“Data and AI come very tightly coupled, because it’s quite hard often for AI deployment to be successful without the trusted data that you need for it to be successful,” Andrew Foster, chief data officer at M&T Bank in Buffalo, told American Banker. Like some other data chiefs in the industry, Foster’s remit includes defining and executing both an AI strategy and a data strategy for the bank. He chose Microsoft Copilot. Today, 16,000 of the bank’s 22,000 employees use the gen AI model for first drafts of emails and reports, and to summarize call center conversations. “For anything involving capturing and using and interrogating text, it’s a starting point,” Foster said. Generative AI can also interrogate SQL databases, he noted. M&T’s software developers use GitLab to help generate code. In most such use cases, “gen AI gets you 60% of the way, then a human reviews it and takes it the other 40%,” Foster said. The benefit is an “uplift in human efficiency, which is obviously useful,” Foster said. “It makes everyone’s work better, faster, stronger.” Having generative AI summarize calls, for instance, saves about six minutes per call. Employees quickly grow fond of the tools, according to Foster. At one point, M&T ran a pilot with 800 people, then got pushback when it considered shutting down the gen AI model. “People say, ‘it’s transcendent, I can’t go back to the way things were,'” Foster said. But he also noted one challenge of large language models: the problem of having multiple right answers. “If you ask Copilot, help me craft an email or help me craft a press release, you could get three different versions, and each of them is right for its own version of rightness,” he said. “So we’ve put human decision-making, critical thinking, at the center of AI adoption. You’re not deferring your own judgment to the machine through the adoption of Copilot. It’s giving you more tools to be effective, but the human being retains that accountability.” When Foster arrived at M&T in March 2023, after 12 years in a similar job at Deutsche Bank, he started a data academy providing in-person and remote training on data governance. So far, 2,000 people have gone through the training. And he began a data lineage initiative. “This wasn’t in response to gen AI,” Foster said. “I saw it as a core capability: Do we know where our data comes from and how we use it, how do we bring it to a level where we can interrogate it, how all the data goes from point A to point B?” His team created a repository called Edison that contains authoritative documents and data on all bank policies. The bank deployed data lineage software from Solidatus and from Monte Carlo. The Solidatus software speeds up the production of data lineage, Foster said. It also provides a single repository for the bank’s data, which enables interrogation and analysis that before would not have been possible. It’s helping to make M&T’s data AI-ready. Solidatus integrates with databases and applications, and it retrieves metadata and lineage from within them, explained Tina Chace, vice president of product at Solidatus.