The First Language of AI Products Is Not AI
Why AI Product Managers must master product thinking before they master technology
Product thinking is the critical skill. AI expertise is the optimizing one.
You need both, but only one determines whether what you build will matter.
There’s a subtle tension at the heart of modern AI product management, the kind you only begin to sense when a seemingly brilliant solution fails to land. It’s the tension between what AI makes possible and what users actually need, between technological potential and human value.
In many organizations, especially those racing to “unlock” AI, the excitement around the technology overshadows the deeper work of understanding real problems, testing what matters, and designing for adoption. There’s an unspoken belief that mastering architecture, benchmarks, and models will somehow lead to a successful product. But this is rarely the case.
The inconvenient truth is that no matter how deeply you understand AI, or how advanced your model performance is, if you don’t understand product, you won’t make AI work in the real world.
This isn’t a personal failure. It’s a recurring pattern. We’ve seen it before across technological waves—mobile, cloud, APIs, data science. Each time, technical skills surged, but adoption stalled, not because the tools weren’t powerful, but because the work of integrating them into actual behaviors, decisions, and workflows was never done.
AI adds a particular challenge. It feels like intelligence. It creates the illusion that it will naturally solve problems, because it appears to understand them. But intelligence is not understanding. And model performance is not product-market fit.
Building an AI product is not the same as using AI in a product. The second is about integrating AI into an already established product, usually as a feature that supports or enhances something that already works. The first still has to prove itself. It must solve a real problem with AI as the core of the solution, not as an addition. That makes it more complex, more fragile, and more dependent on the quality of decisions made long before the first model is ever deployed.
And those decisions begin in product.
What does it mean to speak the language of product?
It means starting with what’s broken, not with what’s impressive. It means identifying real user friction, not just chasing interesting use cases. It means defining what success looks like before deciding what kind of model might support it. It also means building for change, not just for performance. And not to be forgotten, it means building something others would be willing to pay for, or something that creates meaningful value when used internally across the company.
Product thinking teaches you to look at systems over time, not just point solutions. It demands that you test assumptions, reframe features, and integrate feedback into the foundation of what you build. It helps you create value that doesn’t just show well in a demo, but actually holds up in real-world use.
AI, for all its sophistication, doesn’t know what matters to your users. It doesn’t know whether people will trust probabilistic outcomes, whether your solution fits a real decision path, or whether adoption will quietly vanish after week two. AI doesn’t care if the user goes back to Excel.
That’s why product thinking must come first.
If I Had to Choose, I’d Choose Product
When it comes to AI Product Management, you can separate the skills — but you can’t separate their impact. In an ideal world, your AI PM brings both product intuition and technical depth. But if I had to choose just one, I’d always pick strong product thinking over semi-technical AI experience. Every. Single. Time.
Because technology does not define the outcome. Product thinking does.
I’ve seen technically skilled PMs struggle to frame a clear problem, align stakeholders, or prioritize for adoption. I’ve also seen product-driven PMs with minimal AI experience deliver far more impact, simply because they asked better questions, focused on value, and brought the right people in when it mattered. The truth is, AI can be learned. Judgment can be learned too. But knowing which one matters more — and when — is where most people get it wrong.
That doesn’t mean AI knowledge isn’t important. It is. But AI is a tool, and knowing how to build it is only part of the story. Product thinking is what tells you whether the problem is worth solving, and whether the solution will actually stick.
For Product Managers, product is the critical skill. AI is the optimizing one.
Without product thinking, you risk solving the wrong problem. Without AI knowledge, you risk solving the right problem too slowly or with unnecessary complexity. But if you have both, you make better decisions earlier. You shape discovery with technical realism. You speak your engineers’ language. You avoid building for novelty and aim for scale.
You stop treating AI like magic. You treat it like leverage. And you stop asking, “What can the model do?” and start asking, “What is the pain we need to address?”
Because in the end, great AI products are not built by those who know the technology best. They’re built by those who know when and why to use it.
And that begins with something far older than machine learning.
Lead with practical wisdom, not theoretical knowledge
The ancient Greeks made a distinction between two forms of knowledge that still feels surprisingly relevant today. Episteme referred to theoretical knowledge — knowing facts, concepts, and systems. Phronesis meant practical wisdom — knowing how to act, how to decide, and how to navigate uncertainty in specific, real-world contexts.
AI skills belong to the world of episteme. They are grounded in logic, in structured knowledge, in models that can be measured and improved. Product thinking, on the other hand, lives in the world of phronesis. It requires you to make sense of ambiguity, to work with incomplete information, and to guide people and ideas toward value in an unpredictable environment.
This is why product management, especially in the context of AI, is not just a function or a skillset. It is a philosophical discipline. It is the ongoing practice of deciding what matters, for whom, and why. It is the responsibility of shaping not only what gets built, but also how and to what end.
AI Product Management requires both kinds of knowledge. But it only works when guided by phronesis. When practical wisdom leads, and theoretical knowledge supports.
Either way, if you build what brings in value for the company by building things that add value for users, you have done your job.
JBK 🕊️
🧭 Continue your journey in AI Product Management
If you’re serious about building the right foundation for AI Product Management, here are some essential reads from blog:
Before the AI Product, There’s Belief
A personal reflection on why belief and trust are the invisible prerequisites of every internal AI product.Why AI Evaluations Have Never Been Optional for AI Product Managers
A guide on how to treat evaluation as part of product thinking, not just as technical due diligence.Most AI Teams Ship Confidently Into the Void – Prototyping as Discovery
Why prototyping should be a tool for learning, not validation, in internal AI development.A Curriculum for AI Product Management
A comprehensive roadmap of skills, mindsets, and methods for internal AI Product Managers.Too Technical to Succeed? – The Peer I Was Advising Was Me
An honest look at how over-relying on technical depth can derail product decisions.The Path to AI Product
A conceptual journey from AI use case to a real, adopted AI product—with language and framing that stick.
🔁 Looking for another sharp voice in AI Product Management?
I highly recommend AIPM Guru by
. With hands-on experience leading internal AI product development at large organizations like Amazon, Shaili brings a grounded perspective that balances product discipline with technical depth. She writes about stakeholder alignment, AI lifecycle management, model evaluation, and how to frame AI work in product terms—topics often ignored in overly technical discourse.Here are a few of her must-reads:
The Future of AI Product Management
A look at how multi-agent systems, adaptive models, and human-machine collaboration will reshape the role.AI Basics: What It Actually Is (And Isn’t)
A crisp explanation of what AI truly is, and how PMs can stay clear-eyed about its use.Can You Prove Your AI Moat?
A thought-provoking piece on measuring the defensibility of AI features in product contexts.CRISP-ML: A Framework for AI Product Managers
Her 8-part series laying out a comprehensive, repeatable framework for managing AI product delivery from start to finish.
Follow her at aipmguru.substack.com if you want to sharpen your practice and think more strategically about your role as an AI PM.