Following the release of our new report, “The age of AI: Banking’s new reality”, I sat down with my team to discuss how generative AI is reshaping the banking industry.
It sparked an interesting conversation about current adoption journeys, strategic priorities, and the exciting possibilities ahead for banks. I thought I’d share some of the discussion with you.
What stage are most banks at in their adoption journey of generative AI?
Over the past eighteen months, there has been significant evolution in the banking industry’s approach to generative AI. Initially, banks were cautious and sometimes skeptical about this emerging technology. However, the majority have now recognized its real potential and impactful possibilities.
Most banks have moved beyond identifying what use cases to focus on and have conducted preliminary trials and proof-of-concepts, including moves to production. Industry leaders are fully appreciating the transformative impact generative AI can have throughout their organization. They are adopting a comprehensive view, focusing not just on isolated applications, but on the broader value that generative AI can offer. Key considerations being addressed include scaling efficiencies, enhancing technological infrastructure and data capabilities, strategizing around talent priorities, and the ethical deployment of AI.
What should banks focus on when adopting generative AI?
Culture is key. The rapid pace of innovation in generative AI, marked by new market entrants, models and applications, poses challenges for organizations in keeping pace and also thinking about how to continually differentiate. Cultivating a culture of continuous learning and experimentation is essential. Banks must remain agile and adaptable, ready to test new ideas and learn from them. A crucial element here is fostering a cultural mindset of curiosity and a willingness to wisely pivot as needed to drive ongoing value generation.
Banks also need to be mindful about the broader picture and not focusing only on isolated use cases. Organizations should expand their thinking to encompass entire value chains. It’s important to have a clear understanding of the current operational baseline and performance, envision future goals, and strategize on how generative AI can help to bridge that gap.
Finally, it’s important not to focus solely on generative AI, but to consider it as part of a larger ecosystem that includes classical AI, automation, analytics and data. Banks need a comprehensive understanding of the tools and strategies required to mobilize generative AI effectively and achieve the desired impact.
How can banks prioritize their generative AI initiatives?
It’s important for banks to start by being very clear on their business strategy and to ask the right questions. These might include: How are we thinking about reinvention? What is it that we’re trying to achieve as business outcomes? How do we want to differentiate in the market? What results do we want to realize? And what are our near-term and longer-term priorities?
Once leaders set strategic goals, they can explore how generative AI can enhance these outcomes and help to fulfil their vision. This strategic alignment helps prioritize initiatives, allowing banks to experiment, learn and make meaningful investments in areas that align with their overall business strategy. For example, some top banks may focus on driving greater operational excellence and optimizing costs. Generative AI can help accelerate assessment and innovation regarding current processes, looking at more efficient, quicker, and cost-effective solutions. Additionally, banks wanting to boost their revenue could leverage generative AI to gain a deeper understanding of consumer and client profiles, help to refine their pricing strategies, or introduce innovative product launches.
These examples highlight the need to integrate generative AI into a bank’s overall strategic framework. It’s crucial that its implementation goes beyond mere technology adoption, aiming instead to help to boost a bank’s overall value proposition and strengthen its competitive position in the market.
What infrastructure needs must be addressed?
In the highly regulated banking industry, the existing rigor and discipline provide a solid foundation for the integration of responsible AI and secure guardrails. However, it is crucial for banks to enhance their model risk management procedures to accommodate the nuances of generative AI and other emerging technologies. The rapid pace of technological advancement requires that risk and compliance teams, along with the associated governance structures, can adapt quickly. It is important that governance frameworks are adaptable and that the required additional steps are clearly communicated to both business users and the wider organization. This clarity will help prevent friction and drive smoother implementation.
Additionally, preparing to handle the unknown is vital. Banks can cultivate a discipline that allows them to manage ambiguity and rapid changes effectively. This adaptive mindset enables organizations to pivot and innovate proactively, distinguishing themselves in a competitive market.
This adaptability extends to the digital core of banks, including their cloud strategies and data management systems. The ease with which teams can collaborate and devise solutions swiftly is important. Such flexibility not only enhances the ability to respond to emerging challenges but also positions banks as leaders in leveraging new technologies for strategic advantage.
What is exciting you the most about the future of AI in banking?
I love to see our client teams starting to experiment more, getting things into production, and starting to really tap into the true power of this emerging technology.
It can also help to bring a breath of fresh air into organizations, which is exciting. People see things they’ve always wanted to do, or a task they wish they didn’t have to, and are able to tap into new opportunities to leverage their AI partner to drive those outcomes.
I’m also very interested to see what happens as we get used to the human and digital workforce. Generative AI is going to free up intellectual capacity, allowing banks to reallocate those hours to higher-value activities and greater levels of realized creativity. I’m excited to see what will be delivered for customers as a result, as well as internally within organizations as employees start to realize some of their own aspirations.
Opportunities and challenges ahead
The journey of integrating generative AI into banking is full of opportunities and challenges. As we continue to explore and implement this technology, our focus remains on enhancing our services and delivering greater value to our customers and teams.
Stay tuned for more updates as we navigate this exciting landscape; and if you’d like to hear more on my latest thinking, read the report, tune in to episode 61 of our AI Leaders Podcast or get in touch to ask me your own questions.
