Stop Measuring AI. Start Measuring Returns.

The biggest mistake organisations make with AI is assuming that proving the technology works is the same as proving it creates value. It isn't.

That was the standout statement from a session I was lucky enough to catch at the 8th Annual AI in Financial Services Conference in London earlier this month. By far my favourite talk of the two days came from Prerit Ahuja, Global Director for AI and Data Strategy at DNB Carnegie Investment Bank, and that single statement has stuck with me ever since.

The pilot honeymoon never lasts

Most AI programmes start the same way: excitement, funding and a run of impressive demos. The first few months are full of pilot success stories, productivity statistics and adoption metrics. Then the CFO starts asking questions and the business case begins to wobble.

Questions like:

• "Show me where this appears on the P&L."
• "Which costs did we actually remove?"
• "Would this survive an audit?"
• "What happens when it goes wrong in front of a customer?"

These questions get to the heart of a disconnect that plays out in boardrooms everywhere. Technology teams tend to focus on capability - what the AI can do. Finance leaders focus on outcomes - whether the business is materially better off because of it. Bridging that gap is precisely why value sits at the centre of Robiquity's way of working.

The metrics CFOs have learned to ignore

The benefits most claimed for AI today are also the one finance leaders have learned to discount, for example: hours saved, user adoption rates, pilot uplift percentages, and tool usage statistics.

That's not because these metrics don't matter. It's because they're rarely auditable. A CFO knows that two hundred hours "saved" doesn't automatically translate into thousands or millions of pounds of value. If those hours stay absorbed within the business - without changing costs, growing revenue or improving customer outcomes - then very little has changed from their perspective.

Shift the conversation from activity to outcomes

The fix is to stop reporting time saved and start reporting the decisions that followed it.

• Did an external supplier contract not need to be renewed because an AI-powered agent now handles the work internally?
• Did the organisation avoid an administrative hirebecause a workflow was redesigned around automation?
• Did revenue grow faster than headcount because agentic processes increased throughput and capacity?

These are the outcomes a finance leader can track, validate and get behind - because they show up where it counts.

CFOs don't need more demos. They need evidence. So, the most important question for AI leaders isn't "does the AI work?" It's “is the return real, and will it still be real next year?”

The organisations that can answer both with confidence are the ones that will successfully scale agentic AI from experimentation pilots into genuine enterprise transformation.

Struggling to translate your AI pilots into numbers financewill sign off on? Get in touch us to discover how we are helping our clients turn AI activity into evidence-backed returns.