Why AI Needs Product People More Than Ever

There is a lot of noise about AI. The pace of progress is jaw-dropping. Tools are launching weekly, models are growing more capable, and entire industries are being reimagined.

But amid all the hype, something important is missing. The technology is moving fast. The value is not always following.

That is where product people come in.

The future of AI is not just a technical problem. It is a product problem. We do not just need smarter models. We need smarter ways to apply them. That means we need people who know how to translate capability into outcome. We need people who know how to build useful things.

In other words, we need product people more than ever.

The AI Hype Cycle Has a Delivery Problem

Anyone working in product has lived through a few hype cycles. Blockchain. The metaverse. Voice interfaces. Each one follows a similar curve. Early excitement. Wild claims. Massive funding. Then a reckoning.

AI is different because the underlying tech is real and incredibly powerful. But that does not mean it always leads to value. What we are seeing now is a flood of capability without clear application. Teams are shipping features just because they can. Startups are launching on the back of prompts and wrappers. Enterprises are layering AI into legacy systems without thinking deeply about use case or user experience.

The result? Demos that wow and products that underwhelm.

What is missing is not the technology. It is the product thinking.

Why Product Thinking Is Critical to AI

Product people are trained to ask better questions. Who is the user? What problem are we solving? What does success look like? Where is the friction? How do we know we are making it better?

These are not just good questions. In an AI context, they are essential.

AI models can generate. They can predict. They can optimise. But they do not know what matters. They do not know what is valuable to a human. They do not know when a solution is overkill or when a prompt is making life harder, not easier.

That judgement comes from product craft. From proximity to users. From the discipline of trade-offs and iteration. From being in the messy middle between technical feasibility and business need.

I have seen this first-hand at Guild, where we embedded AI features into our professional community platform. We did not just ask what we could automate. We asked where our users were struggling. We asked how we could help them find more relevant connections, faster. We used embeddings to power that. But the insight came from listening to users, not chasing novelty.

AI Without Product Is Just Infrastructure

Imagine giving a jet engine to someone without a plane. That is what most AI projects feel like when they are driven by tech alone. Impressive power, no lift-off.

A strong product person changes that. They turn raw capability into coherent experience. They shape inputs and outputs. They define constraints. They know when to say no. And they help teams ship something that actually makes sense to a user.

In AI, this matters more than ever. Because the stakes are higher. AI can create harm as easily as it creates value. If you do not have a product person in the loop, you run the risk of building something that is smart but misaligned. Something that works in the lab but not in the real world.

We have already seen this in the enterprise space, where AI pilots run for months and then get shelved. Not because the model failed. But because nobody cared enough about the product experience.

The Role of Product in the AI Era

So what does it mean to be a product leader in the age of AI? It means a few key things:

1. Be a translator
You do not need to be a machine learning expert. But you need to understand enough to translate between engineers, designers, and stakeholders. You need to bridge the gap between what is possible and what is useful.

2. Ask better questions
Start with the problem. Always. AI can do a lot of things. That does not mean it should. Product leaders need to focus the team on real needs, not just shiny tools.

3. Embrace experimentation
AI is probabilistic. That means it behaves differently than traditional software. It requires a mindset of testing, learning, and iteration. Product leaders are already wired this way. Now they need to apply that skill to a new domain.

4. Champion ethics and guardrails
You are not just building a feature. You are influencing how a system behaves at scale. Product leaders need to ask what could go wrong. Who could be excluded. What assumptions are baked in. And then make sure the team designs for that from day one.

5. Build for trust
AI can feel magical or unsettling. Often both. Good product design builds trust. That means clear feedback, human oversight, and thoughtful UX. This is not a nice-to-have. It is make or break.

AI is changing everything. But it will not do it alone. It needs the discipline, empathy, and clarity that great product leaders bring.

We are the ones who turn potential into progress. We are the ones who make the complex feel simple. We are the ones who know how to listen, shape, test, and ship.

The future of AI belongs to builders. Not just the ones who write the models. But the ones who make them matter.

If you are a product person wondering where you fit in the age of AI, the answer is simple.

Right in the centre of it.