“Sure. So there’s a lot of challenges. And one is that all of these algorithms run on data. At the end of the day, it’s about data, right? You need data. In order to drive these things. If you have crappy data or junk data, you’re going to get a poor result.
We run into the same intractable evergreen problems and they’re typically around data. In fact, semantic search and intent: classification is really, in many cases to make up for our sins in past content management and data management, right, because we use different terms to describe things are tagged things in different ways.
But again, that’s the nature of humans, humans are creative in both the way they solve problems and the way they present problems or characterize problems. So we’re always going to have that challenge. But at the end of the day, we need to curate data, we do need to have good data.
There’s a big misconception that AI is going to fix your data. Well, it can go a long way to helping to clean it up. But you still need a reference architecture, you still need terminology that describes your services, your products, your problems, your solutions an AI is not going to know highly specialized knowledge or information about your products, especially if you have a complex set of products and solutions. So we have to be able to understand that, curate it, manage it.”