AI & Sustainable Business: Inevitable Tech, Intentional Choices ft. Lauren Scott

[Host: Lauren Scott] Welcome back to another solo episode of The Resilience Report. This is the first solo of 2026, and to be honest, this is a topic I've been wanting to talk about for months and have not gotten to. I think it’s partly because it's such a complex topic, and I've really been trying to weigh how I want to talk about it. And yet at the same time, I think it's necessary because the two topics really keep on coming up together and how they're contrasting and in conflict sometimes. And so I wanted to open the conversation, but certainly this is probably just the beginning. And that is the idea of AI in its relationship to sustainable business. 

I want to set expectations from the top that this is not about hype, and it's not about doom and gloom. The way I view it right now, especially as somebody who works in tech, is that AI is here. It's not something that's going to happen to us. It is something that is happening right now and it's inevitable. However, outcomes truly are optional. So, what I wanted to look at today and really kind of talk out loud, maybe with you, our listeners is how can we think about AI intentionally when it comes to being more responsible business leaders and entrepreneurs? As we look to bring in AI like those around us but do so in a responsible way. The way I'm viewing it is kind of like how sustainability must be integrated into everything that we do, and at the very least, a consideration of every action that we take. AI is really going to be kind of this ever-present concept that we will have going forward. And so, for today's conversation, I'm really going to break it down into four key areas where I see sustainable business and AI overlapping and looking at the benefits of AI in that particular application when it comes to sustainable business challenges. And then kind of some thoughts as to how I'm balancing it all out. And the angle, again, as somebody who works in the tech space that I'll be bringing is that technology does not create values in a business; it simply scales them. So how do we make sure that businesses have the right values in place before we start scaling with AI? 

So, the first area I think a lot of us think about when it comes to sustainability and AI is that of efficiency. as promised, let's look at both pros and the cons, the pros. This is certainly the area where you hear all of the tech companies talking about it. There is an incredible opportunity to find efficiencies that maybe just weren't possible using human skill sets alone. This can be, again, coming back to an area I work (the built space) we can find incredible energy savings just in terms of optimization. Take the most basic, basic example of having to wait on a human to turn on or off a light. You go beyond just automation, but really kind of taking into consideration some learnings as to when the space is used to consider it in terms of the grid capacity at that time. So, there are true energy efficiencies that we can achieve by applying the two. We can also look at waste beyond energy but really inventory. So, if you're planning as a manufacturer for example, what sort of inventory you need for your goods, it allows you to better plan based on historical knowledge modeling, looking at overall macroeconomic trends in a way that even some of the best planners haven't been able to do so. And if we continue down that path of resources, certainly it can help to lower costs, again, because of some of these more time consuming tasks, we're able to just kind of input them into AI and be able to see those efficiencies. So really kind of looking at how we can have more resource efficiency. Certainly, a pro and definitely the one we're hearing everywhere in terms of companies promoting their individual use of AI. 

If we go, however, into the contrast and maybe some of these deeper considerations, on the flip side of the positives of efficiencies is certainly this thought of the rebound effect. It's Where being more efficient leads you to consuming more. So, where you might see this is if an organization, you're managed to drive down your energy usage instead of necessarily just using that as a saving, you actually think, okay, well this is how much more I can do at that same level. So instead of trying to reduce, you're actually keeping your benchmark and trying to do more with the same. A similar concept is if we go to making production cycles more efficient. So, you're producing more and more and more. Well, this is great for us as business owners and manufacturers. We need to make sure that in making more, we also have those environmental guardrails in place. So it's not just kind of mindlessly doing more, but really thinking about how can you do so in a way that is still tied to your values in those outcomes? 

We also want to make sure that if we are becoming more efficient with production of our products, whatever those are, that we are not, then just pumping out more and more into the economy in the sense that we're not trying to consciously shrink the shelf life of our products, especially if we're thinking about technology, making sure and you're starting to see some regulations, especially in Europe, address this, but making sure that you are not creating products to be obsolete intentionally. so we just need to make sure that as we are getting faster at producing products, that we're not just intentionally trying to make more and make that buying cycle that much shorter. But we need to keep in mind as business leaders, is that efficiency without some sort of guardrails Or constraints honestly just leads to a greater footprint. So while it may be sounds counterintuitive, we need to be really conscious with what kind of limitations we do want to put in place beforehand. And instead of just necessarily going and growing more and more faster and faster, producing more and more, and throwing it out into the economy without really thinking about what is enough and what are the kinds of considerations that we have to kind of protect that overconsumption space. So one thing that we can think about as we wrap up this first topic is. How can we use AI to do less harm than simply using AI to do more? 

