The AI Readiness Gap in Banking

AI readiness in banking isn’t about deploying chatbots or chasing the latest model release. It’s about whether your institution can consistently turn data into decisions, at speed, with confidence. The banks making real progress with AI aren’t starting with tools. They’re starting with visibility, governance, and execution. Without that foundation, even the most promising use cases stall out.  

With it, though, AI becomes an accelerator rather than a distraction. 

The first step is starting with visibility. Your executives should be able to answer basic questions without waiting three days for someone to pull a report, and your teams need access to the data required to do their jobs rather than having everything locked behind IT requests. When you look at customer profitability, loan performance, or campaign ROI, you should be confident that the numbers are right. Those aren't AI questions; they're data execution questions. And if the answer is "sort of" or "it depends," you're not ready. 

But readiness isn't just about what you can see today; it's about what's missing from your reporting entirely. A recent Cornerstone Advisors report identifies specific weak points worth addressing: Data quality and integrity monitoring scored low across the board, as did the integration of structured and unstructured data. Data quality matters not just for the historical reporting you're accustomed to today, but even more so for the predictive and prescriptive reporting you need tomorrow. And the integration of structured and unstructured data is critical for future generative AI use cases. Unstructured data includes call center transcripts, customer feedback, and loan officer notes—that's where context lives. LLMs thrive on context, but only if you're capturing it systematically. 

Here's the problem most institutions miss: If every branch, call center, or support agent collects qualitative data differently, your AI is being fed chaos. 

To move from chaos to strategy, you need forethought and proper data governance policies and procedures. The report suggests creating templates or structured forms for capturing customer comments and service interactions, implementing consistent tagging frameworks across customer feedback channels so you can identify patterns around loan experiences, digital frustrations, or product confusion. For open-text fields, prompt employees or customers with structured questions to generate more actionable inputs. These are a few examples, but your teams have tens to hundreds of daily tasks and responsibilities that need to be addressed. 

The good news? You don't have to fix everything at once. Pick the area where better data would have the most immediate impact. For some banks, that's marketing. For others, it's lending or operations. Identify the biggest gaps, build a plan, and make sure people can actually see and act on the information they need. Then do it again for the next function. 

You can download the full report, "Improving Your Financial Institution's Data Execution Quality," HERE. It includes a detailed framework for assessing your own capabilities across your institution. The bottom line: If you're not confident in your data today, you're not ready for AI tomorrow. Start there.