Why Internal AI Projects Fail

Today we're gonna look to talk a bit about the life cycle of AI projects and how they're undertaken, whether that's an internal or external project because they can both go well or poorly. What we found was unless certain things are in place at your organization or with your people staff expertise, really experience, a lot of internal AI projects kind of go awry and don't exactly get to the outcome that's always desired or that was expected. The quality of data relates to whether or not an internal AI project will succeed so we're kind of holding everything else aside and saying how do we isolate the data and ensure that the data is not the problem, cause that's really what we're trying to get to here. So you wanna make sure that you have very complete data in that sense because having just a little bit of the signal and only understanding. Missing data is going to lead to certain things weren't observed, and certain things weren't saved or cataloged, or curated, so you don't have a full picture. You only have a signal or two, at this point is kind of like a needle in a haystack, if you don't have a complete data set, and of course, just generally bad data is not gonna help you. So you wanna make sure that you have data in a way that's going to be usable down the line, clean data, so doesn't have all kinds of weird noise and bad signals in it, and you wanna make sure that labeled this data well. Another problem with all the silo data is we often end up with duplicate data sets. So we may be describing the same thing in slightly different ways, but across the organization, you will duplicate the effort, wasting people's time, and having systems that we're probably paying for, for no reason, that is saving the same thing.