Everyday AI Podcast – An AI and ChatGPT Podcast - Aligning AI With Climate And Business Goals

Episode Date: December 5, 2025

How can you scale AI at the enterprise, yet still hit your climate goals? And can heavy AI usage and an enterprise's ESG mission co-exist? Ashutosh Ahuja lays it out for us. Aligning AI With Climate And Business Goals -- An Everyday AI Chat with Jordan Wilson and Ashutosh AhujaNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion:Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:AI's Environmental Impact and Climate ConcernsCompanies Aligning AI with ESG GoalsAI Adoption Versus Carbon Footprint TradeoffsMetrics for Measuring AI's Environmental ImpactBusiness Efficiency Gains from AI AdoptionReal-World Examples: AI Offsetting Carbon FootprintIndustry Opportunities for Sustainable AI IntegrationFuture Trends: Efficient AI Models and Edge ComputingTimestamps:00:00 Everyday AI Podcast & Newsletter05:52 Balancing Progress and Legacy07:03 "Should Companies Limit AI Usage?"12:02 "Sentiment Analysis for Business Growth"17:07 "Energy Efficiency Impacts ESG Metrics"19:40 Robots, Energy, and AI Opportunity21:41 AI Efficiency and Climate Balance25:04 "Trust Instincts in Investments"Keywords:AI and climate, climate goals, aligning AI with ESG, environmental impact of AI, carbon footprint, energy use in AI data centers, water cooling for GPUs, sustainable business practices, enterprise AI strategy, ESG compliance, climate pledges, AI adoption in business, carbon footprint metrics, machine learning for sustainability, predictive analytics, ethical AI, green AI solutions, renewable energy sector, AI in waste management, camera vision for waste sorting, delivery robots, edge AI, small business AI implementation, AI efficiency, sentiment analysis, customer patterns, predictive maintenance, IoT data, auto scaling, cloud computing, resource optimization, SEC filings, brand sentiment tracking, LLM energy consumption, environmental considerations for AI, future of AI in climate action, business efficiency, human in the loop, philanthropic business practices, sustainable architecture, large language models and climateSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. One thing that we don't talk maybe enough on this show about is AI's impact on the climate.

I think this was maybe a hotter button issue earlier on when there was all these kind of viral studies that compared, you know, the number of questions you ask chat GPT with a certain amount of water. And then you had businesses maybe saying, hey, AI isn't for us. It's bad for the environment. It's bad for your company's climate goals. But here we are many years later since the chat GPT moment, now more than three years. And I think it's time to have maybe a more in-depth conversation about how companies can align their AI goals with their climate goals as well. So whether you work in a field related to the environment or maybe you're in charge of ESG at your company. I think today's show is going to be for you.

And I'm excited to talk about it here on Everyday AI. What's going on, y'all? My name's Jordan Wilson and welcome to Everyday AI. This is an unedited, unscripted, daily, live stream podcasts and free daily news that are helping everyday business leaders like you and me, not just keep up what's happening in the world of AI because it's nonstop, but how we can make sense of it and grow our companies and our careers. So if that's what you're trying to do, it starts here.

But to take it in the next level, make sure to go to our website at your Everyday AI. dot com go sign out for the free daily newsletter we're going to be recapping the highlights from today's interview as well as keeping you up to date with all of the other AI news that you need to be the smartest person in AI at your company or in your department so without further ado I'm excited for today's guest and for this conversation and I know it's going to be a very helpful one so live stream audience if you could please help me welcome to the show our guest for today Ashu Hajja, who is the Enterprise Architecture lead at Starbucks. Ashu, thank you so much for joining the Everyday AI Show.

Absolutely, John. Thanks for having me. It's great to be here. And this already reminds me of a good conversation I've had in the past, so I'm thrilled. All right. Yeah, this is going to be a good one for sure. But, Xu, before we get started, just let our audience know a little bit about your background and all the way of what your role at Starbucks entails.

Absolutely. I'm an enterprise architect lead at Starbucks. I focus on sustainable coffee house solutions. My word looks at the full store built lifecycle, essentially from everything, the technologies we use, choose to build and design a storage for sustainability and the ESG compliance across our operations. That's a gesture threat.

