Training Your Product Managers in the AI Era

While artificial intelligence is reshaping industries at breakneck speed, product management leaders face a pivotal challenge: ensuring their teams are not only AI-literate, but AI-confident.

To stay competitive, your teams need to evolve quickly. And if the insights uncovered by Productboard and User Evidence’s State of AI in Product Management Report are any indication, the pressure is on. Nearly every product team (96%) already uses AI in some capacity, with half embedding it deeply into daily workflows. Product managers (PMs) are offloading tactical work—research, synthesis, documentation—and reclaiming about hours per core functions, time now spent on higher-leverage strategic thinking. 

Our latest CPO Survey revealed that the AI shift comes with new expectations: 59% of product leaders say strategic and business acumen is now the most critical PM skill, while others point to AI/ML fluency (22%) as emerging requirements.

As a Chief Product Officer or Head of Product, how do you empower your PMs to thrive in this AI-driven era?

Hiring for that perfect “AI-native” PM might seem like the fastest path, but it’s not a scalable one. 68% of executives report facing a moderate to extreme AI talent shortage, and 59% of companies cite difficulty finding qualified AI product owners. Demand is surging, but the pool of PMs with real-world AI experience remains limited. 

What’s often overlooked is a far more accessible, impactful lever: upskilling your existing talent. The CPO Survey revealed a stark disconnect. While 85% of leaders plan to invest in AI/ML tools, only 2% are prioritizing talent development. Tool investments are skyrocketing while talent enablement lags behind.

Bridging that gap isn’t optional. It’s the key to moving with both speed and strategic clarity in the AI era.

Emerging Trends in PM Training Programs

How are top companies actually closing the knowledge gap? By proactively building AI knowledge internally rather than waiting to hire “unicorn” AI-native PMs from outside. From in-house AI academies and rotational programs to lunch-and-learns and external certifications, these organizations are sending a clear message: every PM needs to become conversant in AI.

Tech giants in particular are setting the tone with ambitious internal training initiatives designed to democratize AI across the workplace.

Microsoft’s AI Learning Curriculum

Microsoft has launched a company-wide AI curriculum via Viva Learning to raise the baseline of AI literacy across roles. The Basic track introduces foundational concepts like generative AI basics, no-code AI tools, and responsible AI principles. The Intermediate level moves into applied skills—prompt engineering, natural language processing, and hands-on use of Azure AI services. The Advanced track dives deeper into building and training large language models (LLMs), neural networks, and advanced ML techniques. 

Google’s “AI Savvy” Program

Google's AI Savvy initiative reflects CEO Sundar Pichai’s call for employees to embrace AI in their daily work—or risk falling behind competitors. The program blends online courses, toolkits, and hands-on workshops to help Googlers integrate AI into their projects and workflows. Early outcomes are promising: software engineers using an internal AI coding assistant saw a 10% productivity boost within weeks.

Amazon’s Machine Learning University (MLU) 

Amazon’s MLU, originally designed for software engineers, has been expanded to employees across functions. Structured as a series of 6-week modules taught by over 400 senior ML scientists, MLU provides a rigorous introduction to machine learning—effectively giving non-experts graduate-level exposure at no cost.

Why Upskill Your People

When it comes to product management in the AI era, the question isn’t whether you’ll need new skills—it’s how you’ll acquire them.

As previously mentioned, hiring can fill short-term gaps, but finding “AI-native” talent is difficult. This is becoming especially pertinent as generative AI continues to redefine job scopes across industries. A recent Stanford study found that since the widespread adoption of generative AI around 2022, early‑career employment in the most AI‑exposed roles fell by 13%, indicating how deeply generative AI is reshaping entry-level roles.

But there’s good news: investing in internal upskilling pays off—in productivity, retention, and long-term resilience. A joint PwC–Business-Higher Education Forum report shows that companies with the most advanced upskilling programs enjoyed more than three times the gains in innovation and digital transformation compared to those just getting started. 

In product teams specifically, this could mean faster iteration cycles, higher-quality decision-making, and PMs who can confidently lead in AI-integrated workflows. The joint report also found that 93% of CEOs reported productivity improvements, and workforce retention increased by around 5% after investing in upskilling.

Startups vs Enterprises: Different Stakes, Same Mandate

The shape of upskilling may differ based on company size—but the need is universal.

Startups often rely on scrappy generalists wearing many hats. For them, upskilling can mean giving PMs time to explore new tools, encouraging AI experimentation, or weaving AI questions into product discovery.

