So you've seen with the cost of tokens that Claude, for example, used to be very generous with the amounts you could get from them and then suddenly presumably in their organization someone's saying hang on we're not profitable or not profitable enough you know a multi-billion dollar company um but they they're trying to prepare for their ipo and they're saying well we need to make sure we're we're giving less tokens and uh for for charging more money um so that has a knock-on effect then on businesses so you're right the immediate thing which a business will see will be the higher costs coming in um but then those results aren't coming in to justify those costs yet so how do you balance those two things

so how do you balance those two things i think and it might sound really simple but businesses large and small and everyone in between should and could spend more time on going deeper into the qualification and the debating and the discovery side of setting up on the right path with AI.

The thing about AI is, and particularly from the world I came from, which was less working with things like Claude and just being able to connect it to things and just go and prompt it.

It was more what I would class as sort of enterprise AI, building an AI platform that can scale, deploying models at scale, building the cold ML workflow that goes behind it.

I think organizations can just go deeper into what outcome are they looking for from AI, because that is the whole purpose.

The whole purpose is to create an outcome.

And that can be many different things.

Could you maybe share a few examples of some of the KPIs that you think are useful for businesses to evaluate this sort of return on investment and this kind of productivity gain?