We are still at that stage where the promise way exceeds the benefits. The verdict is still out. Yes we have the instinct that there is great benefit to be had. But showing the money remains elusive for most. We are at the value proof stage for Enterprise AI and this still requires deep work.

I have no doubt we will find evidence and there is a lot of it out there already that is growing. I argue that the way to evidence is through business process optimisation, use cases and rapid prototyping.

Process optimisation is one of the rising skills of 2025. For good reason I believe. Because it’s in business processes that we find use cases where AI can deliver optimisation. And where we can prove value.

Use cases are a series of building blocks or processes and currency of success that altogether deliver value outcomes. They are nothing more than how you do your work in a specific role or function or how you are trying to get a job done at work. Some super simple, others extremely complex depending on whether you are trying to submit an invoice for payment or produce a complex product like a car. All are open to disruption and evolution. And especially when technology is added to the mix.

And its through rapid experimentation or prototyping that we discover if there is any evidence of efficiencies to be gained.

That’s my thesis for the next stage of evolution for AI adoption in the enterprise in any event. And we need to innovate how we find solutions. I love the Jobs to be Done approach to innovation which can be applied equally to disrupting the value chain and business process optimisation.

Two good videos I watched recently on process optimisation, for your viewing and learning pleasure.