AI in Banking Is Paying Off -- Data Readiness Determines How Far It Goes

Across banks and credit unions, AI is already trimming back-office costs, giving employees time back and reducing fraud and risk. Many institutions also see better customer experiences and faster, sharper decisions.

The next step is harder: trusting AI with more of the work that shapes customer relationships and the balance sheet. That trust depends on the data underneath, starting with how well it’s governed. In American Banker’s report “Data Readiness: The Essential Ingredient for AI Success,” conducted in partnership with CDW and available below, only 11% of financial institutions say their data governance frameworks support AI-driven or automated decision-making.

Governance is one of three pillars AI stands on. The other two are data availability and data infrastructure. Gaps across the three are common, which helps explain why AI initiatives often stop short of production, even though AI use is widespread. Closing those gaps is worth the effort: Institutions with the most mature data readiness see value in more areas, including revenue growth.

Here’s what each pillar looks like when it’s ready to carry your AI beyond the pilot.

Say a credit union’s new fraud model sails through testing. Once it’s live, it declines a member’s debit card purchase, and the member calls to ask why. Someone now has to show which data drove the decision, whether that data met quality standards and who approved the model’s use. An examiner may ask the same questions later. If controls were set up mainly to protect sensitive data, piecing those answers together could take days across data, risk and compliance teams.

Governance designed with AI in mind settles those questions before a model goes live. It spells out:

• Who owns each data source a model depends on and who’s accountable when it changes
• The accuracy, completeness and consistency standards data must meet before AI can use it
• Where a person reviews, approves or overrides what AI recommends
• How risk and compliance are monitored in real time rather than after the fact

If an AI-assisted decision were questioned tomorrow, how quickly could your team trace it back to its source data?

AI needs reliable data it can reach quickly. At many institutions, though, pulling data from across the organization on demand is still a work in progress, and unstructured sources such as documents, call notes and chat transcripts remain largely untapped.

Take commercial lending. Balances, payment history and collateral values sit neatly in core systems. The context is scattered: a borrower’s mention of tight cash flow on a call last quarter, covenant terms in a scanned agreement and an email thread about expansion plans. An AI tool limited to structured data can produce a renewal summary that looks complete and still misses what a relationship manager would want to see first.

Capturing, classifying and governing unstructured content turns that context into something AI can use. How much of what your institution knows about its customers or members sits where AI can’t get to it yet?

Pilots typically run on data an analyst pulled and cleaned by hand. That works for a test, but production calls for current data flowing between core banking systems, digital channels and third-party platforms. When that data moves through nightly batches and manual reformatting, AI ends up working with information that’s hours or even a day old, and legacy systems make the problem harder to solve.

Modernizing the integrations between those systems lets data move in near-real time and puts AI inside the workflows employees rely on. Can the people building AI at your institution get the data they need without filing a ticket and waiting in a queue?

Boards and executive teams want AI working across the institution, and your technology team is often the one deciding where to begin. Full, enterprise-wide data readiness doesn’t have to come first, though. It can grow alongside AI, one priority at a time.

A practical sequence might run like this:

• Check whether the infrastructure, tools and data already in place can support the AI you have planned.
• Choose a use case with a measurable outcome. Efficiency by itself is a thin justification, while fixing a process that frustrates customers and employees alike can move cost, productivity and experience at once.
• Prepare the data it needs, including the governance, quality standards and integrations required to run in production.
• Build with the next use case in mind, so later initiatives inherit the groundwork.

The full American Banker report breaks down findings by asset size and charter type, highlights which data capabilities are most and least established, and connects AI results to data maturity. Read it to see which data investments make the biggest difference and how much further your institution could take AI.

An AI Governance Assessment from CDW shows how well your current frameworks support decisions made or shaped by AI and where to strengthen them. From there, CDW’s financial services team works with yours to prepare structured and unstructured data, raise data quality and modernize integrations, so people spend less time chasing data and more time acting on it. Learn how CDW can help your organization gauge its AI and data readiness and build the foundation to bring AI into everyday operations.