Product-Led AI: How to Turn LLM Geekery Into Customer Outcomes

Engineers excited about LLMs, Agentic AI, RAGs, spending time going to the vendors, playing around like kids with new toys.

Without a real problem in mind. Without the ability to deploy any of it in the organization (the engineers don’t talk to the legal folks much…)

Can you think of where so much time and energy is spent on a technical innovation without connecting it to a real problem?

Of course you can… Mobile, Cloud, Big Data, just to think of a couple of recent examples.

To be honest, I can’t think of a significant technology wave WITHOUT this cart-before-the-horse behavior.

This is an example of tech/engineering-led behavior.

What would Product-led look like? Here is a non-comprehensive starting point to consider…

• Align around the customer outcomes we’re hoping GenAI will serve—e.g., improving the Developer Experience, the Shopper Experience, or the Fraud Detection Officer Experience.

• Empower the team to deliver outcomes - if Legal is a major issue for deploying AI, have someone on the team who understands their perspective and is ideally empowered to make some decisions. If Security/Risk is an issue, have someone from Cybersecurity on the team. Localize dependencies so that the team can try real things fast.

• Explore the problem/opportunity space - what are the biggest friction points/bottlenecks in the current experience/process that need solving?

How are you approaching your GenAI efforts? What might it look like to apply a product mindset, orientation, and operating model to developing your AI capabilities, even when they have nothing to do with your products?