AI in financial services is not lacking use cases.
It is lacking clarity.
At the Global AI Governance and Innovation Showcase, Colin Payne contributed to an important discussion on how the industry can make sense of AI adoption in practice.
The Meaning of “Using AI”
One challenge stood out clearly:
When organisations say they are using AI, what does that actually mean?
It could mean many different things:
- an idea
- a proof of concept
- a prototype
- a pilot
- an internal tool
- a deployed system
- a production-level solution
Without a common language, it becomes difficult to compare progress across institutions.
It also becomes difficult to understand what is genuinely working.
The Challenge of Comparison
AI adoption is accelerating across financial services.
But without structure, the industry risks confusing activity with progress.
A large number of experiments does not necessarily mean meaningful transformation.
To scale AI effectively, organisations need to understand:
- the maturity of each use case
- whether it is still experimental
- whether it is live in production
- where value is being created
- what risks need to be managed
This requires more than enthusiasm.
It requires classification.
Creating a Common Language
A central theme from the discussion was the need to bring structure to AI adoption.
That means developing a clearer way to:
- define use cases
- distinguish ideas from implementation
- compare maturity
- identify value
- understand what can scale
The goal is not to make AI more complicated.
It is to simplify how the industry understands it.
The Takeaway
As AI adoption grows, the real advantage will not come from experimenting more.
It will come from understanding better.
Colin’s contribution highlighted a critical point for the industry:
To scale AI responsibly, financial services needs clarity, structure, and a common language for what progress really looks like.
