Other parts of this series:
Ella Fitzgerald was one of the first, in the ‘30s, to have a hit with the song ‘Ain’t What You Do, It’s the Way That You Do It’. The fact that legions of musicians have and continue to record it tells me there’s a universal truth there that most people recognize. This is certainly the case with generative AI. Using the innovation is cheap and easy. Making the most of it, however, demands a considered approach that’s aligned with your business goals, ensures a strong foundation is in place, and mitigates the much-publicized risks of the technology. In short, the way that you do it matters a great deal.
We’ve learnt a lot about ‘traditional’ AI over the past couple of decades, and about generative AI since it burst onto the scene about 18 months ago. We’ve helped hundreds of clients across all industries and geographies—including many commercial and multi-line banks—identify and pilot use cases and start to scale the technology across the organization. In our recent examination of the most important trends shaping commercial banking in 2024, we make the point that each of these is affected in some way by generative AI.
We’ve also progressed well beyond merely talking about generative AI. We’re currently working with many of our tech ecosystem partners to design, build and pilot prototypes for a range of use cases. In the course of our work with commercial banks, helping them develop and execute their AI strategies, we’ve learnt five important lessons which can make all the difference to your own generative AI journey:
1. Focus on the needs of the business and lead with value
It makes sense to start by harvesting the low-hanging fruit, which includes taking advantage of consumable models and applications to realize quick returns. Knowledge management use cases are a good example. At the same time, you should start to explore how generative AI can help you reinvent your products and services, your customer experiences and your business as a whole. For this you will need models that are customized with your organization’s data. You’ll also need . Prioritization will be critical, so invest time and effort in defining your business cases, setting stage gates, and assessing the desirability, feasibility and viability of each opportunity.
2. Build the right data foundation with a secure AI-enabled digital core
To make the most of generative AI you need a technical infrastructure, architecture, operating model and governance structure that meet its high compute demands. You will also need data that is more accessible, fluid and unstructured than most commercial banks currently have. Keep a close eye on cost and sustainable energy consumption—more traditional AI and other analytical approaches might be better suited to particular use cases and are certainly a lot less expensive. The ability to accurately assess the cost and benefit of each could save you a great deal of money and effort.
3. Reinvent ways of working with a people-first approach
One lesson banks are learning every day is that people are at least as critical to the success of a generative AI program as the technology. Generative AI will, to a greater or lesser degree, transform every role in commercial banking. Everyone will soon either work with one or more generative AI tools or will have the routine parts of their job automated by the technology—or both. The impact will be so extensive that nothing less than the retooling of all work and roles will be required if the full potential of this innovation is to be realized.
4. Build ‘responsible AI’ with the right risk and compliance framework
Given the speed at which generative AI is being adopted, and the very real concerns regarding its fairness, transparency, accuracy, explainability, privacy and safety, it is vital that commercial banks ensure these attributes are built in at the design stage and monitored continuously. A robust ‘responsible AI’ compliance regime should include controls for assessing the potential risk of each use case and a means to embed responsible AI approaches throughout the business. Most companies have a long way to go in this regard: our 2022 global survey of 850 senior executives found that while most recognized the importance of responsible AI and AI regulation, only 6% had a fully robust responsible AI foundation in place and were putting its principles into practice.
5. Balance rapid progress with the right operating model and governance
As the first point above implies, your approach will need to be dynamic. While facilitating rapid experimentation and agility across your different divisions, you should simultaneously adopt a centralized, coordinated strategy that establishes the building blocks, processes and governance structures that are essential to the success of your overall program. Several leading banks we are working with have established a generative AI center of excellence that serves the entire organization. This COE comprises the leaders and specialist personnel tasked with creating the roadmap, governance, core architecture and use-case development pathways to the relevant lines of business to experiment and scale at pace.
We believe commercial banking is at a crossroads. Generative AI has the potential to transform so many different aspects of our industry—from the legacy core to the customer experience, and everything in between—that we cannot afford to treat it as just another technological novelty. Only a holistic, strategic approach will avoid the pitfalls and realize the full promise of this remarkable innovation.
We hope this series has given you food for thought. If you would like to learn more, we recommend downloading our two recent reports: Commercial Banking Top Trends for 2024 and The Age of AI–Banking’s New Reality. Or you could simply get in touch—we would welcome the opportunity to discuss the potential role of generative AI with you or your team.
We would like to also thank Julie Zhu and Gustavo Pintado for their contributions.