Enterprises, by contrast, may need structured programs, curated learning paths, and cross-functional support to scale new competencies across teams. But the goal is the same: unlock AI fluency where it matters most—at the product decision layer.

Mind the Gap: Evolving PM Roles and Skills

As AI reshapes product development, PM roles are evolving fast. Our CPO Report found that 64% of surveyed product leaders say that product managers are becoming more involved in prototyping and feasibility and 44% report engineers are contributing to product discovery.

In a recent Productboard webinar on Skills for the AI Era, Adam Judelson put it bluntly: “The non-technical PM is dead.” But that doesn’t mean every PM needs to become a machine learning expert. Instead, they need to become curious, conversant, and confident navigating AI’s possibilities.

Here are the emerging skills defining the AI-era PM:

• Data Fluency: Understanding how data is generated, stored, governed, and used to train models.
• Algorithmic Thinking: Framing problems in ways AI systems can solve, and interpreting outcomes.
• Ethical Judgment: Anticipating bias, hallucinations, and edge cases when building with AI.
• Growth Mindset: Embracing ambiguity, continuous learning, and fast feedback loops.

These aren’t just technical checkboxes—they’re pathways to career growth. As Judelson noted, PMs who can “think with AI,” test new workflows, and shape emerging patterns will be the ones leading the next wave of product innovation.

PM at Scale: How to Train Effectively

Equipping your product managers with AI skills isn't just about offering a few online courses—it requires thoughtful design and change management. As Tiago Leão emphasized in Productboard’s Leading Change in Product webinar, driving widespread PM upskilling demands clarity, buy-in, enablement, and reinforcement.

• Clarity: Articulating why the change is needed and what it will impact, grounded in data.
• Buy-In: Showing stakeholders “what’s in it for them” to foster alignment.
• Enablement: Using practical demos and starting small before scaling.
• Reinforcement: Sharing wins and re-communicating the message regularly.

With that in mind, here are five principles to help you scale training effectively across your product org:

1. Make It Real and Relevant

Generic AI theory won’t stick. Ground training in real product challenges. Use your own customer problems, product surfaces, and internal use cases to contextualize AI learning. Keep AI learning tied to daily work to reduce abstraction and increase adoption.

2. Layer the Learning

AI fluency is not one-size-fits-all. Provide beginner-to-advanced pathways so every PM—whether they’re AI-curious or technically inclined—can progress. Implement a “laddered skill model” that builds confidence incrementally: start with AI fundamentals, then move to tools, workflows, and finally, experimentation.

3. Learn by Doing

Don’t just teach—prompt action. Encourage hands-on exploration through hackathons, workflow trials, and safe-to-fail experimentation. Ask PMs to try using ChatGPT or Claude to synthesize research or brainstorm use cases. Early wins build long-term confidence.

4. Foster a Peer Learning Culture

Upskilling is more sustainable when it’s social. Launch internal AI learning forums, schedule lunch-and-learns, and identify “AI champions” within product teams who can mentor others. Peer-to-peer learning accelerates comfort with AI tools and drives organic adoption.

5. Build Ethics and Guardrails Into Training

Training shouldn’t just cover what AI can do, but also what it should do. Include principles of responsible AI in your curriculum. It’s table stakes to be able to anticipate hallucinations, bias, and misuse in AI-augmented features.

Lessons for Change Management at Scale

Beyond these five principles, Tiago reminds us that successful change isn’t just about content. It’s about how you deliver it. Change often fails because we underestimate resistance and fatigue, or because we roll out too broadly, too fast. Instead:

• Start small: Pilot with a smaller PM group, build proof points, then expand.
• Measure and reinforce continuously: Share adoption metrics, highlight quick wins, and celebrate progress to keep momentum.
• Treat training like a product: Define the problem, engage PMs as “users,” iterate based on feedback, and validate before scaling.

The key takeaway: a structured yet hands-on training approach will demystify AI for PMs, build trust in the tools, and accelerate confident adoption across your org.

The Real Differentiator: Confidence

The real differentiator won’t be the tools you buy, but the confidence and fluency your teams build in applying them. By treating training as an ongoing, structured, and hands-on process, product leaders can close the skills gap, future-proof their orgs, and empower PMs to lead with clarity in the AI era.

Want to dive deeper into what today’s product leaders are prioritizing?

Download the full CPO Survey to see how executives are navigating the AI shift—and where they’re placing their bets for the future.