你的银行数据基础为人工智能做好准备了吗

IN PARTNERSHIP WITH Executive Summary Banks can't leapfrog to AI without first fixing their fragmented finance infrastructure. No matter howsophisticated the algorithms or how promising the use cases, AI deployed on siloed, inconsistent dataproduces unreliable results. Financial institutions face margin compression, intensifying regulatory scrutiny, and a talent landscapedemanding more strategic contribution from finance teams. Yet many continue operating onfragmented legacy systems that make basic cross-functional reporting difficult. This whitepaper examines why finance transformation is the prerequisite for AI readiness — andintroduces the “land and expand” methodology that enables banks to build foundational capabilitiesquickly while positioning for continuous innovation. Key Insights 63%of organizations don't know if their data practices are suitable for AI deployment The “land and expand” approach enables implementation in weeks rather than months The ConvergentPressures ReshapingBanking Finance Gone are the days of us lookingbackwards as a finance organization,as historical reporters. The questionfinance leaders must ask now is‘How do we become more strategicdrivers within a business?’. Banking executives face simultaneousoperational challenges that make siloedresponses ineffective. Adam Scheidler Margin Pressure andRegulatory Intensity VP of Financial Services atKainos Net interest income remains constrained,forcing CFOs and CEOs to examine everyoperational cost. But cost-cutting alone isn'tsufficient when regulatory requirementscontinue expanding. Financial institutions mustdemonstrate robust data governance, clearaudit trails, and compliance frameworks thatadapt as rules evolve. substantially. Organizations need systems thatcan scale compliance capabilities withoutproportional increases in headcount. The Talent Shift The mandate to “do more with less” has createdacute talent pressures across banking financefunctions. But the deeper challenge is the roletransformation finance teams must undergo. CFOs and CEOs are being forcedto think about cost control anddata as it relates to how they'repreparing for regulatoryimpacts in the near future. “Gone are the days of us looking backwards asa finance organization, as historical reporters,”Scheidler observes. “The question financeleaders must ask now is ‘How do we becomemore strategic drivers within a business?’” This shift from reporting to strategy requiresautomation of routine tasks to free capacity,and real-time data access that enables forward-looking analysis rather than backward-lookingreconciliation. Adam Scheidler VP of Financial Services atKainos “CFOs and CEOs are being forced to think aboutcost control and data as it relates to how they'repreparing for regulatory impacts in the nearfuture,” notes Adam Scheidler, VP of FinancialServices at Kainos, a Workday implementationpartner serving over 900 banking customers. Positioning forStrategic Transactions Whether planning organic growth, evaluatingacquisition targets, or preparing for potentialdivestitures, banks need financial systemsthat provide clear visibility into performanceat granular levels. Yet many institutions can'tquickly answer fundamental questions about The challenge intensifies for institutionsapproaching key regulatory thresholds— such as the $10 billion asset mark —where examination requirements increase still exist inside those banks. They have asiloed data approach. It's still difficult to movedata between applications. And quite frankly,if you want to be successful in AI, it reallydepends upon having solid data, and moreimportantly, clean data — data that everyonein the organization, from the C-suite down,understands and trusts.” product-line profitability, customer segmenteconomics, or branch-level contribution. This limitation becomes acute during M&Aactivity, where due diligence timelines compressand integration planning requires detailedoperational understanding that fragmentedsystems can't deliver. Deploy AI agents on poor-quality data, andyou risk making bad decisions based on badinterpretations. In a highly regulated industrywhere decisions affect customer outcomesand compliance status, this is a riskmanagement failure. Why Clean DataComes First Banks across the industry are exploringAI applications — from customer serviceautomation to fraud detection to predictiveanalytics. But many institutions are discoveringthat AI deployed on fragmented, inconsistentdata produces fragmented, inconsistent results. You not only have to know yourNorth Star, what the organizationis going to be, but what you expectfrom your transformation. Or else,you'll never know if you got this —like putting in a blank number orblank location for a GPS system. “To take advantage of all the technologies thatare coming out, you really need a solid datafoundation within your organization,” explainsJim Gahagan, who leads financial servicesindustry marketing at Workday after