Hiring managers are looking for PMs who can speak the language of data. Show that you understand how data fuels AI products by emphasizing your experience with metrics, data analysis, and any work you’ve done with data scientists or ML teams.

Showcase Collaboration with Technical Teams

Since AI PMs work closely with technical teams, demonstrating that you can bridge the gap between product and engineering is crucial. Highlight any cross-functional projects where you led initiatives that required close collaboration with data scientists or engineers.

Example: "Led a cross-functional team of engineers, data scientists, and UX designers to develop an AI-powered feature for personalized content recommendations, from ideation to launch."

AI PM roles demand strong technical collaboration skills. Don’t just mention that you worked with engineers; highlight how you facilitated those interactions and ensured that technical and business goals aligned. This will show that you’re capable of leading complex AI projects.

Highlight AI Projects

Even if your experience with AI is limited to side projects or hackathons, include those in your portfolio. Walk through your process of identifying a problem, selecting an AI model, and deploying the solution. Explain the challenges you faced, how you iterated, and what results you achieved.

Example: "Developed an AI chatbot for customer support, which reduced response times by 20%. Iterated on the model based on user feedback to improve the accuracy of responses."

Your portfolio should tell a story. Don’t just list the projects; instead, showcase the decision-making process behind them. Explain why you chose certain AI models, how you defined success metrics, and how the product evolved through iterations. This narrative will demonstrate your deep understanding of the AI product lifecycle.

Quantify Your Impact

Numbers speak volumes. Whenever possible, quantify the impact of your work. Whether it’s increased user engagement, reduced operational costs, or improved model accuracy, metrics make your contributions tangible.

Example: "Implemented an AI-powered lead scoring system, increasing sales conversions by 25%."

AI products are measured in terms of their outcomes, not just their features. Highlight the business impact of your AI initiatives by showing how they improved user experience, efficiency, or revenue. Quantifying your achievements helps to frame your AI experience in a way that hiring managers will appreciate.

Prepare for AI PM Interviews

AI product management interviews differ from traditional PM interviews in a few key ways. While product thinking and leadership skills remain critical, AI PM roles demand a strong grasp of technical concepts and the ability to solve problems that arise from the unique challenges of working with AI and machine learning models.

Understand Common AI Product Challenges

AI products come with their own set of challenges, such as limited or biased training data, model drift over time, and the complexities of deploying machine learning models in real-world environments. Interviewers will expect you to demonstrate awareness of these challenges and have a strategy for addressing them.

Example Challenge - Data Bias: AI models are only as good as the data they’re trained on. If the training data is biased, the model will be too, leading to skewed outcomes. For example, a facial recognition model trained on a dataset lacking diversity might struggle to identify people of different ethnic backgrounds.

How to Prepare: Be ready to explain how you would address bias in training data, ensure fairness in the model’s outputs, and manage user expectations when deploying the model.

During the interview, frame your approach to challenges by focusing on collaboration. Explain how you would work with data scientists to audit and improve datasets, as well as how you’d communicate potential risks to stakeholders. The ability to foresee and mitigate issues like bias, data drift, or overfitting will set you apart.

Master Technical Topics at a High Level

While you won’t be expected to code, interviewers will test your understanding of key AI and machine learning concepts. You'll need to explain these concepts in simple terms and discuss their impact on the product roadmap.

Example Concepts: Neural networks, reinforcement learning, decision trees, and unsupervised learning.

How to Prepare: Be comfortable discussing how models like these are built and trained, their trade-offs (e.g., accuracy vs. interpretability), and the impact of these trade-offs on the user experience.

It’s not just about regurgitating definitions. You’ll be judged on how well you can connect technical concepts to product decisions. When asked a technical question, always tie it back to business value. Why does this model matter for the user or the company? Your ability to navigate both technical depth and product strategy is what will distinguish you.

Practice AI Product Case Studies

AI PM interviews often include case studies where you’re asked to design an AI product or solve a specific AI-related problem. This could involve building a recommendation engine for a new app, optimizing an existing machine learning feature, or addressing model performance issues.

