The Segment of One Is Coming to Banking. Is Your Data Ready?

AI Can Transform the Customer Experience in Financial Services 

Today, banks often use A/B testing to develop customer experiences. They form a hypothesis, build a feature, release it to a group of customers and evaluate the results. AI has the potential to dramatically accelerate that cycle. 

Imagine a customer requesting a new capability within a banking application. An AI agent could review the request, generate the necessary code, create a branch, test and validate the functionality and prepare it for deployment. A human could remain in the loop to approve the release, but the development process itself could potentially shrink from months to hours. Release cycles will continue to accelerate delivery as the pace of AI evolves. 

That kind of acceleration changes how we should think about personalization. The goal isn’t simply to build more features faster. It’s to make it economically and technically feasible to tailor the experience to individual customers rather than designing primarily for broad customer segments. 

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For financial institutions, that could mean delivering the kind of personalized financial insights that customers used to get only from a banker who knew them. AI could potentially help make that experience available to a much larger portion of a bank’s customer base. 

But there’s a significant prerequisite: The institution needs to be able to understand and trust the data behind those experiences. 

Data Governance Is the Foundation for AI-Driven Banking 

This is where many financial institutions still have work to do. 

In research we conducted with American Banker in May, only 8% of financial institutions said they were ready to scale their data capabilities. That aligns with my experience speaking to a room of about 100 financial services executives. I asked how many felt their organization’s data was ready to build the kind of hyperpersonalized customer experiences I’m talking about. One hand went up. 

That gap is important, because AI-driven personalization depends on much more than an AI model. If a customer asks what would happen if they added $50 to their monthly car payment, for example, the system needs accurate, accessible information about that customer’s loan. It needs to understand where the information came from, how it can be used and who is responsible for it.

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For institutions that aren’t there yet, the first step isn’t buying another AI technology. It’s understanding the current state of their data and identifying what needs to change. 

That’s where CDW can help. We might begin with a workshop focused on the institution’s data: What information does it have? Where does it come from? How is it used? What is its lineage? Who owns it? From there, we can work through governance, processes, tooling and the people needed to manage the data effectively. 

That ownership question is particularly important in financial services. I often ask institutions who owns a customer’s phone number. Several people may raise their hands. But if a regulator asks who is accountable for that information, suddenly nobody has an answer. 

AI makes that kind of ambiguity increasingly difficult to tolerate. Institutions need to understand their data well enough for its use to be governed and audited. 

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Once we understand the data and governance requirements, we can move to solutions. That can include determining how the institution should manage and govern its data, where and how it should be stored, and how it can scale. It also means addressing the supporting infrastructure — including backup, business continuity, disaster recovery and security. 

We can bring solution architects and implementation capabilities to those different pieces of the puzzle. Because we’re vendor-agnostic, we can work with an institution to determine what architecture makes sense for its particular situation rather than forcing a predetermined technology answer. 

Most important, I don’t view this as a one-time project. It’s a journey. Financial institutions need an architecture that addresses what they need today while positioning them for where they expect to be several years from now. 

The destination is compelling: customer experiences that are not only highly personalized, but trusted, scalable and well governed. AI may eventually make the “segment of one” possible. The institutions that benefit most will be the ones that start preparing their data, people, processes and architecture now.

This article is part of BizTech's EquITy blog series.