All or nothing: why banks must fully embrace AI
The future of banking is AI-powered. Most people acknowledge this and are taking steps to make it happen. But the process by which banks should evolve towards becoming data-centric is often misunderstood.
When it comes to data, traditional conceptions of progress can be counter-productive. There are other, better ways of doing things, but they require a leap of faith on behalf of management. Becoming a “bank of the future” means fully embracing AI and machine learning, not just at shallow touchpoints and irregular instances, but across the whole organisation.
Re-envisioning the journey
We tend to view journeys as linear. They have a starting point and an endpoint. A board might have a long term vision for everyone in the bank to be 100% data literate and leverage AI for data-driven decision-making. But how do they make it happen?
We are taught as children (and indeed as adults) that in order to achieve success we must keep our eyes on the prize and work through a series of clearly defined steps to get what we want. But digital transformation doesn’t work like that. Rather than pockets of value being realised at different stages, the process of implementing AI across a business is more like a flywheel.
Different elements create momentum and this powers the wheel ever onward, capturing increasing value as the business grows. Value is released from day one, and undergoes a multiplier effect with more demand, more data, more quality, more value. In other words, value compounds as more data flows into the business and is monetised through progressive data management.
The end game for the bank of the future is AI baked into business capabilities, powered by a platform that enables potential, and a culture that knows how to use it. And while the journey to become an AI-powered bank feels linear and it might appear that way, it isn’t. It’s more nuanced and complex than that. All elements need to work together as a “momentum network” in order to power the bank.
The AI powered bank becomes self-fulfilling, with a culture of data-led decisions driving more demand, more quality, more data, more insight and ultimately, more value.
Sure, there are short-term wins along the way – propensity models, assistant-driven interactions, customer analysis and segmentation, analytics, data ethics, data-driven portfolio management, predictive delivery, and transaction monitoring, to name just a few of the benefits of adopting AI across various banking departments – but you must have the courage to implement different elements simultaneously to generate the momentum needed to become truly AI powered.
Anywhere and everything
All banks are different. Depending on their leadership and data maturity, they all have different starting points on the journey. A CTO in charge of legacy systems might say “it’s time to move to the cloud”, while a bank that has clear data practices already in place might start with building a robust data culture. Ultimately, the opportunity to embrace AI is open to all, but it takes vision and courage to abandon existing structures and processes and reforge the bank into what it can truly be. What’s needed, above all else, is a shift in mindset.
The world of data and AI can seem a little esoteric and impenetrable at times, which can lead to inertia. But the greatest risk in today’s dynamic and competitive banking landscape is standing still. So, in order to get started on the journey to unlocking the power of data and fully implementing AI across the organisation, it’s helpful to keep things simple – you can start anywhere, but you need everything in order to drive the AI-powered bank.