Digitizing Sustainability: The AI-Powered Transformation of Business

The convergence of AI and other cutting-edge technological advancements with escalating sustainability concerns has placed digital transformation at the forefront of every organization's agenda.

In this episode R. Mukund of Benchmark Gensuite and Lori Coombs, former NASA Sr. Principal Technical Manager and current DHS Sr. Networking Engineer, explore the critical intersection of digital innovation and sustainability, moving beyond buzzwords to offer practical guidance for Chief Sustainability Officers (CSOs) and business leaders. We examine the transformative potential of digital solutions, particularly AI, while addressing the crucial need for responsible implementation and a balanced approach to adoption.

Key learnings from this podcast:

• Learn how AI empowers companies to analyze vast datasets, improve ESG reporting, and enhance disclosures, while addressing the challenges of AI adoption.
• Explore why enhanced data, robust governance, and streamlined processes are essential for sustainability success.
• Discover how organizations are moving beyond siloed approaches to embrace integrated strategies connecting finance, EHS, and sustainability.
• Understand how CSOs can engage diverse generations in sustainability and tailor AI-driven strategies for maximum impact.

Host:

• David Metcalfe, CEO, Verdantix

Guests:

• Lori D. Coombs, CEO at WWCM and former NASA Sr. Principal Technical Manager
• R Mukund, CEO at Benchmark Gensuite

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Chapters

• 0:00 Digitizing Sustainability in Business World
• 10:06 Sustainability Challenges in Manufacturing Industry
• 14:46 Implementing Digital Solutions for Sustainability
• 32:44 Navigating AI for Sustainability Success
• 35:45 Adapting Solutions for Organizational Complexity

Transcript

00:09 - David (Host)

Hello, I'm David Metcalf, CEO of Verdantix. Verdantix is the essential form leader for world-end marketing innovation. I'd like to welcome you to the Curiosity Applied Podcast, where we debate topical issues relating to the scale, shape and velocity of change in the business world. Today, we'll be talking about best practices for digitizing sustainability. What are the primary ways in which firms should use digital solutions to improve sustainability performance? How can ESG risk management be enhanced? What is the potential for AI in 2025? 

00:43

I'd first like to welcome R Mukund, who is the CEO of Benchmark Gensuite, one of the world's leading EHS and sustainability software firms. Prior to founding Benchmark Gensuite, Mukund worked in senior roles at GE and he holds a PhD in environmental engineering from the University of Illinois Urbana. Welcome, Mukund. Thank you, David. Joining Mukund on today's podcast is Lori Coombs, who is a former senior principal at NASA and currently working in a senior IT role at the US Department of Homeland Security. She's also the CEO of WWCN and has advised a wide range of US government agencies. Welcome, Lori, thank you. To start off the discussion today, what I'm really interested to learn about is what you see as the sustainability business challenges in 2025. 

01:43 - Mukund (Guest)

Mukund, if I could start with you on that, With regard to the question you asked, in terms of what are the key drivers or key challenges, key priorities, I would say, from an organizational, enterprise, organization perspective, the three things that we're hearing are enhanced data acquisition, improved governance and process streamlining. Those things are top of mind of sustainability leaders that we speak with as they look at 2025 and 2026, getting ready for you know what is on the horizon 26, getting ready for you know what? 

02:28 - David (Host)

what is on the horizon? Great, and to what extent did you would you say that you're seeing the level of ambition when they think about digitization as increasing, given the business context around sustainability, where I think there's more uncertainty in 2025, certainly in the? U US, than there has been previously? 

02:53 - Mukund (Guest)

Oh, great question. I think what we are seeing is that the convergence of multiple drivers is causing sustainability leaders in the enterprise to think about the needs for that sort of consolidation of approaches and solutions, such that the outcomes, if you will, can be better addressed and court required, not required currently. The reality is that there is a general convergence of those requirements that is causing a strong interest and a strong motivation, as I said, to being able to acquire data, have better governance and to have to not spend as much time doing it and then redoing it. At the moment, it's done because you've got to get ready for the next court-unquote cycle. How do you get real-time data and real-time good data? That's what I think is driving it, because of the convergence of requirements. 

03:56 - David (Host)

And final question for you before I go to Lori on this question how much do you think these requirements have changed in the last three years? Are we seeing a more strategic thinking around digitization or are we still maybe locked in the world of point solutions, tackling the different sustainability challenges out there? 

04:21 - Mukund (Guest)

I think what we're seeing is more of the functions, as what I call the convergence theme is becoming more visceral, if you will, more easily perceived. What we're seeing is that the functions that have historically been doing activities that are now coming together, are needing to find a way to operate with each other in a more cohesive way. So, while I would term it more sort of siloed solutions rather than point solutions, because I think even if there are point solutions or a platform or whatever, typically they've been within the silo of, say, finance or EHS or sustainability, but now the convergence of all of those measures is coming together into pulling all that together. So I think this concept of getting systems to talk to each other is a big driver within each of those functions that are still individual. Distinct functions will probably remain siloed but need to talk to each other better rather than once a year in some sort of patched together way. 

05:39 - David (Host)

That's a really interesting distinction to make and I definitely agree with it, that idea of different functions have got their own siloed solutions that may be quite comprehensive in terms of their functionality but and not enabling that connectivity across their longer chain processes. Lori, if I can come to you, I know you operate in a different, you know part of the world in the sense of looking more in the public domain and government agencies. What are you seeing are the current requirements around sustainability of the mainland war area. 

06:14 - Lori (Guest)

Research is showing a growing concern for the advent of Gen AI. This is part of the narrative of innovation and progress. The global proliferation of data centers, driven by advances in the AI industry, is placing new demands on grid operation and capacity expansion. We have to consider the amount of electricity required by AI data center racks. This is estimated to require seven times more power than traditional data center racks. This is estimated to require seven times more power than traditional data center racks. Goldman Sachs estimates that there will be a 160% increase in demand for power that is propelled by AI applications by the end of this decade. At 2.9 watts hours per chat, gpt request AI queries are estimated to require 10 times the electricity of traditional Google queries, which use about 0.3 watts hours apiece. So to put that in perspective, as organizations aim to make their Gen AI models larger, they will need to consume even more energy for both training and other purposes. So to meet this demand, the energy industry will need to find solutions to support and cope these challenges. 

07:42 - David (Host)

That's a really interesting point. I mean certainly a very live debate in the technology world in terms of the balance between the benefits of generative AI for sustainability use cases, which we'll talk about, but then the costs associated with the construction work for new data centers and then the power that is required for that, and certainly I've been hearing from CSOs and other people in power utilities in the US that they're having to deal with this challenge of having to increase electricity production, and it's not just data centers, it's also EV charging as well, so maybe we can talk about this then. So what are the technology then solutions that need to be thought, the digital solutions to bear to help resolve this question around the demand for the energy systems? 

08:41 - Lori (Guest)

Absolutely. I would say that the growth trajectory underscores the need for greater collaboration amongst policymakers and regulators, utilities and industries globally. So the goal is to generate clean energy, sustainability and, of course, make it affordable. So this is going to require a framework that accommodates this resource planning methodologies. The grid investment is going to be driven by the energy resource planning, so that is something that has to be considered when power companies map out how they handle these expected load increases. Typically, we are normally looking at equipment capacity, but now we've got to look at a much larger and grander scale. So this is going to take regulation. A regulated approach worked well in previous eras, but now we've got to look at energy equity. We've got to look at affordability for end use customers. 

09:42 - David (Host)

Okay, so there are some very specific questions and I know there are solutions like distributed energy resource management systems DERMs I always call that an acronym by engineers for engineers and that is really helping in terms of optimizing renewables on the grid, both in front of and behind the meter. As we're talking about these industry sustainability challenges I know Mukund, your company, does a lot of work in the manufacturing space Are you seeing specific new sustainability requirements from those companies? 

10:26 - Mukund (Guest)

I think what we are seeing from them is twofold. One is how do we, as I mentioned earlier, how do we get these data streams to get captured so we have some more automation and connectedness with what is going on in the enterprise? And that should be a quote, unquote, a simple putt. But the reality is that, given that, no, if you were building a plant today, you would put all your sensors in, you would have data acquisition systems, everything would be connected. But we also got to recognize that 90% or 95% of operating sites already exist. Some of them have existed for a hundred plus years, certainly 20 years, 15 years and these things weren't in place. So we have a patchwork out there. 

11:16

So what we are seeing from our customers is that these enterprises are saying how do we get better? And there's other drivers, there's operational efficiency, there's cost out, there's all the other things that are driving it, but it's still siloed. Going back to my earlier point about you know that's a manufacturing function or a supply chain function doing its thing. How do we then, as from a sustainability function, how do you connect that so that all those functions that have those siloed systems can get tied in? So that's. 

11:45

The other piece is how do we get more productive with how we're compiling that data? Make it more assurance ready, avoid restatement of things. Get more enterprise engagement, because you cannot engage your functions if you keep bugging them for data and then you bug them again two weeks after they gave you the last set of data. That cannot be how you sort of operate. Sustainability. I think that's what we're hearing from the field. It's a frustration of I don't have the data, but yet in order to collect it I got to annoy everybody and then they are no longer our allies in sustainability. 

12:25 - David (Host)

Yeah, and it's interesting because that also applies in terms of supplier engagement. We frequently hear about suppliers are getting fed up with being asked about survey after survey that they have to complete. Well related to this, then do you think on the requirement side, you're saying well, there's a need to connect these siloed systems, there's a need to get better integration of the overall sustainability data management system. That usually implies that a new vision and governance approach is needed, that you can't be a chief sustainability officer who buys a software product and then goes out and asks everyone to provide the data for it. Are you seeing much change? Are CSOs working a lot more with chief information officers? 

