Kristin Milchanowski:
Choose one process where AI can directly reduce energy waste or optimize consumption, whether that's in data centers, manufacturing lines, or commercial real estate. At BMO, we began by embedding AI into our data operations to cut compute costs and reduce carbon intensity simultaneously. The message is simple, measure what matters, automate what's repetitive, and scale what works.
Michael Torrance:
Welcome to Sustainability Leaders. I'm Michael Torrance, chief sustainability Officer at BMO. On this show, we will talk with leading sustainability practitioners from the corporate, investor, academic, and NGO communities to explore how this rapidly evolving field of sustainability is impacting global investment, business practices and our world.
Speaker 3:
The views expressed here are those of the participants and not those of Bank of Montreal, its affiliates or subsidiaries.
Melissa Fifield:
Hello, I'm Melissa Fifield, head of the BMO Climate Institute, and I'm really excited about today's episode. We're hearing about AI every day in so many different applications. Artificial intelligence is a captivating technology that's rapidly evolving and energy intensive. As demand for computing power grows, so do the challenges of securing clean energy and modernizing our electrical grid. That's an important story for sure, but I think there's another one worth telling. AI can be a powerful tool for organizations to accelerate their energy transitions and improve resilience. And today I'm joined by my colleague Kristin Milchanowski, chief AI and data officer at BMO. We'll be exploring how AI can be a part of a corporate sustainability strategy and its potential to support positive and measurable outcomes. Welcome, Kristin. Thanks so much for joining me.
Kristin Milchanowski:
Hi, Melissa. Thank you so much for having me. I'm really excited to talk to you about this.
Melissa Fifield:
Let's start with some background. 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. Data is the bloodstream and AI is the intelligence that interprets it. So together, they really define how an organization learns, adapts, and competes.
Melissa Fifield:
So interesting. I think we're hearing so many companies that are exploring or implementing AI tools to help improve efficiency, boost productivity, and reduce complexity. 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. So, when AI is integrated into sustainability goals, it can transform ESG from a reporting exercise into a performance engine, and that's one that aligns profit with purpose.
Melissa Fifield:
I love that and I love that phrase, catalyst for accountability. I think that's so important and super interesting. You are obviously someone who monitors AI developments and use cases across industries, across geographies, and certainly across North America. What are some inspiring examples you're seeing of AI producing more sustainable outcomes in particular?
Kristin Milchanowski:
I used to spend a lot of time in college on farmland and cattle ranches, so I'm really passionate about following that space. 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. And so, I'm really excited about how computer vision is helping them in a real tangible way. 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. So those are two industry-specific areas. And then, in banking, AI models are quantifying climate risk in lending portfolios, turning sustainability from a qualitative aspiration into a quantitative discipline, which is what I'm really passionate about. These aren't pilots anymore. They're proof points that intelligence and impact can coexist.
Melissa Fifield:
I love that example about cotton in particular. In my background in the apparel industry, we know that cotton is a particularly thirsty crop, and I love that example specifically of the reductions that can help drive, and you're absolutely right, there's so many follow-on benefits from those reductions in resources. I want to pivot a little bit. At the Climate Institute, we do an annual survey of business leaders across the US and Canada, and we have found that a lack of data is a major barrier to a lot of business leaders to their own sustainability planning. How can AI help organizations identify relevant data sources, manage their data effectively, and you'd mentioned this earlier around ESG reporting, but report to stakeholders in a timely and accurate way?
Kristin Milchanowski:
I see the biggest barrier to progress is insight. Most organizations don't lack a commitment around this. Their intent is in the right place, the lack of clarity on what data matters, and so AI can actually help surface hidden relationships across fragmented systems now. 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:
Well, that's so important not being, I mean, I think every business leader struggles with not having perfect data. That's a perennial challenge.
Kristin Milchanowski:
You're absolutely right, and I just wanted to kind of bounce off of that with you for just a second, that the mindset that data has to be in a perfect, pristine situation to be able to leverage it is just not true anymore, because of these advances that I was just mentioning in AI. So, I'm really trying to help people understand that it's not as big of a barrier as it used to be, because the technology has evolved, and we can really go after predicting those data signals now.
Melissa Fifield:
I think it's such an interesting parallel in sustainability in general. I think we have tried for decades to chase perfect data and perfect reporting and precision. I think what we're seeing with the promise of some of these different AI enabled applications is that the predictive element can yield the insights maybe that get you toward progress as opposed to perfection. And I think that's a really, really important development.
Kristin Milchanowski:
And I think all of us listening would take progress and actionable insights all day over being paralyzed over whether or not something's going to be perfect a year from now.
Melissa Fifield:
Absolutely. Across many industries, supply chains are really the source of a substantial part of a company's carbon footprint. What role do you see AI playing in optimizing supply chain logistics, or do you have specific examples of how they are minimizing their environmental impact in their supply chains in particular?
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. Imagine every shipment vendor and process being continuously recalibrated for environmental and economic efficiency. I think that's what intelligent sustainability looks like.
