“Relativity” is a great print by Dutch artist M.C. Escher that you’ve probably seen before.1 It depicts a topsy-turvy architectural space where staircases, doors and windows exist on different plains, isolating figures from each other even though they appear to be close together. Everything about the image challenges the eye.

I’m reminded of it when I think about the financial services customer care technology stack: Things that should be in synch with one other aren’t and complexity rules. Artificial intelligence (AI)-powered platforms can dramatically simplify the technology stack. I introduced this topic when I started this blog series, and would like to explore it here in more detail.

Consider first how financial services organizations are addressing the customer care technology stack. Most recognize the challenges of multiple products and vendors, duplicative scripting, and customer insight blind spots. Many are investing in cloud-based contact center solutions like Amazon Connect and Salesforce to shatter the silos, improve speed to market, and make scaling easier and less expensive. As an example, a US bank implemented Amazon Connect in less than five months, covering retail and commercial lines of business, multiple sites and shifts, and hundreds of workers to realize these benefits in its contact centers.

Cloud-based solutions like this serve as a single brain or routing engine for financial services customer care operations. One platform collects customer information from across all channels—email, phone, voice, chat. This alone transforms the service experience for customers and employees alike. Even better, these solutions are building AI into the platform (or can work with the organization’s AI tools) to deliver real-time customer insights to service teams at the point of every customer interaction. This is empowering frontline workers to do their jobs better with deep customer insight. This is not dumping mounds of big data on people. It is applying AI to present the data in a more cohesive and consumable manner.

Think about just how different this is for the industry—and the future customer care workforce. Today, financial services organizations have data scientists running models and pushing after-the-fact insights to operations executives who may or may not share it with frontline customer care personnel. AI democratizes data insight, packaging it and bringing it right to the people who interact directly with customers.

This plays out in so many exciting ways. Imagine data and analytics predicting why a customer has walked into the branch, picked up the phone, or started an online chat so customers don’t have to tell and retell their stories to get the help they need. Imagine too customer care representatives using a truly personalized screen to monitor multiple customer touchpoints and intervene proactively.

Such cross-channel, cross-product customer views powered by AI create new opportunities for financial services organizations to cross-sell based on a truly holistic view of the customer. The caution? With data fueling AI, and AI fueling next-level customer care, data veracity will be even more of a strategic—even existential—concern for leaders across the industry moving forward. Please continue to follow my blog for more on AI in financial services customer care.

References:

  1. Visit http://www.mcescher.com/gallery/back-in-holland/relativity/ to view the print.

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