How to Train AI PMs and Keep Your Job
Tips and tricks for AI-curious PMs and product leaders
You are reading You Are The Product, the highly irregular newsletter for aspiring product leaders, now infused with AI. I’m Mirza, an AI Product Leader building autonomous AI Agents at Zendesk, and you should question everything I claim or at least ask AI to confirm it.
The alarm goes off at 8:00 AM, and Sarah, a Platform PM at an enterprise B2B company, opens her laptop to start her day. Her AI assistant has already analyzed overnight API usage patterns and discovered something interesting: customers are combining features in ways the product team hadn't anticipated. This insight kicks off a day of AI-enhanced product management that would have seemed like science fiction just a few years ago.
By 11:00 AM, Sarah is discussing this discovery with her team in an online strategy session. Their AI collaboration tool has generated several development paths, complete with resource requirements and risk assessments. But it's Sarah's product intuition – a uniquely human skill – that steers the discussion toward transforming this emergent behavior into a new enterprise service offering.
Welcome to product management in 2026/2027. While this might sound like a distant future, McKinsey & Company predicts that generative AI could enable automation of 70 percent of business activities across almost all occupations between now and 2030. The transformation of product management isn't coming – it's already here.
The AI-Native Product Manager
Brian Balfour of Reforge explains what this future looks like: "The next generation of product teams will be trained as AI-native from day one. They will think, work, and build differently." This isn't just about using AI tools; it's about fundamentally reimagining how we approach product management.
Consider Ahmad, a B2C Product Lead at a HealthTech company. His morning looks quite different from Sarah's. While she's analyzing API usage patterns, his AI assistant has processed millions of user interactions from their health monitoring app overnight, identifying behavioral patterns and areas where users are struggling. By mid-morning, he's reviewing AI-generated user archetypes that go beyond traditional personas, incorporating real-time behavioral data and predictive health outcomes.
The contrast between Sarah and Ahmad's workdays illustrates an important point: while AI is transforming all aspects of product management, its application varies significantly based on context. Enterprise B2B and consumer-facing B2C products require different approaches, but both benefit from AI enhancement.
The Core Competencies of an AI Product Manager
To thrive in this new landscape, product managers need to develop three interconnected sets of skills:
Technical AI Skills: While AI PMs don't need to be technical experts, they need enough understanding to make informed decisions. This includes basics of AI/ML fundamentals, data processing, model evaluation and observability, prompt engineering, and API integration. Think of it like being a film producer – you don't need to operate the camera, but you need to understand what makes a good shot.
Traditional PM Skills: The fundamental skills of product management become even more critical in an AI context. Strategy development, user research, roadmapping, and stakeholder management all remain essential, but they're enhanced and transformed by AI capabilities.
Human Skills: As AI handles more routine tasks, uniquely human capabilities become more valuable. Strategic thinking, ethical judgment, and the ability to lead and mentor teams are irreplaceable. AI can analyze data, but it can't make ethical decisions or build genuine human connections.
The magic happens at the intersection of these skill sets. When technical understanding meets product thinking, we get data-driven strategy. When product skills meet human judgment, we create ethical product leadership. When technical knowledge meets human wisdom, we ensure responsible AI development.
Learning Through Real Examples
Let's make this concrete with a practical example. Imagine you're managing a SaaS product, and your data science team proposes building a churn prediction model. The model suggests an 85% accuracy rate in predicting customer churn. What does this really mean for a product manager?
It means that of 100 customers predicted to churn, about 85 actually will, while 15 are "false positives" – customers incorrectly flagged as likely to leave. As a PM, you need to decide if this accuracy rate justifies building automated intervention systems. You need to consider questions like: Should we invest in improving the model further? How do we design intervention systems that account for prediction uncertainty? How do we communicate the system's reliability to stakeholders?
This example shows how technical understanding enables better product decisions. You don't need to know how to build the model, but you need to understand its implications for your product and users.
Starting Your AI Journey
The path to becoming an AI-capable product manager might seem daunting, but it doesn't have to be. Start with foundational learning through courses like Andrew Ng's "AI For Everyone" on DeepLearning.ai or Marily Nika's "AI Product Management 101" on Maven. These provide the basic understanding you need to begin working with AI tools.
But theory alone isn't enough. The real learning happens when you start applying AI tools to your daily work. Begin exploring now.
Tools like, Replit, Bolt, v0, and Cursor can help you quickly validate ideas and product prototypes and even full applications.
Platforms like Amplitude now offer AI capabilities that can help identify patterns and opportunities in your product data. Use these to make more informed strategic decisions. I also really like
’s GPTcsv.Tools like Dovetail and even ChatGPT can help you analyze user feedback at scale, identifying patterns and insights that might be missed through manual analysis.
I’m shamelessly stealing this recommendation from
, but it’s such an amazing watch that I have to recommend it to everyone. gives a lightning lesson teaching you how to prototype using AI in 10 min.
Leadership in the AI Era
For product leaders, the challenge isn't just personal development – it's building and enabling AI-capable teams. This starts with asking the right questions about AI readiness:
Are your company leaders aligned on the value of AI? Do you have budget for AI exploration and training? Do you have the necessary technical infrastructure? Is your data ready for AI systems? Do you have good use cases to explore?
The cautionary tale of McDonald's AI-powered drive-thru experiment shows why these questions matter. After a three-year experiment, they ended the program in June 2024 due to customer frustration with the experience. The technology worked, but the use case wasn't right.
To avoid similar missteps, leaders need to create ecosystems of learning opportunities. This includes providing access to courses and bootcamps, enabling hands-on work with AI tools, rotating PMs through AI projects, and encouraging experimentation through innovation labs and hackathons.
The Symbiotic Relationship Between Product and AI Research
One of the most crucial aspects of AI product management is the relationship between product teams and AI research. This relationship needs to be symbiotic – product teams need to stay at the forefront of AI capabilities, while AI research needs feedback loops from real-world product usage.
To foster this relationship:
Develop a shared product vision that includes AI
Align AI research with business and product strategy
Create time and space for researchers to go deep
Encourage small-scale pilot projects
Build a shared experimentation culture
Make AI research visible in the product roadmap
Preparing for AGI
As we look further into the future, product leaders need to prepare for the potential emergence of Artificial General Intelligence (AGI). This preparation involves developing capabilities in four key areas:
Ecosystem Design
Learning to create and manage multi-product ecosystems, thinking systemically, and aligning product strategy with long-term societal goals.
AI Ethics & Advocacy
Engaging with policymakers to influence AI regulations and understanding the ethical implications of AI development.
Workforce Resilience
Learning how to train humans to work alongside AI and guide teams through technological transitions.
Human-Centered Product Design
Creating products that prioritize human value over mere utility, understanding how to leverage new technologies for emotional experiences.
The Path Forward
The transformation of product management through AI is both a challenge and an opportunity. While the technical aspects might seem daunting, remember that the core of product management remains unchanged – understanding user needs, solving real problems, and creating value.
The key is to start small and build incrementally. Begin with simple AI enhancements to your current processes. Experiment with AI tools in your daily work. Build your understanding through a combination of learning and doing. Most importantly, maintain your focus on creating value for users, using AI as a powerful tool rather than an end in itself.
As Lenny Rachitsky notes, "PMs will become editors of super-intelligent suggestions." Our role isn't to be replaced by AI but to evolve into something new – leaders who can combine human judgment with AI capabilities to create products that were previously impossible.
The future of product management is already here. The question isn't whether to adapt, but how quickly and effectively we can embrace these changes while maintaining our focus on creating genuine value for users and businesses.
I recently gave a talk on this same topic on the Product People livestream. Watch it below: