Cutting Carbon Footprint and Unlocking Sustainability with Data Science with Mukund Raghunath

As the climate landscape rapidly evolves, reducing carbon footprints has become a critical priority for businesses and individuals. The accelerating effects of climate change, highlighted by extreme weather events and record-breaking temperatures in 2023, have transformed sustainability from a corporate social responsibility initiative into a key competitive advantage. This shift opens new opportunities for innovation, particularly within data science and technology. By leveraging digital transformation, companies can not only track and report carbon emissions but also develop strategies that balance immediate needs with long-term resource preservation. The integration of artificial intelligence, data science, digital twins, blockchain, and generative AI provides powerful tools to tackle sustainability challenges, optimize processes, and generate tangible value from eco-friendly practices. As regulatory pressures mount and consumers demand more environmentally conscious solutions, businesses that embrace sustainability as a strategic advantage are well-positioned to lead in a greener future.

This week, we had the privilege of hosting Mukund Raghunath, a seasoned leader with a diverse range of experience. Mukund founded and served as the CEO of Acies Global, a digital transformation company. He has also held key roles as a board member at In-Med Prognostics, and as an angel investor, startup advisor, and board member for CXO Advisor. Prior to these roles, Mukund was a Senior Vice President and Business Unit Head at Mu Sigma Inc., where he played a critical role in scaling the organization. He also brings extensive experience from his time as a consultant at ZS Associates and a project manager at Motorola.

Mukund completed his MBA from The University of Chicago Booth School of Business between 2002 and 2004, and earlier attended the University of Illinois Chicago from 1995 to 1997. His educational journey also includes his time at PS Senior from 1981 to 1990. While Mukund has a history with the University of Madras, specific details about his degree or field of study are not available.

Mukund sat down with Kashyap Raibagi, Associate Director of Growth at AIM Research, to explore strategies for reducing carbon footprints and shaping a more sustainable future. In this episode, they delve into the role of data science in decoding sustainability and driving impactful environmental change.

Key Highlights

Sustainability as Competitive Advantage: Mukund emphasized that sustainability should not just be seen as a checkbox or compliance task. Companies should start competing on sustainability, using it as a competitive tool similar to pricing, quality, and branding.

Data Science for Measuring Carbon Footprint: Mukund shared examples of using data science to track emissions, such as identifying hidden carbon impacts (e.g., sulfur hexafluoride in manufacturing) and helping companies measure Scope 1, 2, and 3 emissions.

Challenges with Data Collection: He highlighted the difficulty of gathering accurate and reliable data for Scope 3 emissions, which cover external factors like supply chains and transportation, and the need for better data exchanges to facilitate this process.

Emerging Technologies: Mukund discussed how technologies like digital twins, generative AI, and blockchain can play a crucial role in improving sustainability efforts by simulating processes, connecting data, and ensuring data integrity.

Shifting Mindsets for Sustainability: He advocated for a shift in mindset within companies—viewing sustainability as an opportunity for growth and innovation, not just a regulatory obligation. This mindset is key for companies to thrive in a future where sustainability is central to business success.

Kashyap: Welcome everyone to the next episode of the AIM Media House Podcast, Simulated reality. Today we have with us Mukund R. He is the CEO and founder at Acies. How are you doing Mukund?

Mukund: I’m good. How are you doing?

Kashyap: I’m doing really well. Thank

you so much for asking. And I reached out to your team. They wanted you to talk about reducing carbon footprint. I was a little taken aback. I was like, are we doing an AI podcast or not, want to really understand a little bit what was the thought process behind speaking about sustainability, especially

Mukund: Acies is a digital transformation solutions company. What that means for me is bringing together technology, data, and data science to address any problems in the business or the environment. This includes sustainability. Sustainability, for all the wrong reasons, is a hot topic now. Given the speed of adverse climate changes, even in the last couple of weeks, I think sustainability is all about striking a balance between today’s needs and preserving natural resources for future generations. Data and technology can play a huge role in achieving this.

Our passion for sustainability stems from the fact that we can make a material difference. There is a lot of data present, but companies are not even scratching the surface in terms of what can be done in this space. If you look at sustainability today, a lot of the initiatives around sustainability are coming from the lens of corporate social responsibility or compliance, checking the box, and making sure that regulatory reporting is done.

I think in order to truly make a difference, companies have to start thinking about how they can compete on sustainability. Just like companies compete with each other on price, quality of their products, branding, and so on, I think sustainability is an important tool for a company to take forward. That is not happening today. If that happens, I think progress towards a greener planet will be much faster, and I think we can enable that using data, AI, and technology. That’s where our vision for sustainability lies.

Kashyap: Can you share some examples of use cases you have worked on using data science for sustainability?

Mukund: We work for a lab manufacturing company, focused on biotechnology and pharma. They manufacture the labs and put them together. And for them, one, they wanted to understand what their footprints are, and they wanted to figure out. So if you know a little bit about sustainability, there’s no scope one, scope two, and scope three emissions that companies measure and report on. Scope one and scope two are mandatory. Scope three is still not mandatory. And scope one refers to, think that the company, the way you think. Can think about it. Think of the company that the company burns within their, you know, the direct emissions from whatever they burn in the process of running their business. So the second is scope two. Indirect emissions from everything that you buy. You buy electricity, you buy gas, you buy heat, all of that falls under scope two. Scope three, everything that’s outside of these two, and that’s vast and has about 15 different categories supposed to report on scope three emissions, and that’s a lot of data that is not easily available, so companies are still not reporting a whole lot of them. So, what we helped this lab manufacturer do is figure out their scope one and scope two, and to a certain degree, scope three emissions, and help them in that process.

And one of the interesting things that we found was, carbon dioxide is the number one. CO2 emissions are the number one thing that companies measure. Here, they were losing in one of their processes. In their manufacturing process, they were using SF6, which is sulfur hexafluoride. And they were using a very small quantity of that. So it was about, thoroughly purchasing about 15 kilograms per year, or something like that. So that it got missed. In the grand scheme of things, when you’re reporting, you’re doing the 80/20 rule. So you’re looking at what, where 80% of your cost is being built in purchasing raw materials, and where what your emissions are. And this SF6 is about three times, 1,000 times more potential in terms of emission impact, than CO2. And because it was a small quantity, it was flying under the radar. The biggest moment was figuring out something that’s small from an expense perspective, but very big from an efficiency impact. So that’s one use case.

The second is very interesting from a different perspective. What companies struggle with today is who’s paying for all the sustainability?

Kashyap: I was going to ask you, is there willingness among companies to pay for sustainability, or is it incentivized? 

Mukund: So most of it is, either because it’s a corporate social responsibility initiative or because it’s a compliance requirement. So in this case, we work with a packaging company, which does packaging for pharma and food and beverage companies, and their question was, “Okay, I’m investing in all the sustainable packaging. How do I articulate the value that I’m delivering to my downstream customers in the supply chain?” That allows me to say that if I’m investing this much, I need to get something back? How do you change the game from doing this for check marks and compliance reasons to actually competing on sustainability?

So we put a solution together that helps companies have a dialog with customers and other partners in their value chain. This way, they can say, “If I’m investing this much, this is how much I’m saving you from a sustainability perspective. I can save on your reusability cost. I can save on how much you can recycle.” And that’s where data science and everything comes into play. How do you articulate the value of your sustainability effort and pass it on to your value chain so that you are more incentivized?

Your incentive is compliance. It’s only going to go so far. But if you’re actually starting to compete, what do middle management get measured on? They get measured on how you optimize costs, how to maximize profit, save money, or make money. So it’s got to come back to that. How do you explain sustainability benefits in dollar terms? There’s a huge gap there today, and that’s what we’re looking to solve.

Kashyap: And that was my question, not just the willingness to do it, but also the possibility of doing it. There must be a different set of challenges in terms of implementing data science solutions for sustainability within organizations, right? Even when it comes to solving many problem statements, people are talking about AI strategies. Sridhar Ramaswami, and others are all discussing data strategies first. So the availability of data is fundamental to the entire AI strategy. Given the collection of data around this, for example, the packaging example you mentioned, how effective is the data collection for solving these problems? What are some of the other challenges you face?

Mukund: So, I explained a little bit about Scope 1, 2 and 3. Scope 2 which includes everything happening within your company’s boundaries. Scope 3 covers things that happen outside of your company’s boundaries, such as things you buy, services you purchase, employee commutes, and transportation of your goods, as well as how you dispose of goods at the end of their life. The data for all of this exists within some part of the ecosystem. The problem is not whether the data exists or not because there are tons of smart meters, sensors, IoT devices, and other tools available today. The problem is how to get the data together. There is a need for almost an exchange to get that data to the right people at the right time.

How do you trust that data? How do you know if somebody is saying their emissions are accurate? These are some of the challenges that still exist, especially with Scope 3. Many companies are trying to figure out how to measure and report on Scope 3 emissions more effectively. That’s why the protocol has not made it mandatory yet, as they understand the challenges in reporting these emissions.

