One of the biggest challenges in AI today is not finding use cases.
It is making sense of them.
Across financial services, AI adoption is accelerating. Institutions are experimenting with new tools, building prototypes, launching pilots, and exploring how AI can improve productivity, decision-making, customer experience, risk management, and compliance.
But as activity increases, so does complexity.
At the Global AI Governance and Innovation Showcase, Huy Nguyen Trieu shared the thinking behind the AI in Finance Observatory, highlighting the need to structure how the industry understands AI adoption in practice.
His message was clear: to move from experimentation to responsible scale, financial services needs a common way to understand what AI is doing, where it is being applied, and what value it is creating.
The Problem With “Using AI”
Many organisations now say they are using AI.
But that statement can mean very different things.
It could refer to an early idea, a prototype, a pilot, an internal experiment, a production system, or a scaled business solution. These stages are not the same.
An organisation exploring an AI concept is in a very different position from one that has deployed an AI system across a core business process. A pilot is not the same as a production-level solution. An internal experiment is not the same as a scalable use case creating measurable value.
Without clarity, it becomes difficult to understand what is really happening across the industry.
Are firms experimenting? Are they deploying? Are they scaling? Or are they still exploring?
This distinction matters because AI adoption cannot be measured by activity alone. It needs to be understood through maturity, value, risk, and real-world impact.
Why Structure Matters
AI adoption in financial services is becoming increasingly complex.
Use cases can appear across many areas of an institution, including:
- customer service
- compliance
- risk management
- operations
- fraud detection
- product development
- investment processes
Each of these areas has different requirements, different risks, and different measures of success.
AI can also create different types of value. Some use cases improve productivity. Others reduce costs. Some support better decision-making. Others improve customer outcomes, strengthen controls, or enable new business models.
Without a structured way to compare these use cases, it becomes difficult to identify patterns. It also becomes harder to understand which applications are genuinely creating value and which remain experimental.
This is why structure matters.
It helps the industry move beyond broad claims about AI adoption and towards a clearer view of what is actually being built, tested, deployed, and scaled.
From Complexity to Clarity
The approach presented through the AI in Finance Observatory is to map AI use cases across two key dimensions:
Where AI is being applied and what value AI is creating.
This provides a more practical way to understand AI adoption across the industry. Instead of treating all use cases as equal, it allows institutions, regulators, and innovators to see patterns more clearly.
For institutions, this can help identify opportunities and benchmark progress.
For regulators, it can support better visibility into how AI is being used across the financial system.
For innovators, it creates a clearer way to explain their impact and position their solutions within the wider market.
The goal is not to add complexity.
It is to make complexity understandable.
Building a Common View of AI Adoption
As AI becomes more embedded in financial services, the industry needs more than isolated examples. It needs a common view of adoption.
That means being able to compare use cases across functions, maturity levels, value types, and risk profiles. It also means understanding whether AI is being used to improve existing processes, transform business models, support compliance, enhance customer outcomes, or create entirely new capabilities.
This kind of classification is essential for responsible scaling.
It helps organisations understand not only what is possible, but what is practical, valuable, and ready for deployment.
It also supports a more informed conversation between financial institutions, regulators, technology providers, and the wider innovation ecosystem.
The Takeaway
As AI adoption accelerates, progress will no longer be measured by experimentation alone.
The next phase will require clarity, comparability, structure, and scale.
Huy Nguyen Trieu’s message at the Global AI Governance and Innovation Showcase was clear: the financial services industry needs a common way to understand what AI is doing, where it is creating value, and how it can be scaled responsibly.
AI adoption is not only about having more use cases.
It is about making sense of them.
