Michael Conway, Executive Partner, Data, AI and Technology Transformation Service Line Leader at IBM Consulting
The New York Times recently suggested generative AI has reached a tipping point. According to the newspaper, it’s having a “Netscape moment” – the instant where a technology triggers wide-spread, irrevocable change. Back in the ‘90s the Netscape browser unleashed the nascent power of the internet. Today, generative AI applications that can instantly produce natural language or even computer code are creating a similarly epoch-defining moment.
While it has been the consumer-focused applications of generative AI that have driven sensational headlines and captured mainstream attention, the underlying capabilities have caused businesses to sit up and pay attention. Recent IBM research found that 64% of CEOs face significant pressure from investors, creditors, and lenders to accelerate adoption of generative AI.
The banking sector has a reputation for being on the front foot with technology, but many institutions remain underprepared or unsure about how to profit from generative AI. In tandem, commentators are now talking about us reaching ‘peak generative AI’, adding to the confusion facing leaders. This risks undermining the potential benefits the technology has to offer.
Success in the long-term depends on experimentation and iteration. Here are three fundamentals that businesses can focus on now that will place them among the early winners in the generative AI era.
Start with the customer experience
Today, every product is a digital product — and every company is selling a digital experience. The increasing demand for a seamless, personalised experience is driving steep competition, but businesses that can tap into the power of generative AI will leap miles ahead of their peers.
For example, a bank could use generative AI to rapidly analyse their own customer data—as well as data from social sources and partner organisations to determine which customers are most likely to take certain actions, such as opening a new account, investing assets, or applying for a loan. The AI system can then help bankers achieve true one-to-one marketing with a personalised strategy and automated, point-in-time customised offers, translated into the customer’s preferred language.
Financial services businesses can also leverage generative AI to shift digital customer service interactions from the customer needing to ask the right question, to the virtual assistant making the right suggestions intuitively. It could ‘remember’ previous conversations with the customer and know which products and services the customer is using, allowing it to provide smarter, more helpful advice. When combined with more human-like language skills, this deeper level of service will help financial institutions to build better, longer-term relationships with customers.
Supporting and upskilling employees
Looking beyond chat bots, AI can also add more value for customer service professionals. With AI and automation tools taking care of the more repetitive, mundane tasks, teams will have more time to work with customers on more complex needs and situations that call for more of a human touch. The businesses that excel in using AI and automation to augment their workforce are likely to have a sizeable competitive advantage.
To be successful, companies must be prepared to invest appropriately in upskilling colleagues so that they can work with the latest AI tools. Working with partners that can bring the right AI transformation expertise can also help businesses to bridge their skills gaps in the more immediate term. At IBM, we’ve set up the Centre of Excellence for Generative AI within IBM Consulting to help clients move forward quickly with putting this capability to work in their business.
Invest wisely in the right AI platform and expertise
It’s important to underline here that, when it comes to business use cases, we’re not talking about any old generative AI tools. While consumer applications can get away with producing incorrect or even offensive output, financial institutions have no such room for error. Customers of banks need accurate, reliable information, delivered in a professional manner that’s consistent with the bank’s overall brand experience. And that’s before you get into the requirements of financial regulators.
Using an AI platform designed for the needs of enterprises in highly regulated industries is therefore a must in financial services. That means the AI models being used are comprised of data that has been screened for things like bias or harmful content and which can be traced to its source. It means the data and the AI models the institution is using have governance controls baked in, so that the outputs are explainable and transparent.
Financial institutions also need AI models that are tailored for the specific domain areas of their business and that are interoperable across different cloud environments, which is important to regulators like the FCA. As IBM AI is built for businesses, we have built all of these requirements into our watsonx AI and data platform for the enterpise.
Commercial value beyond the hype
Are we in a generative AI hype cycle? Yes. But don’t be fooled. This technology is already starting to transform financial services – and virtually every other industry. Those who can harness it effectively stand to reap immense benefits – from more satisfied customers to lower costs, greater productivity and faster innovation.
Don’t wait for the perfect conditions, they’ll never come. Start now, start small, then scale your generative AI applications across the business. Focus on the use cases where you can gain early commercial value – such as customer experience and automating repetitive tasks – and work with technology designed for the enterprise. In a couple of years, you’ll be very glad you did.
