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Beyond the Hype - Exploring AI Potential and Pitfalls
By Chuck Monroe, Head Of AI Enterprise Solutions, Wells Fargo
There are several areas in which we’re already seeing data and AI transform the way we do business.
1. Personalized experiences- AI allows us to bring white glove service to the masses , and the level at which we can do this will continue to get more sophisticated. The foundation starts with creating a holistic profile of a customer. By understanding customer habits and behaviors, we can better predict what information they’re looking for, as well as uncover insights for products or services that might make their lives easier. One such example is predictive banking features, which provide insights to customers about spending patterns and suggest actions around money management. It’s allowing them to have easily digestible information in the palm of their hands and make more informed financial decisions “in the moment.”
2. Operations excellence- AI is an important piece of digital transformation that’s taking place across organizations worldwide. Machine-based analytics are starting to take over aspects of data and analytics that previously have been un-automatable, cutting risk of human error as well as costs. This automation is also a key part of growth and sustainability of the current on-demand economy. But that doesn’t mean human workers are going away.
3. Work smarter- AI automation is starting to change some of the skills needed in the workforce.
AI allows us to bring white glove service to the masses and the sophistication of the level at which this is done, will increase
AI insights are also helping team members reduce likelihood of error and enable access to better information, leading to more enriched, focused conversations among colleagues and with customers.
Imagine that someone close to you has passed away and you’ve been named executor of their estate. You have to contact their bank and start the process. It’s one of the hardest calls our bankers receive. Customers are sad, grieving. It’s a terrible use case for a chatbot. Customers want that human connection, a sympathetic ear. It’s also an extremely complicated topic for our bankers, who need to access dozens of documents, policies and systems to process these requests. We are currently testing a Natural Language Processing (NLP) solution that would let AI systems surface all the right questions to ask and content to review, freeing up the bankers to focus on the customer and their needs.
While many are in a rush to deploy AI solutions, there are a few potential pitfalls to avoid in order to have the best chance at success.
1. Failing to organize your data in the right way- Data practitioners often refer to the “three Vs” of big data–volume, variety, and velocity–but overlook a key fourth element: value. Data alone is not enough if you want to optimize customer experiences, reduce risk, and drive process efficiencies. Arranging data in a way that you can glean meaningful information and analytic insights in order to tailor those personalized experiences is important. Additionally, making sure that a customer has opted for these experiences is key for them to see value rather than “big brother.”
2. Looking at AI through a narrow lens- Many organizations get stuck early on by viewing AI through a narrow lens of either data science or technology, both of which are critical partners in expanding AI. In my organization, we’ve developed a cross enterprise team in charge of accelerating the adoption of AI throughout the organization, touching everything from the customer experience to operations and risk management. A dedicated team—or at least an evangelist who can break through silos—is an important element to make meaningful change.
3. Not forecasting enough talent- Yes, AI will replace some tasks that can be automated. But it’s a critical mistake to believe you won’t need customer support available to talk about insights or recommendations AI may have helped uncover. At the end of the day, there are many decisions where digital experiences are matched with human guidance and expertise for an optimal customer experience. In finance, we don’t see a human element disappearing from experiences like buying a house or investing anytime soon. Additionally, you may need to make key hires in areas like data science to support growing AI needs.
The AI landscape will look much different 10 years from now. Focusing on practical, real-world applications, always with a customer-centric perspective, will be a critical differentiator as we get to the next stage.