Artificial Intelligence (AI) is transforming industries at an unprecedented pace, offering new ways to optimize operations, enhance decision-making, and unlock efficiencies. But beyond its role in productivity, AI holds immense potential to accelerate sustainability goals—a critical priority for organizations navigating the energy transition and climate risk.
In the most recent episode of the Sustainability Leaders podcast, Melissa Fifield, Head of the BMO Climate Institute, sat down with Kristin Milchanowski, BMO’s Chief AI and Data Officer, to explore how AI can become a cornerstone of corporate sustainability strategies. Their conversation covered practical applications, ethical considerations, and the steps required to integrate AI into corporate sustainability frameworks.
Check out the episode:
Below are highlights from their discussion:
Melissa Fifield: What led you to pursue a career in AI, and why does your role specifically encompass both AI and data?
Kristin Milchanowski: My path to AI began at the intersection of math, both quantitative mathematics and even a twist of quantum science. And early in my career, I saw how data wasn't just a byproduct of business, but it was really the raw material for decision advantage and AI became a natural extension of that realization. And so, today, as BMO's chief AI and data officer, my mandate is really spanning both of those areas, because you simply can't have one without the other.
Melissa Fifield: As AI becomes a core part of business strategy, why is it equally important to integrate it into sustainability planning from your perspective?
Kristin Milchanowski: AI isn't just a tool for efficiency, it's a catalyst for accountability. As firms weave AI into their strategic fabric, I see it as really mission-critical to be in sustainability and sustainability planning. And it's because these same algorithms that can optimize growth can also optimize for carbon reduction, circularity and also for social impact.
Melissa Fifield: What are some inspiring examples you're seeing of AI producing more sustainable outcomes in particular?
Kristin Milchanowski: One of my best friends owns one of the largest cotton fields in southern Texas. And in agriculture, computer vision is helping farmers use 40% less water while increasing yields. That is measurable difference. And so, not only are you preserving our precious water supply, but farmers are able to increase their yields and then also sustain their families with the increased yields, also help benefit how much money they take home.
Predictive maintenance is the other area across industrial equipment companies, where we're seeing up to 20% emission reduction by empowering predictive maintenance with AI.
Melissa Fifield: How can AI help organizations identify relevant data sources, manage their data effectively, and report to stakeholders in a timely and accurate way?
Kristin Milchanowski: I see the biggest barrier to progress is insight... You don't have to have a perfect data environment anymore, so you can use AI to help predict what data signals are most material to your organization, and then you can automate the integration of that data into sustainability dashboards, so you can turn raw data into decision intelligence, so that boards, investors and regulators get real-time or near real-time visibility instead of just retrospective reports.
Melissa Fifield: What role do you see AI playing in optimizing supply chain logistics?
Kristin Milchanowski: Supply chains are both a risk and an opportunity zone, so AI can dynamically optimize routes, predict demand surges, and identify suppliers with lower emissions and/or lower emissions intensity. Beyond logistics, though, generative AI can simulate supply chain scenarios to find lowest carbon pathways that still meet service levels, which I think is really important balance.
Melissa Fifield: What are the main challenges of integrating AI into existing sustainability frameworks?
Kristin Milchanowski: The main challenge is not technical, it's more structural or organizational. Many sustainability frameworks were designed before some of this fancy generative AI movement came along and they focused on static metrics and annual disclosures, but AI thrives on feedback loops.
Melissa Fifield: What ethical considerations should business leaders keep in mind?
Kristin Milchanowski: Innovation without empathy is empty. AI must be designed with transparency, traceability, and trust at its core. When applied to sustainability, ethical AI means ensuring that models don't just reduce carbon, but also uphold fairness, privacy and inclusivity.
Melissa Fifield: I know you're following developments as it relates to productivity across the US and Canada. Where are you seeing particular strengths in Canada specifically on this conversation?
Kristin Milchanowski: Canada has world-class research and startup investment, but the challenge now is translating that into large-scale adoption across industry, especially in financial services, capital markets, supply chains, sustainability frameworks. And so, that's exactly why companies like ours exist – to help bridge research to enterprise value.