I work in the public sector in Europe. We’re regularly approached by various ML projects. From universities to IBM, and we don’t turn them down, because we genu

I work in the public sector in Europe. We’re regularly approached by various ML projects. From universities to IBM, and we don’t turn them down, because we genuinely never turn down potential. Especially not something that’s on top of Gartner lists, or on the minds of our CEOs because they regularly hear about it from various Deloitte/E&Y types. This may sound like me being condescending, but I’m not, the same thing happened with RPA, and we’re reaping huge benefits from that despite the technical weaknesses of it. We have yet to make anything work in the real world with AI or ML that wasn’t some sort of image recognition. We let IBM use Watson to come up with various forms of analytics, but it didn’t come up with anything our analytics team wasn’t already building and it was frankly far behind them. Even if it had matched them, or surpassed them, the licensing would’ve likely made us stick with humans because they were just cheaper. Eventually it may be good and cheap enough, but one of the key issues we’ve often run into is that we don’t have enough data of a high enough quality and that’s honestly not very likely to improve. Even with the EU focusing on common enterprise architectures, and suppliers adopting/accepting that a police CaseFile always has a specific recipe to follow, they don’t necessarily stick to it outside of the APIs. It’s hard to name a specific failure rate. Because as far as recognising images, and trolling through millions upon millions of casefiles in tiff format, it works flawlessly. But for everything else, our anecdotal failure rate is probably 100%.