I want to start with a number that I find both striking and entirely unsurprising: the majority of AI automation programmes that underperform their projected value do so not because the technology fails, but because adoption falls short of expectations. The agents are running and the processes are automated, but the people who are supposed to be working with the system are partially or entirely working around it. They’re reverting to manual processes, duplicating work, or simply not using the capability that’s been built.

I have sat across the table from a lot of CFOs presenting business cases for AI programmes. I have seen cases get approved, shelved, sent back for revision, and, on a handful of occasions, killed at the last moment despite months of preparation. The pattern that distinguishes the cases that succeed from the ones that fail is not the quality of the financial modelling, it is whether the case speaks the CFO's language. 

Financial services are, in some respects, the ideal environment for AI process automation. The processes are complex, high-volume, and rule bound. The data infrastructure is mature. The regulatory requirement for consistency and auditability is not just a compliance burden, it is actually an argument for automation, because AI agents apply rules consistently in ways that human processing cannot guarantee. 

There is a moment in most AI automation programmes that I have come to recognise as a warning sign. It usually happens about six months after go-live. The initial metrics look fine — hours saved, error rate down, costs reduced. And then someone in the boardroom asks: 'So how is the AI programme actually performing?' And the team in the room realise they are not entirely sure how to answer.

The most common question people ask when AI automation comes into their team isn't 'will I lose my job?'

It's 'will anyone ask what I think about this?'

That difference matters more than most organisations realise.

The teams who adapt to working alongside AI agents most effectively aren't the ones who were reassured it wouldn't change their role. They're the ones who were involved in designing how it would.

Process knowledge lives with the people doing the work. The best automation designs come from them — from the person who knows where the friction is, where the errors creep in, where they're spending time on work that feels beneath their capability.

There is a version of the AI governance conversation that I find frustrating. It tends to involve a lot of abstract principles such as accountability, transparency and fairness. However, there is very little operational guidance on what any of those things actually mean when you are designing an agent that will process 20,000 patient records a week, or flag transactions for a regulated investment firm, or manage prescription reauthorisations for a hospital network.

Principles are necessary but insufficient. The organisations that govern AI well are the ones that have translated principles into specific, operational controls and the ones that have tested those controls before they ever needed them.

Numbers like $750 million have a way of sounding abstract. Let me make it concrete.

That figure represents the measurable value VOPS has delivered to a single US healthcare provider over a multi-year automation programme.

Most AI pilots succeed.

Most AI programmes don't scale.

That gap — between a proof of concept that works and a digital workforce that delivers enterprise-wide impact — is where we spend most of our time.

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