Where does it show up in a way that's, that's quite similar, and where does it differ, do you think?

It's a great question.

I mean, I think in terms of the actual use cases and the value that can be unlocked through AI, there are far more similarities than differences between the public and private sector.

So citizen-facing applications like the ones that Miguel mentioned or that I discussed, chatbots, smart search, ways of streamlining the client experience, those are equally applicable between government and, say, a large financial institution or a telco.

I think where there is the most obvious difference is what's at stake when things go wrong.

For those who are working in private corporations, there is more room, I think, to test, to try new things with AI, to learn from failures, and to pivot.

And there may, of course, be consequences for customers and for shareholders, but those are generally somewhat contained.

Where were there similarities in the challenges? Where were there things that were perhaps unique to regions? Daniel?