How to Prepare: Approach AI case studies the same way you would any product case study, but layer in AI-specific considerations. Start by framing the problem: What is the business goal? What are the user needs? Then, think about which type of AI solution could best meet those needs. Consider factors like data availability, model complexity, interpretability, and ethical implications.

Example: You might be asked, “How would you build a recommendation system for a music streaming platform?” In your answer, walk through your product vision, the types of user data you would collect, how you would ensure data privacy, and how you would measure success (e.g., precision, recall, or user engagement).

Don’t just think about how to build the AI feature. Think about how to maintain it for the next three years!

AI products require ongoing iteration, monitoring, and retraining. Show the interviewer that you understand how to ensure the model continues to improve over time, and how you would handle situations where the model’s performance degrades.

Highlight Ethical and Regulatory Considerations

AI is often subject to greater scrutiny than traditional software due to its potential for bias, ethical issues, and regulatory challenges. Be prepared to discuss how you would navigate these concerns in your product decisions.

Example: If you’re working on an AI-driven healthcare product, how would you ensure that the model’s predictions are both accurate and equitable? What steps would you take to comply with healthcare regulations like HIPAA?

How to Prepare: Familiarize yourself with ethical AI guidelines, data privacy laws, and industry-specific regulations (e.g., GDPR in Europe, or CCPA in California). Be ready to discuss how these frameworks influence your product roadmap and decision-making.

AI ethics is not an afterthought; it’s a core part of your product strategy. In your interview, demonstrate that you take ethical concerns seriously. Discuss how you would build fairness checks into the model development process, how you’d explain model decisions to users, and how you’d mitigate risks.

Avoid Trend Chasing

In a field as dynamic as AI, it’s easy to get caught up in the latest breakthroughs, hyped technologies, or the opinions of high-profile thought leaders. But while staying informed about the AI landscape is important, there’s a fine line between being informed and blindly following trends. 

Make sure you focus on building AI products with scalable, generalized approaches that are positioned to leverage the long-term trajectory of AI advancements.

General Methods Scale Better Over Time

The most effective AI advancements come from general methods that scale with computation and data, rather than short-term, domain-specific optimizations.

Prof. Rich Sutton’s "Bitter Lesson" reveals that AI methods reliant on human-designed heuristics or domain expertise tend to plateau, while generalized techniques like deep learning and reinforcement learning continue to improve as computational power and data availability grow​.

The success of these methods is largely due to their alignment with Moore’s Law, which predicts the exponential growth of computing power. Systems that are built to take advantage of this scalability will ultimately outperform those that are overly fine-tuned to specific, short-term needs.

The Dangers of Information Overload

Reading too many AI papers, or constantly staying up to date with every new breakthrough, can lead to confusion and decision fatigue for AI product managers.

While academic research often explores cutting-edge ideas, many papers focus on highly specialized use cases or experimental methods that may not be practical or scalable for real-world product development.

This overwhelming influx of information can make it difficult to discern which approaches are genuinely useful for your product and which are simply speculative.

When PMs try to integrate too many experimental ideas into a product roadmap, it can lead to scope creep and misalignment with the core product strategy.

Instead of attempting to absorb every new AI paper, focus on foundational techniques that have proven to scale effectively!

Focus on Scalability, Not Trends

As an AI PM, prioritize building sustainable, scalable solutions that can adapt to future advancements.

While it's tempting to jump on the latest AI trends, the true path to long-term success lies in creating models that consistently perform well across various datasets and scenarios.

Make scalability a core part of your product strategy to ensure your AI system grows and improves without needing constant overhauls.

Chasing trends may bring temporary success, but lasting innovation in AI is driven by focusing on foundational principles like reliable data pipelines, robust models, and scalability.

By sticking to scalable practices and adopting new technologies only when they're ready to add value, you'll ensure your products remain strong and adaptable for years.

Closing Thoughts

Entering AI product management demands focused learning, networking, and hands-on experience.

By mastering AI fundamentals, gaining practical experience with AI projects, and connecting with industry professionals, you'll be well-prepared for this dynamic and fast-growing field.

Stay patient, continuously enhance your skills, and seize any opportunity to engage with AI.

All your efforts will culminate in a strong story that positions you to successfully land your first AI PM role!