If Your AI Isn't Working, Fix The Foundation

Merlin Bise is the CTO of Inbenta. He has built conversational AI since before generative AI.

​A lot of the AI deployed into the customer experience over the past three years isn’t performing. That’s just the reality. And the default response I hear, over and over, is to start all over again, evaluating and paying for a new platform.

In most cases, that’s an expensive way to rebuild the same problem.

I’ve been building AI for customer experience since before generative AI showed up. I watched intent matching give way to generative layers, and now we’re into agentic systems that can reason and act on their own. The platforms keep changing. But it isn’t the model that causes the deployment to fail—it’s the knowledge underneath it. I’ve seen that pattern in nearly every underperforming deployment I’ve looked at, going back years.

And look, none of this is a knock on the decision you made three years ago. You made it with the tools that existed then. Those were very different tools.

Here’s what’s new. Fixing the knowledge layer used to be a multiyear, hand-built project. That’s why re-platforming seemed faster, even when it wasn’t. AI now does the heavy lifting up front. It reads, structures and tags your content before anything goes live, so what customers hear is approved, not improvised. Work that used to take a room full of contractors a full year can now get done in weeks, not quarters. If you wrote off your knowledge problem 12 or 18 months ago as too slow or too expensive, it deserves a second look and, honestly, a lot more optimism than it would have earned back then.

Before you run another evaluation, check three things. You can diagnose all of them without a new vendor.

1. Was your content built for machines or for people?

2. How many knowledge stores do you have?

3. Who’s allowed to change an answer?

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?