No, that's great. And maybe for some of our audience that isn't super in tune with the ESG, right? environmental social governance, right? I remember like 10 years ago having to look that up, right, coming from a smaller company, I'm like, what the heck? But maybe can you talk a little bit about kind of why, you know, AI has become over the last couple of years, maybe more highlighted than other recent technologies, especially when it comes to environmental concerns? For sure. I think the biggest highlight of the AI has been.

in how rapidly it has evolved and touched everyday users. Many times back when 10, 15, 20 years ago, when cloud was hot, everyday user could not really relate to it. It was still at a business level, or the end users weren't really feeling the kick of it. With AI, the end users were the first one who adopted to it.

The businesses started coming into it. In a way, this is the other way around conversation. So when end users, they come in and amass, it creates certainly the positive. At the same time, it actually adds more of a carbon footprint. They give more data centers, more GPUs and everything. So they are very related. And I think the reason why,

this got a bigger hype out of everything else in the last 100 years or so, in my opinion, based on, I'm not that old because I've been reading about things, primarily around how quickly users adapted to it, and then all the negatives came along with it, right? What impact it has in the environment. Yeah, and I think that potential negative side or the potential downside or the environmental impact has really been thrust in the spotlight a little more recently, right? When you see these, the big tech companies making these $100 billion investments in these data centers, right? And now all of a sudden you have everyday people like me reading about, you know, water cooling for GPUs, right?

And the amount of energy that's needed to run these AI data centers should, right? And maybe this is more for our enterprise audience that, you know, have companies. that have to make climate pledges and things like that. But how should companies be looking at the trade-off, right, between investing in AI and maybe being more efficient with the environmental toll said AI may ultimately have? Yeah, I think we certainly have to be cognizant of how much footprint we leave for our kids and generations to come.

And I think at the same time, I certainly feel in a long run, it is going to offset whatever carbon footprint we're doing, or maybe the good for the humanity. I was reading something on LinkedIn, maybe a couple of weeks ago. It could detect breast cancer five years before it even happens. So imagine evolving to, I know we're not there yet. Imagine evolving to a level where you could detect something that,

bad disease, which helps you save lives. And I think if you start to compare it, and there are many examples comes like this, then I think it speaks on its own. So carbon footprint, though it is very, very, very important, but the amount of value it adds, or it could add in a long run, suddenly offset everything else. Yeah. And that's a great point. And, you know, I think some of this conversation goes back to that original kind of alluded to in the opening there. I think what it was is there was a 20-23 study that said, you know, for every 20 to 50 questions, you ask chat GPT, it was 500 milliliters of water. And I think that maybe that study very early on caused a lot of maybe smaller companies to say, hey, maybe AI isn't for us. right? Look at this impact. Do you think now that companies should still be, you know, looking at their

AI usage, maybe on the smaller and medium-sized side, to studies like that and saying, hey, we shouldn't use AI at our company. Is that something company should actually be asking themselves? They can ask, but I think they should certainly look to adopt AI. It has very, very more advantages than not using it. I'll give you something super simple example, nest thermostat. Yes, it's owned by Google now, but back when it was not acquired by Google, it was a smallish company. Yes, it was a big value. It was a smallish company. It started very, very small. It uses machine learning to detect your patterns, how much temperature you like it, at what time of the day. And it actually adjusts that. And it also has an impact on your carbon footprint.

Low energy bills, less the temperature, less electricity usage, and thus less carbon footprint. But from a smaller, but from a smaller enterprises or smaller companies, they certainly should. At the same time, I think we need to be cautious. It's a very tempting thing to do. I remember back when cloud was hot and everybody hopped on and they were stories and posts on LinkedIn and everywhere that there was a bill from somebody about $200,000, $400,000 because compute was running. So I think we need to be cognizant. Yes, everybody should try. It has so much advantages from productivity saving to bringing more people

in, bringing more businesses in and improving your overall efficiency the way you do it, the way you operate, but got to be cautious. It is certainly very helpful in day-to-day life. Yeah, and maybe what metrics, that companies, if they still want to, you know, have climate goals, if they still want to, you know, have that philanthropic side of being, you know, putting the environment first, but they still do want to use AI, right?

Are there certain metrics that they should be looking at in terms of, again, this isn't my space, right? The amount of energy, use, you know, measuring carbon footprint, like how can companies still do both? But then say on the back end, like, yes, we are still being, you know, environmentally friendly, even though we are still using large language models. So maybe what are some of those metrics or things for those companies to focus on? Yeah, so carbon footprint is suddenly a big thing. but I think the metrics that are more important are more on identifying the patterns that you have

in terms of in how your business operates looking at for traffic the small business a mom and pop cake shop they open let's say seven days a week and they can detect the parent on when the most customers are coming in do they come in between eight to ten in the morning Or do they come after 11 or things like that? Is there more footfall? That's one parent and one metric. Footfall traffic is that more on weekends or more on Fridays. There are patterns like this that small businesses essentially can look at it.

