We take it fairly serious in the public sector in Denmark. We’re doing our own POCs and we’re inviting partners to build things with us. It’s very much a hit’n’

We take it fairly serious in the public sector in Denmark. We’re doing our own POCs and we’re inviting partners to build things with us. It’s very much a hit’n’miss field. It works with recognition. It helps us sort through millions of page files looking for missing documents, both faster and with higher hit rate than using people. It helps us determine how much water is in drone photographed land areas based on the colours of the vegetation. Stuff like that. Where it absolutely doesn’t work is for prediction and analysis. Well, it’s not that it doesn’t work in BI as such. I kinda does, but we’ve been working with analysis and BI for 25-50 years. IBM wanted to sell us Watson analytics for BI as an example. We let them run their magic on some of our non-sensitive datasets and they came up with a range of interesting BI models and metrics. Every one of them were far, far inferior to our current human build BI setup, however, and a subscription to them would cost us around three full time analysts. Prediction is much worse. The probability just isn’t there to use it for anything that isn’t as harmless as advertising. Worse than that, almost every model turns out biased. So I guess you can say that I agree with you. It’s mostly just hype, but those parts that aren’t hype are incredibly useful.