Melissa Fifield:
Absolutely. On a related note, again, I mentioned our business leader survey and over the years that we've found how a growing share of companies across the US and Canada, they are increasingly developing climate plans for their organizations, but what do you see as maybe some of the main challenges of integrating AI into existing sustainability frameworks? We've talked a little bit about precision versus progress and prediction. I'm just curious what you're seeing as some of the current challenges.
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. So, bridging that gap means redesigning governance models, retraining teams, and embedding AI literacy into ESG functions. It's about moving from compliance to continuous optimization.
Melissa Fifield:
I think that's so great. It takes progress, not perfection, to the next level. As business leaders consider AI tools and solutions in the context of these sustainability plans and as you said, embedding that AI literacy into their operations and maybe redesigning governance models, what are the ethical considerations to keep in mind? How do we keep that holistic view front and center as this AI train moves so rapidly?
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. Data must represent all communities impacted by environmental decisions. The North Star on this is really clear. Intelligence without ethics isn't progress. It's risk rebranded.
Melissa Fifield:
Absolutely. Well said. Kristin, 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 a meaningful AI progression to talk about, and it has some distinct strengths. For example, the pan-Canadian AI strategy, we laid a national foundation, top tier institutes in Toronto, Montreal, Edmonton. There's a deep talent pool and one of the highest rates of AI research output in the G7. So, at the same time, we're a pivotal inflection moment. 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. Canada really can lead AI and the conversation of productivity both, because they have a strong ecosystem to lean into. They have a very active governance process and program through government entities that are wanting to help establish proper regulations. You've got world-class research institutions through academia, and you have active engagement from both the public and private sector in Canada, that together this massive ecosystem is poised to really lead the conversation in AI and help support productivity for Canada.
Melissa Fifield:
Based on your experience in AI strategy here at BMO, what in your view is one practical step that business leaders can take today to use AI in accelerating their energy transition?
Kristin Milchanowski:
I recommend starting small, but strategically. Choose one process where AI can directly reduce energy waste or optimize consumption, whether that's in data centers, manufacturing lines, or commercial real estate. At BMO, we began by embedding AI into our data operations to cut compute costs and reduce carbon intensity simultaneously. The message is simple, measure what matters, automate what's repetitive and scale what works. And for those leaders who want to go deeper, moving beyond experimentation and truly institutionalizing intelligence, I can offer the audience that my book is going to be coming out at the end of March, Return on Intelligence. It's a strategic playbook for scalable AI agents. It's not just about technology, but it's about leadership discipline. And so, Melissa, in the book, we lay out how to design AI agents as digital co-workers and to modernize data foundations, and build these measurable returns on intelligence, and it spans across growth, efficiency, and risk. There's a lot of good nuggets in there as a roadmap for turning AI from potential to performance.
Melissa Fifield:
That's exciting. I can't wait to read it. I may have a more personal question for you. In your everyday life, either personally or professionally, what's your favorite or maybe most used AI prompt?
Kristin Milchanowski:
I don't know if I have a favorite most used prompt, but I can share my tip and trick for prompt engineering, which is start by telling your generative model of choice who it is and who you are. So, if I am wanting to ask about the best barbecue recipe, I'm going to tell it that it is the world's leading pit master and that I am a novice barbecue chef with a simple grill. By telling it very specifically who it is and who you are, it answers the question a whole lot more directly the way that you need it to be answered just by that simple little trick.
Melissa Fifield:
That's so fantastic. That's such a good insight. It provides the context necessary to get the answer that you actually want. I'm going to try that today. Before we wrap up, anything else you'd love to share with this audience, which I'm sure comes from a broad spectrum of understanding and appreciation for what AI is and what it has the potential to do, but anything else you'd like to share with our audience today?
Kristin Milchanowski:
I often get this question about is AI going to take my job? And I do like to weave this in that AI is not replacing human intelligence. It's an extension of it. The future is going to belong to organizations that treat AI not as a technology product or a technology project, but as a leadership discipline. The question isn't whether AI can accelerate sustainability. It's really about if we have the courage to lead with intelligence and integrity along the way, and I know we do at BMO, and I know our listeners really do have that courage to lead that way. The energy transition sometimes can be seen as a destination, but really we can see it as a direction, and we can leverage AI to get us there faster and smarter and uphold all our ethical values and responsibility that we need to have.
Melissa Fifield:
I think that's so true. It's really a transformation. We need all the tools in our arsenal. So many good gems in today's conversation, Kristin. Thanks so much for joining me and sharing your insights with our audience.
Kristin Milchanowski:
Appreciate it.
Melissa Fifield:
Thank you.
Michael Torrance:
Thanks for listening to Sustainability Leaders. This podcast is presented by BMO. You can find our show on Apple Podcasts, Spotify, or your favorite podcast player. Press the follow button if you want to get notified when new episodes are published. We value your input, so please leave a rating review and any feedback that you might have, or visit us at bmo.com/sustainabilityleaders. Our show and resources are produced with support from BMO's Marketing team and Puddle Creative. Until next time, thanks for listening and have a great week.
Speaker 5:
For BMO disclosures, please visit bmocm.com/podcast/disclaimer.
