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The Future of AI Product Management Roles: What Every Product Leader Should Know

ai product management roles digvijay singh
Blog Author: Rina Alexin

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The Future of AI Product Management Roles: What Every Product Leader Should Know

AI isn’t coming for your job. It’s coming for your ambiguity. 

That was one of the biggest takeaways from our conversation with Digvijay Singh, Senior Product Manager at YouTube Ads, who’s spent years operating at the intersection of machine learning, data science, and product strategy. In our latest episode of Productside Stories, Digvijay unpacked the way AI Product Management roles are constantly shifting — and what it really takes to succeed in each layer of this fast-shifting stack. 

For seasoned product leaders or ambitious PMs who want to skill up your AI fluency, here’s what you need to know. 

 

The Three Layers of AI Product Management

When most PMs think about AI, they picture flashy applications: copilots that write code, chatbots that sell, or recommendation engines that predict the next binge. But Digvijay breaks the ecosystem down into three distinct layers, each with its own product mandate, skill set, and business impact: 

  1. Infrastructure Layer: These are the PMs building the foundations: data centers, GPUs, cloud clusters, and the pipelines that feed model training. 
  2. Model Layer: These PMs define and refine large-scale models themselves (from LLMs to diffusion and retrieval models) setting the metrics that make intelligence measurable. 
  3. Application Layer: This is where most product managers live today. It’s about embedding those models into products that deliver real user value — from ad optimization to creative generation. 

Each layer requires a different product mindset. The Infra PM thinks in capacity, cost, and latency. The Model PM thinks in evals, fine-tuning, and step-change innovation. The Application PM translates it all into customer outcomes. 

As these AI Product Management roles mature, PMs will increasingly specialize by layer, while leaders who understand the entire stack will become the new strategic connectors inside their orgs.

 

From Roadmaps to Evals: The New Currency of Success

Traditional product managers live and die by OKRs. But in AI, success looks different. 

“You don’t define success through OKRs anymore,” Digvijay explains. “You define it through evals.” 

In other words, you’re not just measuring product outcomes. You’re training the system to recognize what good looks like. Evals (short for evaluations) are the new success metrics of AI Product Management, defining the ground truth your model learns from. 

Imagine you’re optimizing ad experiences on YouTube. Your evals might measure when a user chooses to skip an ad, how long they engage, or whether the ad matches contextual intent. These metrics don’t just tell you performance — they teach the model what to do next. 

For product leaders, that’s a profound shift. Your “requirements doc” now doubles as a training framework for the product’s intelligence. You’re not just writing user stories; you’re writing instruction sets for learning systems. 

This mindset marks a major evolution in AI Product Management roles, where PMs are now responsible not just for outcomes—but for shaping how machines learn to deliver those outcomes.

 

Every PM Is Now an AI PM. But Not Every PM Knows It

There’s a popular belief that “every product manager is now an AI product manager.” Digvijay agrees, but there’s a twist. 

Yes, AI will touch every product surface eventually. But true AI product management demands a new layer of intuition: understanding how models think, fail, and improve. 

That doesn’t mean you need to code or build neural nets. It means you need to know enough about AI model behavior, data dependencies, and evaluation techniques to guide cross-functional decisions with credibility. 

The best AI product managers don’t ask, “Can we add AI to this?”  they ask, “Where does intelligence actually create value?” 

That distinction separates the resume titles from the operators. 

The most impactful AI Product Management roles will belong to PMs who can answer that question with clarity, anchoring their strategy in business value.

 

The Rise of the Model-Layer PM 

While most PMs today work in the application layer, Digvijay predicts a surge in demand for model-layer PMs: the ones defining how LLMs, multimodal models, and agentic systems evolve. 

Why? Because that’s where the step-change innovation is happening. 

“Model-layer PMs get the best of both worlds,” he says. “They can leverage all the infrastructure that’s being built and still invent new applications that didn’t exist even a year ago.” 

These roles demand deep cross-disciplinary fluency (understanding data pipelines, evaluation frameworks, and ethical considerations) and a willingness to think from zero to one. 

