BMO is using AI to strengthen frontline performance, compliance operations and data analysis while exploring additional AI applications at scale.  

The $1.5 trillion bank’s AI-powered strategy is reshaping how it supports clients while “putting AI in the hands of everyone,” Chief Executive Darryl White said during the bank’s Dec. 4, 2025, earnings call.  

Kristin Milchanowski was appointed chief AI and data officer in October 2024 to spearhead the Chicago-based bank’s AI initiatives. The bank is now seeing “clear efficiency improvements across AI‑enabled operations,” as adoption scales amid reinvented workflows, she told FinAI News. 

“Our focus is not on isolated productivity metrics, but on enabling teams to scale capacity, service quality, and consistency more efficiently.”

— Kristin Milchanowski, chief AI officer, BMO

AI, she said, allows BMO employees to: 

• Handle higher volumes of work; 

  

• Respond faster to customers;  
• Reduce manual effort; and  
• Maintain risk management and compliance standards. 

BMO expects the efficiency gains to grow exponentially as it embeds AI into end-to-end workflows rather than layering onto existing processes, she said.  

“We view this as a long‑term operating model shift rather than a short‑term cost‑takeout exercise.”

— Kristin Milchanowski, chief AI officer, BMO

Milchanowski sat down with FinAi News to discuss BMO’s AI initiatives and how AI advancements will reshape financial services overall. What follows is an edited version of that conversation. 

FinAi News: What AI advancements do you foresee having the biggest impact on financial services and why? 

Kristin Milchanowski: The next competing advantage isn’t just AI or data; it’s intelligence velocity. Everyone has data, not everyone can translate it into judgement, foresight and action as fast as we can. Our strategy is about compressing the distance between insight and impact. The faster intelligence moves through an organization, the faster value compounds. That is how BMO will lead the next era of financial innovation.  

Three areas will be particularly important. 

1. There needs to be a shift to focusing on relationship-deepening AI agents and revenue-creation AI agents. These will-be agents that can execute multi‑step workflows under human oversight will fundamentally change how banking work gets done.  
2. Advances in AI governance, explainability and auditability will determine which institutions can scale AI responsibly under regulatory oversight.  
3. Domain‑specialized financial models will continue to improve decision quality across risk, compliance, underwriting and advisory use cases. 

In the next decade, speed of intelligence will define the strength of an institution.  

FinAi: Which BMO operations are currently benefiting most from AI integration? 

KW: We are seeing the strongest impact from AI in frontline enablement, customer service, underwriting and advisory workflows, banker analytics and compliance operations. These are high‑volume, information‑intensive areas where speed, consistency and decision quality matter. 

For example, frontline employees use AI assistants to quickly access internal policies, procedures and product guidance, which reduces search time and improves consistency in client interactions. Digital assistants are helping manage large volumes of customer inquiries, improving responsiveness and customer experience.  

AI is also streamlining advisory workflows by accelerating information synthesis and decision support, while keeping humans accountable for final decisions. Banker analytics platforms provide timely insights that help relationship managers better understand client needs and identify opportunities. On the control side, AI is supporting compliance teams by automating monitoring activities. 

The pattern is simple: AI delivers the most value where work is repetitive, knowledge-intensive and time-sensitive. 

FinAi: What factors come into play when deciding whether to build an AI solution internally or use a third‑party technology provider? 

KM: We assess build‑versus‑buy decisions across several dimensions: strategic differentiation, speed to market, regulatory posture, data sensitivity and internal engineering capacity. 

Speed is often a key factor. Partnering for a mature capability can significantly shorten time to deployment, which matters in a rapidly evolving AI landscape. Risk considerations are also important; well-vetted third‑party solutions may come with established controls and documentation that support regulatory review. We also consider engineering opportunity cost. Internal teams are often best focused on capabilities that are truly differentiating rather than rebuilding commoditized functionality. 

If a use case is tightly linked to proprietary data, core to risk management or decisioning, or central to competitive advantage, we are more likely to build and deeply customize internally. Ultimately, the decision balances strategic value, time, risk, and resources. 

FinAi: How does BMO approach experimentation with AI? 

KM: We have moved from mere experimentation to executing a strategic playbook that allows us to deliver AI at scale. We approach AI initiatives as a structured pipeline, not a collection of disconnected pilots. Each initiative starts with a clearly defined business problem and value hypothesis, along with success metrics that span business impact, risk and operational feasibility. 

We are researching AI at the edge — the technology not yet mainstream and ready for production. We do this in a controlled lab environment, with clear guardrails, representative data and human oversight. Responsible AI, model risk and compliance partners are engaged early, not added at the end.  

We also invest in reusable platforms and patterns to ensure our initiatives can scale efficiently. Our mindset is simple: experiment quickly, govern early and scale what proves both valuable and safe. 

FinAi: What AI initiatives or new technologies are in the pipeline for BMO in 2026? 

KM: Our 2026 focus is on scaling AI across engineering, operations, customer engagement and personalization. On the operational side, we are expanding AI‑enabled capabilities in software development, middle‑ and back‑office processes, and contact centers. 

On the revenue side, we are investing in more personalized customer journeys, including retention, digital conversion and creating digital account coverage in our Capital Markets business. 

The broader shift is from isolated AI use cases to AI embedded across end‑to‑end workflows, with governance and controls designed to scale alongside adoption. 

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