Governance gaps that can undermine your AI ROI

Artificial intelligence has become a significant area of investment for UK businesses.

More than £6 billion of new AI-related investment was announced during London Tech Week in June, while the UK remains home to the largest AI sector in Europe and the third largest globally.

From customer service and knowledge management to software development and internal operations, organizations are looking for places where AI tools can improve productivity , decision-making and business performance.

But as investment accelerates, another gap is becoming harder to ignore: organizations are often scaling AI faster than their ability to measure, govern and explain its value.

That matters when CIOs and CFOs are increasingly being asked not simply whether AI is being adopted, but what the organization is getting in return.

As businesses move from individual copilots towards agents embedded across workflows, the economics become more complicated.

A user making a single prompt is relatively easy to understand.

An agent may make multiple model calls, retrieve information, invoke tools and take actions to complete one task.

Depending on the platform and pricing model, that can introduce additional consumption, infrastructure and oversight costs.

CIOs therefore need to understand not only where AI has been deployed, but what it is doing, what it costs and whether the outcome justifies that cost.

The not-so-hidden cost of AI AI investment is increasingly being scrutinized in the same way as any other major technology investment.

The difficulty is that measuring its return can be unusually complex.

Usage may be distributed across departments, applications , models and workflows, while the benefits can range from time saved to improved quality, reduced risk or increased revenue.

Without agreeing what success means first, organizations can end up measuring activity rather than value.