The research Turing Meta utilises for businesses to benefit from is complexity science. So what is 'complexity science'? It is really just another attempt to re-draw the map of different specialisms, using both new mathematical methods and computer science, based on an intuition about how subjects are really related. The approach Turing Meta has taken, for example, involves seeking to establish a strong connection between organisational economics and biology.
The motivating idea for all complexity scientists is that, in some sense, the way we look at research domains today still resembles the way that biologists looked at biology before the discovery of DNA. Before DNA we classified animals by what they looked like. Now we know by DNA analysis that what we call wasps (yellow and black striped insects that live together in hives) are part of the same genetic family as other insects which look just like flies, and are solitary and parasitic. In the same way, we are unable to say which domains are really closely related because we don't understand which domains share the same 'DNA'.
The same thing may be true of the family of problems we study called organisational economics and problems of understanding cell differentiation in biology. They are actually closely related, but superficially, they look different. If you disagree, you may perhaps believe the way that we have classified these two different domains, biology and economics, is correct. In this case, you are making the opposite 'bet' which is that we already understand the 'DNA' of these two domains pretty well. Perhaps how you feel about this sort of bet depends on your intuition. However, science = counter-intuitive; mathematics is arguably only common-sense with a super-powered prosthesis, but that extra power makes it harder to come by without some extra effort. See Lewis Wolpert's book 'The Unnatural Nature of Science'.