In a matter of months, generative AI has caught public imagination across all markets. Asking an AI to write up a report, answer a question, or create an image has become something most people have tried and some companies, including my own, have already added generative AI into their products. So, it is unsurprising that the impact of AI on commercial banking will be discussed at Sibos.

But don’t expect an apocalyptic narrative about AI destroying jobs and side-lining humans. The role of AI in the commercial banking sector will be very different to its effect on retail banking. Financial services organisations are on a rewarding path to becoming autonomous enterprises in which smart automation and intelligence further impacts how people, processes and technology interact.

To understand how AI will be applied in commercial banking, it is important to remember what the prime objective of the sector is – delivering products and services via still primarily relationship-based channels, although applying more automation and intelligence to processes. This means optimising the balance between in person or virtual assistance and automated straight through processing (STP) to improve client service delivery. 

The job for AI is to create and speed up these pathways between the commercial banker and their clients. This means using AI more to augment and assist the banker and to automate and focus on STP where it makes sense. What is key about AI in commercial banking is how it clears away mundane tasks that slow down processes and is expert in handing over to human intelligence much more regularly than is the case in retail banking. These are the core principles of the autonomous enterprise.

The competitive edge comes from how well the AI can route and escalate the client assignment with the right information and recommendations to the right team and the right person to help with that product for that specific client from that specific industry. To do this well the AI must contextualise in rich detail what the intent of the client’s message into the bank is, understand how the engagement can be best structured to arrive at the best possible outcome for the client and the bank. 

In this respect, commercial banks will be more dependent on process automation than the more hyped generative AI. This can be defined as the difference between using the smart muscle power of left-brain AI or the creativity of right-brain generative AI to generate answers, content, and applications. There are areas in which process AI will make greater inroads like straight through processing of payments where intelligent automation can shave down the number of exceptions that have to be passed onto a human. 

Overall, it will be how autonomously the AI knows to hand over to and support the human banker in their interactions with high-net-worth individuals or corporate treasurers. The quality of that support could be enhanced by how well generative AI is knitted into the processes to support the delivery of more natural and fluent responses to client questions. Next level chatbots will recognise when requests are not just informational and trigger the right structured workflows to satisfy regulatory requirements or interact with back-end systems. Regulatory demands for doing know your customer (KYC) searches and generating detailed reports also could be eased by applying powerful large language models within the corporate banking processes. 

Over the next 12 months or more, the adoption of process and generative AI in commercial banking will be tied to how these institutions need to differentiate themselves in a period of high interest rates. The competitive edge is going to be even more about flawless service excellence. Process AI can remove much of the friction around services like payments, while generative AI can do more to support talented relationship managers to excel in their roles as advisors and wealth managers.

So, expect Sibos to be the arena where bankers and technologists come together to hammer out how institutions can truly benefit from their journey to become autonomous enterprises.

Whatever the balance is between process AI, generative AI and human intelligence, banks need to tread carefully. There are lots of ethical principles to address, from empathy to fairness and transparency, when AI is unleashed on sensitive customer data in a closely regulated sector like banking. By addressing these concerns, banks can leverage the power of both left-brain and right-brain AI to provide better products and services to their customers and become more agile and competitive in the market.