If infrastructure PMs scale capacity and application PMs scale adoption, model-layer PMs scale possibility itself. 

 

AI Skills for Product Managers: What to Master Next 

So, what does it take to thrive across these AI product management roles? According to Digvijay, there are two must-have skills every PM should invest in this year: 

  1. Building Product Intuition with AI Tools
    – Spend time with the products shaping the field — from Claude and Gemini to Midjourney and Synthesia.
    – Don’t just use them; reverse-engineer how they work. Ask: what’s the model optimizing for? What data patterns is it learning from?
    – This hands-on curiosity is how you’ll sharpen your instinct for what’s possible (and what’s hype). 
  2. Writing Evals Like a Teacher, Not a Tester
    – Think of the model as your intern: you have to train it on what good looks like.
    – Every eval you write is a learning scaffold. Clarity, context, and edge-case thinking matter as much as the product spec itself. 

Together, these skills elevate PMs from “AI users” to AI architects: product leaders who can shape how intelligence itself gets built. 

The Next Frontier: Product Strategy at AI Speed

Digvijay predicts that the market will soon value PMs who can operate across multiple layers: fluent enough in infra, model, and application to connect the dots between technical capability and commercial strategy. 

In other words: the new 10× PM isn’t the one who ships faster. It’s the one who decides smarter. 

That’s the future of product leadership in the AI era. not managing roadmaps, but managing reasoning. 

 

Lead the Shift. Level Up Your AI Product Management Roles

  • Listen to the full conversation. Catch Digvijay Singh and Rina Alexin on Productside Stories — available now on Spotify, Apple Podcasts, and Amazon Music. You can also watch it on YouTube for the complete interview.
  • Take your understanding of AI PM roles even further with our AI Product Management Certification.
  • Now it’s your turn. How are you evolving your AI Product Management roles into 2026? What new skills or frameworks are helping your teams bridge strategy, data, and intelligence? Share your thoughts and tag Productside on LinkedIn — we’d love to hear how you’re leading the next wave of product innovation through AI.

About The Author

Rina Alexin

Rina Alexin, the CEO of Productside holds a BA with honors from Amherst College and an MBA from Harvard Business School. She is also a member of the AIPMM.
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Frequently Asked Questions

AI Product Management roles generally fall into three categories: Infrastructure, Model, and Application. • Infrastructure PMs manage compute systems, data pipelines, and AI infrastructure. • Model PMs focus on training, evaluation (evals), and improvement of AI models. • Application PMs integrate these models into real-world products that deliver business and user value. Each role requires a different blend of technical fluency, strategy, and product intuition.
The most in-demand AI skills for product managers include: • Evaluation writing (evals): Defining success metrics for model performance. • AI literacy: Understanding how models think, fail, and learn. • Data intuition: Knowing what data powers your models and how to identify biases. • Cross-functional collaboration: Partnering with data science, engineering, and ethics teams. • Strategic synthesis: Translating technical possibilities into business outcomes. AI PMs who can bridge these skills will shape the next generation of intelligent products.
Traditional PMs focus on outcomes and user needs, while AI Product Management roles add a new layer: teaching systems how to achieve those outcomes autonomously. Instead of just defining OKRs, AI PMs write evals—training frameworks that help AI systems understand “what good looks like.” They also manage a more complex intersection of data, ethics, and model behavior, requiring both strategic and technical decision-making.
Not necessarily, but technical fluency is increasingly expected. You don’t need to code, but you should understand: • How models are trained and evaluated • The basics of machine learning workflows • Common AI pitfalls like bias, hallucination, and overfitting PMs with business acumen and strong collaboration skills can thrive as long as they can speak the language of their technical teams.
Start by building AI literacy and developing hands-on experience with generative tools like ChatGPT, Gemini, or Claude. Next, take a structured course like Productside’s AI Product Management Certification to understand frameworks for evaluating and applying AI in real business contexts. Finally, demonstrate your value through AI-driven projects—even small internal experiments can show you understand the bridge between technology and outcomes.