Northwest Bancshares CFO Applies Enterprise AI Governance Model to Regional Banking

Regional financial institutions are increasingly drawing on technology playbooks established inside major national banks to guide their enterprise software and artificial intelligence deployments. According to Doug Schosser, chief financial officer of Northwest Bancshares Inc., the core challenge facing smaller lenders is not matching the budget of larger peers, but establishing the operational discipline required to determine which automation projects should scale.

Speaking in an interview on theCUBE, SiliconANGLE Media’s livestreaming studio, at Workiva Inc.’s Amplify event, Schosser outlined how his experience leading a financial overhaul at KeyBank National Association influenced his strategy at Northwest Bancshares. As first reported by SiliconANGLE, Schosser chose to bring enterprise software vendor Workiva over to Northwest Bancshares after observing how connected operational data improved reporting efficiency, despite the stark difference in scale between the two institutions.

During the broadcast hosted by Krista Case, principal analyst and practice lead for cyber resilience and security, alongside co-host Alison Kosik, Schosser revealed that he evaluates all proposed artificial intelligence initiatives against three core criteria: customer impact, internal efficiency, and risk reduction. The framework arrives as software providers expand their automated capabilities, highlighted by Workiva's launch of specialized AI agents and a dedicated intelligence layer for high-stakes reporting in July.

Schosser noted that governance frameworks across the industry have struggled to keep up with the deployment velocity of modern AI tools. He argued that organizations should supervise technology tools with the same rigor applied to junior human personnel, given that automated systems are handling similar data and executing comparable analytical tasks.

A successful automation strategy requires clean data and refined business workflows prior to software installation, Schosser emphasized. He warned that adding advanced tools to flawed operational models risks automating existing friction and inefficiencies across finance, information technology, and underlying lines of business.

By leveraging years of historical document data stored within enterprise reporting platforms, financial institutions can train automated systems to handle repetitive roll-forward tasks. Schosser explained that the ultimate goal of adopting these technologies is to accelerate routine operations so human staff can concentrate on complex analysis, detailed explanations, and high-level decision-making.