Kashyap: So, that’s a framework to start measuring the carbon footprint of your company and putting the data sets together to understand the fundamentals within the company. But that’s very interesting. Now, another question I have is about using data science to cut carbon footprints. However, AI and data science, in general, also emit a lot of carbon. It’s an ironic problem, and many people are struggling with it, especially with large language models and huge data centers that require significant processing power. What is the solution?

Mukund: There’s always this consideration that compute power has increased so much, but it’s also adding to the carbon footprint. Because you’re burning everything. So, I don’t think there’s an easy answer to that, but I think being watchful of where every AI solution doesn’t deserve to become full scale is important. It’s about picking and choosing use cases wisely. Also, consider how to write code more optimally and how to optimize resources. That needs to be measured, penalized, and taken into consideration. 

Kashyap: Should companies have an AI tax?

Mukund: I think at some point of time they will. Carbon tax is reality. 

Kashyap: Sometimes, when it comes to carbon emissions and footprint, my understanding is that for really large language models and data systems, the impact might be significant. However, for data solutions used in querying to build regression models or traditional AI models, the energy savings might outweigh the costs. Looking ahead, what are some of the more innovative, emerging solutions that your company is exploring to address this issue? Your company focuses on this area a lot, right? Over time, as a company focuses on a particular area, they are always building state-of-the-art solutions early in the journey.

Mukund: We are still early in the journey in this case. We’ve been thinking about it for the last year and a half or two. Some technologies that have attracted our attention include the whole concept of digital twins, which is essentially a virtual replica of your physical infrastructure and plant. The advantage is that if you can simulate a digital twin with some degree of accuracy, you can look at different processes for efficiency and understand the impact in the virtual world before investing more cost and potentially causing more emissions by experimenting in the real world. That is something we are looking at very closely.

Generative AI also holds significant limitless possibilities. Ultimately, Gen AI can play a big role in what we discussed earlier: data is there but it’s all over the place. So organizing data so people can easily exchange information. Because somebody has scope 1, 2 or 3. So data might reside somewhere, but there’s no easy exchange of information. Gen AI’s strength lies in connecting dots that were very hard to connect earlier. We want to explore the possibilities in that regard.

Blockchain technologies can also play a part. Blockchain can help ensure the integrity and security of data exchanges, making it easier to trust and verify the information being shared. These are some of the things we are currently considering.

Kashyap: One of the things we talked about at the very start when it comes to data science and sustainability is incentivization? Over the years, we have observed that sometimes there is a lack of motivation? And rightly so, as everyone’s careers depend on the money they make or the costs they cut, which is fair enough. Incentivization can help to an extent. How do you envision integrating this into the culture of organizations and taking a data-driven approach for the same? At the same time, what is the role of different stakeholders in the ecosystem, not just enterprises and vendors, but also other stakeholders in government and education?

Mukund: So, if you look at it compared to something like diversity, there are incentives and mandates today that require a certain level of diversity in your supply chain as a vendor. Similarly, if you have sustainability as a line item, companies are incentivized to say, “I can really compete on sustainability. My product is more sustainable, and I’m able to show that to the people in which I operate. My chances of winning business are higher, and my product can compete better in the market.” This ability to translate sustainability into business goals is largely still a CSO initiative. It has to be something that can be measured at the operational management level, using a data-driven approach. Your goals are set using that data, and your goals—meaning not just soft goals but also cost, revenue, and profit goals—can be directly tied to sustainability efforts. That is something that is still nebulous today. It’s not easy to convert your sustainability efforts into dollar terms, but that’s where data can help. 

Kashyap: And I think this change needs to be driven by people passionate about society. It is not an overnight thing; suddenly, everybody becomes carbon-conscious. Companies like Apple and Microsoft are already on their way, making big claims and working towards it. But what about some of the companies still transitioning? What is your advice in terms of taking the first step or the next step?

Mukund: I think the first step in my mind will be a mindset shift where you stop looking at sustainability as a checkbox and look at it as a competitive advantage. Or how you can enable that, because I think the regulations are going to get even stricter. And if you continue to look at it only from the regulation angle, and say, “What do I need to do to get it done with?” your competitive advantage in the market is going to go down. The people who want to flourish are those who have the mindset that they will turn this into a way of doing business and compete better. So this will affect my costs, prices, margins, and marketing strategy. And how I want to market myself. If I start measuring my suppliers, and they start measuring their suppliers, then the whole ecosystem will tune to think this way. This happens because no one wants to lose business to a more sustainable competitor, and you will do whatever it takes.