AI ambition in financial services is high.
But practical adoption remains unclear.
At CFTE’s UK–Singapore Exchange, held during UK FinTech Week, Kenneth Gay, Chief Fintech Officer at the Monetary Authority of Singapore, addressed one of the biggest gaps facing financial institutions today: while AI is widely discussed, many organisations are still trying to understand how to adopt it in practice.
The issue is not a lack of interest. Financial institutions recognise that AI has the potential to transform operations, customer experience, risk management, compliance, and decision-making.
The challenge is knowing where to begin.
The Adoption Gap
Many financial institutions are still asking fundamental questions.
What AI solutions actually exist? Which ones have already been deployed successfully? How should they be assessed? How can they be implemented safely? What risks need to be managed before they can scale?
These are practical questions, and they matter.
AI adoption cannot move forward on ambition alone. Institutions need visibility into what works, what is safe, and what can be scaled responsibly.
As Kenneth highlighted, this is not simply a technology problem.
It is a navigation problem.
Financial institutions are not necessarily blocked by a lack of AI tools. They are often blocked by uncertainty: uncertainty about which use cases are mature, which solutions are credible, which governance standards apply, and how to move from experimentation to implementation.
From Fragmentation to Shared Learning
One of MAS’s key responses to this challenge is the Pathfinder programme.
Rather than leaving institutions to explore AI in isolation, Pathfinder is designed to support more structured and collective learning across the industry.
It provides:
- industry-level AI use cases
- curated solutions across domains
- shared best practices from institutions further ahead
The objective is to move from fragmented experimentation to shared progress.
This is important because many institutions are still duplicating efforts. They may be solving similar problems independently, testing similar tools, or exploring similar use cases without benefiting from what others have already learned.
In a fast-moving AI environment, this slows adoption.
Shared learning can help institutions move faster, avoid repeated mistakes, and build on proven approaches.
The Four Pillars of AI Readiness
Kenneth outlined a broader framework underpinning Singapore’s approach to AI in financial services.
The first pillar is adoption. Institutions need support in discovering, assessing, and implementing real AI use cases that are relevant to their needs.
The second pillar is capability. AI adoption requires more than access to tools. It requires competency, expertise, and dedicated centres of knowledge that can help institutions understand how to apply AI effectively.
The third pillar is governance. Financial institutions need practical, industry-led frameworks for managing AI risks, ensuring accountability, and building trust in deployment.
The fourth pillar is talent. AI will change jobs, skills, and ways of working. This means institutions must invest in upskilling, reskilling, and workforce transformation.
These four pillars are supported by a fifth dimension: international collaboration.
Together, they reflect an important shift. AI readiness is no longer about isolated initiatives. It is about building ecosystem-wide capability.
The Real Challenge: Knowing Where to Begin
Perhaps the most important insight from Kenneth’s session was that many institutions are not blocked by technology.
They are blocked by uncertainty.
Without clear visibility into what works, what is safe, and what is scalable, AI adoption slows down significantly.
This uncertainty affects decision-making. Institutions may hesitate to invest, delay implementation, or keep AI initiatives at the pilot stage because they are unsure how to evaluate solutions or manage risks.
That is why ecosystem-level initiatives matter.
They help reduce uncertainty by creating shared reference points, practical examples, and clearer pathways for adoption. They also help institutions understand where they are on the AI journey and what capabilities they need to build next.
From Individual Effort to Ecosystem Capability
AI adoption in finance will not be solved by individual firms acting alone.
Financial services is a highly interconnected and regulated industry. Adoption depends not only on what one institution builds, but also on shared standards, regulatory confidence, talent development, trusted solutions, and cross-border collaboration.
This is where Singapore’s approach offers an important lesson.
By focusing on adoption, capability, governance, talent, and international collaboration, the ecosystem can move from ambition to practical implementation.
The goal is not simply to encourage more experimentation.
It is to help institutions adopt AI responsibly and at scale.
The Takeaway
AI readiness is not just about building models.
It is about building an environment where institutions can learn faster, adopt responsibly, and scale collectively.
Kenneth Gay’s contribution at CFTE’s UK–Singapore Exchange highlighted a critical point for the industry: financial institutions do not only need more AI ambition. They need clearer pathways to adoption.
The future of AI in finance will not be built by individual firms alone.
It will be built by ecosystems that enable shared progress.