The second key theme is really looking at data transparency. So if you're somebody who is working in sustainability space, you know that we are drowning in data. This is probably the same across so many different industries, but essentially the positive part of AI is that it could potentially help us take all of these different data points. I'm especially thinking of those folks who are working in the emissions, the scope one, two and three reporting space, having the ability to more easily centralize and just understand the data that we're looking at. And then certainly helping us in preparing our reports is going to go such a long way. It will probably help us better understand our supply chains. So we're thinking about those scope three emissions. I know this is the area that so many companies are grappling with, and it often represents a bulk of, of our emissions. It will hopefully allow you to better understand where your suppliers are at, and then potentially even use technology to build dashboards to have some predictive modeling in there and to really see and maybe even compare and contrast what your different options are. We can even think about this from a supply chain standpoint, to think about what are maybe some proactive moves that you can do in advance to help protect your work and to ensure that you're continuing to help your customers. 

We're also seeing it as an opportunity, perhaps, to polish up some of our messaging when we're so busy trying to get all of that sustainability data, then we maybe need some support as a very lean sustainability team to put together some of that messaging so we can lean into different AI tools to help us to produce maybe some of those more formal language documents or storytelling that we want to accompany the data, which kind of brings us to the risks, I would say, of using AI. I've talked about AI in the communication space quite a bit, that while it can be incredible and maybe kind of democratizing how we talk about it. There is a really high risk of homogenizing. So we need to make sure that we are not just all sounding the exact same when we're talking about really important topics like sustainable business. There's also perhaps a risk. We all always know that there are some error, issues or considerations when we're pulling in large pools of data. So making sure that we are not either intentionally or not greenwashing with some of our messaging. And then because of this potential risk of error, as well as just kind of homogenizing, we need to realize as a team, as a group that there is potentially an even higher risk of regulatory challenges or maybe even public backlash, especially at a time like right now in 2026 when ESG is a bit sensitive. So we just need to make sure that whatever we are putting out there truly can hold up. So from a business lens, it's just important for us to realize that AI can really help us sound sustainable. We just really need to make sure that behind those messages, we truly are being more sustainable. The best way we can do so is to make sure that whatever kind of metrics you are sharing, you are able to back third-party verification is always a great way to do so. You can also make sure that any of your methodology is documented. We've talked about this before as a way to kind of avoid green hashing, is just to make sure that you are clearly indicating all of the calculations that you're doing, and kind of the logic that goes into your sustainability claims. 

So the third area is probably the one that made me the most hesitant to talk about AI and sustainability is so many tech companies are talking about how AI is helping their solutions help their customers be more sustainable. So what I mean by this is you'll sometimes hear companies talking about how their products, thanks to AI or ML, are able to do things faster or they're able to better help with outcomes. Could come back to that first topic of energy efficiency. But could also go as deep as true areas of sustainability that we know are maybe a little bit more complex, like circular design. Or certainly smarter structures. So that could be smart grid. better transportation, certainly water consumption. We know this is a key way that AI can possibly help us going forward. But then again, this is where I think I had the longest time to work through, is that I also know a lot of people Working in the energy space or in the data center space. We know that AI, as it stands in 2026, is incredibly resource hungry. The high energy demand of data centers, model training, cloud usage. It's like nothing we've ever seen before. It's also putting an incredible amount of pressure on water usage, on land usage, certainly on energy. We're seeing certain less clean forms of energy making a resurgence once again, just to be able to meet the needs of data centers in certain regions across North America and beyond. We're also seeing a tendency of some of these new needs being outsourced to regions that have less stringent regulations, which is truly penalizing and bringing up the topic of ecojustice in certain areas, simply because they do not necessarily have the regulations protecting their populations and their ecosystems in place. so AI doesn't just consume electricity. It chooses where impact lands. And as business leaders, we really need to keep this in mind when we think about how we're pulling in AI into our own practices. Think about the downstream consequences of this more broadly. So don't be afraid to have those conversations with your suppliers. Ask them where they are really focusing their AI efforts, how they're thinking about it more sustainably so you can have those more educated conversations. Because it's so important for us as leaders that we're not just thinking about. Reducing emissions, but also thinking about reducing impact across maybe communities that we don't see in our day to day operations of our business. So I really don't think that this third theme is easy or one and done. I think everyone is trying to figure out how to make data centers operate more efficiently and just AI overall. So it's not that we are going to kind of turn a blind eye to just really trying to address the situation. 

We always know when we're talking about sustainability, we have to think about not just the environmental side, but certainly the social side: our fourth theme. And we've heard a lot of conversations when it pertains to AI about the human impact. And so I think it's important that we consider that when we're talking about how can we weave in AI into our sustainable business practices. I love the fact that in speaking to a number of our previous guests who are entrepreneurs and have their own smaller businesses, that they've actually been the first to say, hey, AI is actually helping me do my business that much faster. I can focus on the things that have the greatest impact on myself or my work, and on my community by delegating some of these tasks to AI. So I think that's a really important point that maybe hasn't been talked about enough, is the positive benefit for these smaller organizations and kind of democratizing how they can do their business on the day to day. Certainly, there's also an opportunity for us, even in larger companies, to focus on the work that really moves the needle for our own careers by delegating, if you will, some of the more administrative tasks to two tools that leverage generative AI. I've heard a lot of people say it's kind of like a really eager, but maybe not the best intern, at least as it stands in 2026. So it still requires that oversight, but it allows you to get a great draft version that you then can edit as a leader. much like you would with a junior staff member. So while it's not apples and apples, it does allow you to scale when maybe you just don't have the finances to do so. it really does help level the playing field a little bit further. Also, I know we have a number of listeners who are students, and I think there's going to be some great career opportunities that combine AI and sustainability in terms of roles that look at the ethics of AI. So there might be regulatory roles coming out. Seeing how we're going to navigate this within countries, but then across global markets is certainly going to be a fascinating opportunity for many different career developments. So those are the pros.

