BMO on Scaling AI for Measurable Business Value

September 1, 2026

AI Summary

5 min read

BMO’s chief AI officer Kristen Milchanovsky came to the podcast with a clear distinction: the industry has spent the last few years proving that AI can work. The next phase is proving it can deliver measurable value at scale. “As a scientist, I often say that the pilot’s gonna prove that something can work and the scale is going to prove that it can work repeatedly safely and reliably,” she said. At BMO, that means embedding AI into core banking workflows rather than treating it as a bolt-on tool, with a public target of $1 billion Canadian in combined value from revenue growth, productivity gains, operational efficiencies, and risk management benefits by 2030.

The billion-dollar target and early proof points

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What you'll learn

1 (01:25) **Defining AI Value at BMO** - Kristen explains BMO’s strategy: AI is grounded in business outcomes, not just technology, and treated as enterprise infrastructure.
2 (02:54) **The Billion-Dollar AI Target** - Kristen breaks down BMO’s $1 billion CAD AI value target by 2030, covering revenue, productivity, efficiency, and risk.
3 (06:25) **Measuring Operational Outcomes** - Kristen shares real metrics backing the billion-dollar target, including cycle time reductions and workload reductions.
4 (07:00) **Agentic AI: Assist vs. Act** - Kristen distinguishes between agents that assist and those that act, explaining where BMO deploys agentic capabilities today.
5 (09:29) **Three Key AI Deployment Areas** - Kristen groups BMO’s AI use cases into relationship intelligence, credit/risk decisioning, and frontline enablement.
6 (11:16) **Measuring Credit Analysis Effectiveness** - Kristen explains how BMO measures AI’s impact on underwriting and credit processes.
7 (13:16) **Scaling AI in Commercial Banking** - Kristen discusses the jump from 10% to 70% of commercial processes powered by AI.

+ Full timestamped outline available in the app

Show Notes

“The hardest challenge in enterprise agent deployment is not generating them; it’s the accountability,” Kristin Milchanowski, chief AI and quantum officer at BMO Financial Group, tells Bloomberg Intelligence senior analysts Paul Gulberg and Anurag Rana. In this episode, the group discusses BMO’s AI profit target of $1 billion by 2030, how the bank is embedding AI into core workflows and why agentic systems still require strong human oversight, governance and measurable benchmarks. Milchanowski explains how BMO is designing for model change with a hybrid infrastructure, exploring small language models for focused workflows, and prioritizing measurable business value over token consumption as it expands AI across the enterprise.