Artificial intelligence is now firmly established as an essential technology in financial services. This is backed up by a recent Bank of England survey, which revealed that 75% of businesses in the sector were already utilising AI, while a further 10% planned to do so over the next three years.
Crucially, these figures also reveal a substantial increase in adoption from just 58% in 2022, illustrating a marked acceleration in AI uptake. Furthermore, the survey notes the rapid adoption of complex machine learning types, with foundation models now forming 17% of all AI use cases.
However, even with widespread uptake, a common challenge remains. That is, the perception that truly impactful AI projects are inherently lengthy or risky undertakings, demanding substantial upfront investment and a long wait for tangible outcomes.
Such a sentiment can lead to an approach that is not only cautious towards deeper AI integration, but also overlooks advancements which now empower financial institutions to realise measurable gains in weeks, not years.
We address the swift application of AI by championing the accelerated realisation of value. As an IBM Silver Partner in the UK, we possess the expertise and direct access to IBM’s latest AI capabilities and expert implementation services.
Our focus is thus on fast implementation of systems that are ready to deploy, offering banks early successes that build both organisational confidence and a strong foundation for broader AI innovation.
A key factor in this speed is the availability of pre-trained foundation models such as Large Language Models (LLMs). Unlike traditional AI projects that demand extensive model building and training from scratch, foundation models are already trained on vast, often curated datasets.
Importantly, they can be grounded on the bank’s information repositories using a technique called Retrieval Augmented Generation (RAG) to incorporate AI into specific use-cases in HR, compliance and IT domains.
This helps customers and bank staff to retrieve relevant internal data to generate accurate and contextually appropriate responses. Such capability directly enhances customer and employee engagement and can free up human agents for more complex tasks.
Additionally, foundation models can be easily adapted and put into use within a bank’s environment, reducing the delivery cycle from months or years to four to six weeks. Our team’s deep understanding of these powerful tools also ensures their effective tailoring and safe deployment.
Another emerging area for rapid adoption of AI in banks is agentic AI. Most newer foundation models are now capable of reasoning, planning, and acting independently. This capability can be unleashed by AI agents, a specialised type of AI application that makes autonomous decisions within business processes and initiates actions based on these decisions. To further accelerate the adoption of agentic AI, pre-built domain-specific AI agents can be used.
IFINITY can deliver business value to banks by building solutions with IBM’s pre-built AI agents for HR, procurement and sales. This rapidly improves productivity by enabling bank staff to interact with processes in these domains conversationally and execute actions autonomously with properly defined guardrails.