So how do we get that balance right?

So look, I think, and I share a common love of both sports and business, I think, with you, right? And as I said, in writing the book, I read every book on innovation and metrics and finance and other things and ended up in the world of sports and digging into and speaking with various folks that were around the development of the metrics of sport, the Moneyball side, the expected goals piece and everything else and looking at how we might translate that into innovation world right like there are parallels and there are similarities and there are things that just don't carry across as well i think i just did a big exercise so over the last 15 plus years We've run more complex innovation programs probably than anyone else on planet Earth, right? It's a big claim, but probably more than anyone else on planet Earth.

And what I therefore have is a huge data set that we could, and a corpus of data that I could really analyze.

And I think this gets to the heart of some of the fears of AI and the role of the human intelligence versus the machine in the things that we are doing as well, which is if I go through and assess all of the ideas, all of the solutions to some of the world's most complex, deeply technical problems over the years, and I replicate those with synthetic data as well, then I get experts to evaluate them and look at them.

There are some incredible on-paper solutions, things that would look amazing.

And to your world, there's some incredible players that on paper look amazing.

However, time and time and time again, it is the idea, the solution in the middle or even the lower quartile that ultimately gets selected and awarded by the company, by the need holder of that specific piece.

How does a leader in simple terms embrace benefits that technology gives us in terms of building and analyzing multiple data sets, but at the same time, tap into the human intelligence within their organization, the intuition, the experience, the instinct, the gut feeling of the people that work there and make sure that they can embrace that data without becoming overly reliant on it and losing that human connection.