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AI platforms enable sustainable success

Reusable AI capabilities are crucial to scaling efficiently and reducing long-term costs.

AI platforms enable sustainable success

Under pressure to use AI, many in corporate and commercial banking have allowed teams to implement use cases in the quickest way possible. As a result, many are purchasing out-of-the-box tools and rolling these out. This approach may be quick, but it stores up problems for the future: AI use cases end up being underpinned by a patchwork of enabling technologies, tools and capabilities. This can make it difficult to scale.

Streamline and scale up with a reusable approach

A more sustainable tactic is to build a platform of foundational capabilities that can be reused across any use case. These capabilities include optical character recognition (OCR), machine learning, retrieval augmented generation (RAG) structures, vector databases and prompt libraries.

In the long term, this platform approach improves scalability and reduces costs by avoiding duplication of capabilities and the need to maintain multiple underlying systems.

“The only way to cost-effectively scale AI is to build reusable components to avoid creating every use case from scratch,” says Marie-Claude Ferland, Associate Partner, Consulting, AI Strategy, Ernst & Young Advisory Pte. Ltd. “These include summarization capabilities, or the various methods you need to validate and monitor models.”

The only way to cost-effectively scale AI is to build reusable components to avoid creating every use case from scratch. These include summarization capabilities, or the various methods you need to validate and monitor models.

Marie-Claude Ferland, Associate Partner, Consulting, AI Strategy, Ernst & Young Advisory Pte. Ltd.

You can't fast-track AI without solid fundamentals, like data, quality, architecture and security. Skip these and instead of scaling intelligence, you'll be scaling chaos.

Ulku Rowe, CIO, Commercial at Lloyds Banking Group

Ulku Rowe at Lloyds Banking Group agrees: “You can’t fast-track AI without solid fundamentals, like data quality, architecture and security. Skip these and instead of scaling intelligence, you’ll be scaling chaos.”

Building an AI platform helps banks to overcome issues associated with siloed and unconnected AI agents.

“Today, banks rely heavily on AI embedded into platforms they have used for years,” explains EY’s Matt Cox. “But these are siloed, which makes it impossible to link together processes in a service like credit. And without this linkage, it’s impossible to change how credit is provided and to improve client service offerings. A platform approach enables this.”

Today, banks rely heavily on AI embedded into platforms they have used for years. But these are siloed, which makes it impossible to link together processes in a service like credit. And without this linkage, it’s impossible to change how credit is provided and to improve client service offerings. A platform approach enables this.

Matt Cox, EY Global Corporate, Commercial and SME Banking Consulting Leader.

Key actions

Evaluate whether capabilities and models are reused for different use cases.
Shift to a platform approach, unifying tools, data and models to boost scalability and improve consistency.
Establish rules and governance to ensure that every team uses pre-established capabilities.