And then go from there, from whether their investments perspective, whether they are more compliant with the ESG goals or not. You know, when you are looking at the future of AI, right? And it's hard for anyone to predict what might be happening in, you know, five years, let alone five weeks. But in terms of AI's impact with company's climate goals, maybe what are some things that you're most excited about for the future, of AI and its, you know, potential impact on what might be possible that we aren't even

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See it today at firefly.adobie.com. That's a good one. I think it's mainly about how value you're able to drive out of it, especially when you look at your, business goals and how well you're adapting it. And I think it is it is important in terms of bringing more revenue. It is less talked about and especially small businesses, those who rely on word of mouth sort of marketing. The sentiment matters a lot. Five years from now, I think many companies should and will be using a lot of sentiment analysis. if they aren't doing it anyway, to look at how the brand is, brand is progressing.

I remember back when I worked for Subway, before I go into any of that, I just want to frame the discussion, you know, I'm speaking from my own personal experience, not at the professional capacity from Starbucks. Back when I worked for Subway, and Subway was going over a big shift in brand narrative in the marketing perspective. Around five years ago, they did a whole menu revamp. And we were looking at the social media, how this animate is. And Subway, the billion dollar company. And the smaller company, smaller brands, should also look at investments like this.

So five years from now, five, I think it's still too far ahead. Everybody should look at this, using the tools at the disposal to do analysis like this to see where the market is heading, where customer sentiment is heading, because that would drive a lot of business and that will allow you to serve your customers more effectively and tweak your business model and look and hear the customer's feedback, So you kind of already gave, you know, one good example on, you know, kind of the other side of AI, right? Like you can look at AI as, oh, it's, you know, consumes a lot of power and all these data centers.

But yeah, it can detect, you know, maybe certain illnesses or diseases earlier than if you weren't using AI. Do you have any other examples specifically on the environmental side, on how AI can actually? maybe more than offset, right, the carbon footprint in terms of what it can help on the environmental or the climate side. Yeah, I'll give it two, in fact. So about a year ago, I was in Toronto and I was looking at some projects for some colleges who are affiliated with the University of Toronto. Students, they developed a camera vision to detect the waste, the kind of waste. So, So there's a big problem when you throw plastics. I'm glad states are banning now when you throw plastic and it had a lot of impacts to it.

They developed a camera version and a sensor, which actually detects what and along with and verify it along with the weight of a product to see where it should be discarded. Should it be recycled? It should be discarded. So it's, yes, it is using a lot of resources in which gets talked about. And I think we have already started seeing some impacts in a day-to-day life where you have benefits in terms of less sorting off waste facilities. That means less diesel, less petrol, less gas, a lot of many of the things which goes

unnoticed and they are behind the scene. So it helps offset in those ways. So yes, it may get talked about, you know, it uses, you know, 500 metric ton of water to run a data center. I'm just making up the numbers for now. But there are benefits are far more than impact it has. Another one I remember, this was again 10, 12 years ago, 10 years ago, I think. I was working for Cigna as a consultant and we were,

Cigna was doing some sort of an acquisition with Express Scripts. And we needed to do the context and modernization. And there was a lot about AWS compute. From on-prem to AWS, we needed to go there. And we even looked at auto-scaling options. And when we did that, it broader compute down, resources down. I think it didn't save thousands of dollars per week. And again, less likely to get noticed in a day-to-day life, but it has a bigger impact

because less compute means less energy, less energy means less carbon footprint and better ESG metrics. If you're a public company, which is even a big thing, because you have a lot of SEC vials and everything. But even for a smaller company, it helps. The only thing with this with this is it's more like this. When something goes wrong, it goes in the deepest pocket. And when the positives are less likely to come out in front of everyday user, because this

is a behind-the-scene metric. But I can assure you someone who works in this field, it has a big impact, whether it kids in front of it not. Yeah, and I love those two examples that you gave. And even one, I've been seeing a lot personally, right? I'm in Chicago. So, you know, it seems like it's one of those cities where they're trying out these kind of like delivery robots, right?

