Product Innovation in an AI World. How do I compete?

I hear this question most weeks now. In conversations with leaders and business owners who have just watched a competitor announce an AI feature or a new product integration. Their concern is real. If everyone has access to the same models, the same tools, the same clever prompts, where exactly does my edge come from?

Here's what I believe after being part of enough of these conversations: AI capability is becoming a commodity. What you can buy today, your competitor can buy tomorrow. The edge was never the model. It's how well you understand your customer's friction, your own data, and the way work actually flows through your business.

Let me put two organisations side by side. Both are composites, but the detail is based on real conversations I have been part of over the last year.

The first shipped an AI assistant inside their customer portal. It could answer questions, summarise documents, draft replies. The demo was superb. The leadership team loved it. The launch post did well. Six weeks later, usage was flat. When we dug in, the reason was almost embarrassingly simple. Their customers didn't come to the portal to ask questions. They came to check an order status and leave. The assistant was solving a problem nobody had. It was clever, and it was beside the point.

The second organisation did something far less visible. They started with their support inbox and a spreadsheet. Three questions kept coming up, week after week, and each one took a staff member ten minutes to answer because the information sat across two systems. They used AI to pull that information together and pre-draft the reply. No new product announcement. No launch. Within a month, response times had halved and the team had their afternoons back. Customers noticed, not because of the AI, but because things simply got easier.

Same technology. Very different outcomes. The difference was never the capability. It was the clarity and understanding of the exact problem.

Pause on that for a moment and I think we all land on the same truth. In an AI world, the competitive edge moves upstream, into the unglamorous work of understanding your own business. If you're wondering where to start, here's what I'd do:
🔷 Map the friction before you map the features. Ask your customers, and your team, what is genuinely annoying and where they get stuck. Start there, not with what's possible.
🔷 Know your data. Where does it live, how clean is it, who owns it? AI on messy data is expensive noise.
🔷 Follow the workflow, not the tool. Walk one process end to end and note every handover, every wait, every “let me check.” Those are your opportunities.
🔷 Fix the known before chasing the new. Ten small improvements to existing friction usually beat one flashy new feature.
🔷 Measure use, not launches. If your customers or your team aren't reaching for it, it isn't working, however good the demo.

None of this is exciting. And that's rather the point. Your competitors can copy your AI feature in a quarter. What they cannot copy is the years you've spent learning what actually frustrates your customers, and the discipline to fix that first.

Here's my reflection for you this week. It isn't “which AI should we adopt?” It's this: do you understand your customer's friction well enough to know where AI would make a real difference, and where it would just be noise?

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