Catching Up Fast by Driving Value From AI

“Back in 2019, Porter and Thomas decided that the primary focus of the bank’s analytics and AI activity should be customers — hence the “customer insights, data, and analytics” label. Thomas, Lee, and their colleagues felt that improving processes and making better decisions within the bank was the best way to catch up to and surpass competitors.

Thomas felt that, given the bank’s relatively late start, a results-oriented approach to AI was necessary. That’s why there are no “big bang” projects, and there is little pure experimentation or research. Instead, the bank’s key use cases focus on continuous improvement of its operations and customer relationships. Lee told us that as a result, most AI projects are deployed into production, with about 80% of Scotiabank’s analytics and AI models already in place and the other 20% pending.”

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