By Mukundan Sivaraj · AIM Media House
“You keep the human in the loop, because the human has to be accountable for the final product,” M&T Bank Chief Data Officer Andrew Foster told CDO Magazine in a recent podcast.
The bank’s core stance as it scales generative AI is that productivity gains are welcome, but not at the expense of trust, governance, or control. Foster joined M&T in 2023 and began by defining where the bank stood.
His team used the EDM Council’s Data Management Capability Assessment Model (DCAM) to set the baseline and build a two-year strategy, extending through October 2025.
Governance, quality, and lineage became foundational elements, later reinforced through additional cloud-based tooling and a federated model that reduced reconciliation work and cut analytics ticket time by an average of 50 days. That groundwork has shaped how the bank approached generative AI.
Building Control Conditions Before Scaling AI As generative AI accelerated across the industry, M&T chose a sequencing that ran counter to hype. “With banks and other well-regulated institutions, you must have your compliance and governance ducks in a row,” Foster said.
The bank conducted a six-month proof of concept for Microsoft Copilot before deploying the technology across the organization. By early 2025, M&T had rolled out Microsoft 365 Copilot and Copilot Chat to approximately 17,000 employees. Productivity use cases drove early adoption.
Employees use Copilot to draft internal communications, summarize documents, prepare meeting notes, and assist with coding tasks. Foster describes these efforts as part of a broader attempt to “return time to the employee.” Other regional banks are moving along similar lines.
KeyBank CIO Amy Brady said, “I think in our industry, where trust is so important, you have lots of opportunities to earn trust – you can lose it very quickly.” KeyBank, like M&T, is experimenting primarily in middle- and back-office functions while training large segments of its workforce on generative AI foundations.
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