On the darker side of AI, we've all heard about it in the media. It is thinking about the rate at which maybe some companies are using AI as an excuse at this point. maybe that's a strong statement. But to lean out their teams, maybe not necessarily in massive layoffs, but we are seeing a number of companies who are allowing for quote unquote, natural attrition and are not backfilling the roles of people who leave, and trying to leverage tools like AI to sort of justify that. I think it's still really early days to see the true impact, and certainly this will only continue to evolve, but it is something that I think is just being covered deeply in the media at the moment. So one area that I think that we need to focus on as sustainable leaders is to think not just about bringing in the AI tools into our company, but also thinking about how can we help with the transition. You often hear about a just transition when it comes to transitioning for energy. So you have all of this talented workforce, for example, in Canada who are working in oil and gas, and then you're asking them to go over to renewable energy. Well, it's not just about giving them the new tools. It is really about transitioning them over through the education, through the training programs to making sure that we're taking this top talent and then helping them come over to these newer opportunities. So again, just keep this in mind as a business leader as to how you can help that transition period. So we just really need to think about from the social side of AI that minimizing negative impact or maximizing positive impact is not just about carbon. There is obviously the human side of all of this. So just saying kind of closing out the social side for AI, it's to think about AI not just as a tool, but to learn to look at the transition as being critical to your operations in risk mitigation itself. And if you're thinking about it for yourself as a professional, view it as a way to kind of reduce risk in your own professional career. So again, you're kind of having to toss it up as to it's not going anywhere. So let's just make sure that we're doing it as responsibly as possible. 

So net net, where do I stand in terms of my thoughts with AI and sustainability and sustainable business specifically? After months of kind of toying with this idea, I still don't necessarily think I have the answer, but I do see both sides of it. And I think that that's where especially at a time where it feels like we are talking at each other and not with each other. It is critical that we really try to understand both sides of every single situation. So if we go back to the four topics today, we talked about efficiencies. We talked about. Data transparency and just kind of data consumption. We've talked about transitioning to more sustainable Solutions. And we've also talked about that human side. Again, every single area requires that conversation and ongoing conversation, especially if you're leading a team, but also if you're just newer to the workforce and you're trying to consider a career in the space, make sure that you're balancing those different areas. The end of the day, I really don't think AI is necessarily the hero that you hear about in all of these different conferences that you go to, especially if you're in the tech space. But I also don't think it's the villain that you might read across social media headlines or admittedly, if you're in the environmental space as well. So it's neither the villain nor the hero. It's probably somewhere in between, like almost everything else in life. 

So, if you are like me, and you're a leader and you're trying to grapple with the two topics you really care about sustainable business. And you also realize that AI is here to stay. It's inevitable. What can you do? Here are three quick tips with everything we just talked about in mind that I'm trying to take into my own leadership in my day to day. 

• The first is always think about intent before implementation. Why are you using AI? Is it just to go faster or is it to truly increase your impact? So make sure that you are tying it back to your values in terms of your implementation. 
• The second is that constraints or guardrails truly are necessary when it comes to being sustainable. This can be applied to your personal life in a very simple way of you can be more efficient in your own life, but without boundaries, you're not necessarily going to be sustainable. So think about it in that same way. But for business, limits aren't anti-growth, they're just really allowing you to be part of the long game. 
• And then finally, remember that human judgment must remain part of decision making within your business. AI informs decisions. It does not replace accountability. We all need to keep this in mind as we go forward. Again, don't be ashamed for leveraging these tools, especially if it's helping you maybe step up in an area where you feel like you have been kind of left behind. However, do so in a way that you feel like it is in line with your values. That represents what you're trying to go as an organization, and that you can really get behind and stand behind and truly be accountable for as a leader. 

So this is where I stand in early 2026. We have the same conversation, which I think we need to continue to do because the space is evolving so quickly. But if we have this conversation a year from now, perhaps it will have changed enormously. But I think for right now, it would be completely unrealistic to say that the future of sustainable business does not include AI. However, for it to be truly sustainable. We need to use it with intention, humility and long term thinking. and it's really only in doing so that we will truly be resilient, both as businesses and leaders going forward.  

Thank you for being part of this conversation with me and for bearing with me as maybe I talk this out a little bit more because this is not an easy topic. So I appreciate our listeners for you always being here as we figure this out together.