So delivering food. And, you know, at first I'm like, okay, I, you know, don't really understand this. And it's obviously using a lot of, you know, AI to optimize delivery, computer vision, you know, to make sure that a little, delivery robots don't run into little kids or get hit by cars. And at first, I'm like, okay, I don't really see the point of this. But then when you read about it a little bit more, it's like, okay, this is probably really cutting down on just the pollutants and, you know,

more getting more cars, maybe, you know, off the road and, you know, cleaner energy and all those things. You know, maybe is like what are some of the industries that you might see, maybe adopting AI a little bit more quickly in order to maybe have a greener footprint. You know, delivery is one I've just, you know, kind of seen off-hand. But yeah, are there certain sectors that you really see a lot of potential in that maybe, you know, we're not there yet, but there's a lot of potential. Yeah. So let me touch on this delivering and then I'll speak on a sector too.

I used to think exactly like you, how is this like it's going to cut jobs, people are depending on it. Yes, it is going to do it at some extent. But cities like Manhattan, Chicago, they're already very crowded. And having someone on a scooter or a car delivering food, it adds more congestion at the same time. and using the robots, I think it is fascinating. So let me speak on the industry side now. I'm a board member for one of a company in India. It's in the renewable energy space.

And it's a very hard core run industry in a way that they develop a lot of big plants, which actually uses alternative energy to alternate waste to generate energy, be it your home food waste or something else. They don't use, that industry, don't rely on AI a lot. And I think there's a huge, there's a huge opportunity. Primely around those machines, they cost 200,000, half a million. They're big machines. And right now the maintenance is, especially, in that industry is like we'll just look at it when something breaks imagine uh leveraging machine

learning and uh predictive analytics on it to see when i can do a predictive maintenance versus something of reactive maintenance so that's a big uh that's a big opportunity it it yes it will add a cost to begin with you need iot data to feedback to you and you need the models to run on it to analyze what to do it. But in the long run, that cost is going to offset. And I think in less than three years, depending on what your investments are, obviously. But that industry is very underutilizing AI, in my opinion. Yeah. And speaking of the future and being more efficient, it's something I'm always trying to keep up with and make sense of. But it seems like the AI, you know, the AI High Frontier Labs are getting more and more efficient in terms of their next and newest models.

It seems like models maybe that could have been trillions of parameters, you know, two years ago, are now getting into hundreds of billions. And maybe, you know, more will run locally, right, which then would, you know, reduce, you know, some of that carbon footprint or some of the need to go to the cloud every single time. You know, might there be a point where in a couple of years just the models are, smaller, more efficient, more edge AI that this is less of a conversation? Or do you think that just that the capabilities are going to keep increasing? So it's always going to be, you know, climate and AI is always going to be this kind of a balance. I think models are going to keep increasing.

And the climate and AI, they're always going to be sort of at intersection at some point. But I personally feel this overpowers the, it actually, the value that you get out of it is way, way bigger than in the long run. It looks like this right now, like when you start to have material results that impact mass population. Right now, the results that we have in front of us, they're not mass. introduced, right? Chad, GPT, Gemini, these models, yes, they're mass used, but not in a layman perspective. When I say detecting the breast cancer example, I said, right, but doing something which literally has an impact on everyday user, when those things start to become more normal, that's when these conversations start to subside down.

So I think these will continue to master use, and I think companies would, or even the smaller users would continue to leverage it because it actually brings, it can bring a lot of revenue to you if you do it right. So we've covered a lot in today's conversation, but as we wrap up, you know, what is your one most important takeaway for business leaders that are, you know, wanting to maybe see bigger and better returns on AI yet at the same time, ESG is a top priority and climate goals are still a top priority. What's your one most important takeaway message?

Go slow at the same time. Don't hesitate to try something to him. It has more advantages. Listen to someone who has direct experience in it, not me in general, right? Those who those who you trust around you, at the same time, be cognizant. Don't try to ride the high train.

Look at your business, look at the model you're in, and then look for the right investments. Because at the same, I trust you. I think this is going to be the next next big thing, or it is the next big thing in my opinion, for generations to come. One big thing, in my opinion, that often gets overloaded, human in the loop. Those who are not in a tech, this is a very tech-beat term, human-in-the-loop. It's a, you always have to have a human instinct.

If you think something isn't right, trust it. and trust your judgment to kind of look at it again. So if you keep these sort of goals in mind, I think the results would far outweigh the hesitations of the investments we have today. Great words of advice and Ashu, thank you so much for taking time out of your day to join the Everyday AI show. We really appreciate it. Absolutely. Thank you. Thanks for having me. Take care.

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