13:16 - Mukund (Guest)

We're seeing them work more with information officers when it comes to data aggregation. 

13:23

That is probably where we are seeing more of that activity because, especially as some of this data are showing up in financial reports or various filings or within the corporate reporting system, it's elevating the importance and the visibility and accountability of that data. So, as a result, that's where the IT security people are coming in and data officers are coming in to verify the quality and integrity of the data and we are seeing some level of sort of connectedness where CISO's asking the question how are the individual functions compiling this data? What systems do you have? But not as much, because I think it's still a bit of a turf battle, if you will. I mean, everybody's got their little fiefdom. It's my function, your function, every enterprise is always going to have that, and you have people responsible for it as chiefs of something, and I don't think CSOs necessarily yet have that clout to dictate what a specific function can and will do, other than to say but you need to be prepared to provide this kind of data on this kind of frequency and this kind of accuracy, if you will. 

14:43 - David (Host)

Okay, interesting. Lori, you've obviously worked in some very large data management and transformation projects. If you were to sit down with some chief sustainability officers who are saying I'm not getting the digital solutions in place that I need, I'm not getting the buy-in, what would you recommend that they did to progress with their digital strategy? 

15:07 - Lori (Guest)

Absolutely Great question. I would reference a use case, that is, the AI for sustainability at the government level, the Smart City Initiative. This project is located in Copenhagen, Denmark. The duration of this use case project is from 2018 to 2023, and the investment was approximately $300 million. The Smart City Initiative in Copenhagen aimed to use AI and IoT technologies to enhance urban sustainability, improve resource efficiency and reduce the city's carbon footprint. 

15:44

I think that the key takeaways that we have to look at are those key performance indicators the carbon emission reduction, which measured the decrease in the greenhouse gas emissions. The energy efficiency, which assesses the improvements in energy consumption across the municipal buildings. The water uses efficiency, as well as the waste management efficiency. The reason that I would look at these KPIs, especially when it comes to transportation efficiency and even air quality improvement, is because, looking at the results, when it came to the carbon emission reduction, 50% was reduced in carbon emissions by 2023. So that means that the result achieved a 55% reduction. In terms of energy, there was a 30% increase in energy efficiency in municipal buildings, so they achieved a 35% increase. Water usage efficiency was a 25% reduction in water. Waste Waste management efficiency was a 20% increase in recycling rates, as well as a 15% reduction in landfill waste, and so, ultimately, the result achieved a 22% increase in recycling rates at 18%. Transportation saw a 25% decrease in traffic congestion, a 30% increase in public transportation use. Air quality improvement saw a 20% reduction in air pollutants. 

17:15

So, ultimately, when we're looking at implementation, we're looking at technologies that are being used. Ai and the Internet of Things integration are tools that are used to monitor and optimize the resource usage. Smart grids, of course, enhance the energy distribution and consumption efficiency, as well as the smart water systems. The real-time monitoring and management of waste supply networks are benefactors here. Waste sorting robots improve the recycling and the waste management. The intelligent transportation systems optimized traffic flow and encouraged public transportation use and, of course, air quality sensors monitored and provided real-time data on air pollution levels. So, inclusion I would say that AI is proving to be a transformative tool in the quest for sustainability, from optimizing energy use and renewable energy generation. I believe that this was a successful use case and will be crucial to forward success. 

18:22 - David (Host)

It's a really interesting example. You make me want to go and check out Copenhagen. What is it they've done? But I think it's what's interesting about it. Lori is that we tend to see success comes from companies where there's an executive with a high level of ambition and I think you said it was a 300 million dollar project, which is far, far beyond the budget of most chief sustainability officers. But what's interesting about it is it demonstrates that the technologies are there. If you have the budget, if you have the executive sponsorship, then you can actually achieve these really impressive results. And I think this is a great lead into the next section that we wanted to talk to, which is what are those digital solutions out there that chief sustainability officers, whether they're part of a city government or in a chemicals company or an automotive firm what should they be aware of? If I can ask you that question first, Mukund what do you think CSOs should really get educated about in terms of? Here are the things that you should be considering to invest in in 2025?. 

19:37 - Mukund (Guest)

That's a great question, that's a great lead-in from Lori, your comments around that smart city, Copenhagen and I agree with you, they would love to go visit Copenhagen now and see that action. 

19:50

But I think what you point to is the fact that you know there was an overarching sort of construct created that looked at all of these different silos or you know, the imprimatur, the push from their board and their C-suite to say this matters, and usually that matters because of a vision that the C-suite has, reflective of the operating profile and the market profile of the enterprise that they lead and the market profile of the enterprise that they lead. And when you look at it that way, it represents an opportunity and a risk Opportunities for demonstrating leadership and differentiating in the marketplace, and risk, as in managing whatever the downsides are that this enterprise may be wary of and needs to be prepared for and that its stakeholders would expect it to be prepared for. And I think CSOs then can align with those corporate objectives in a much more effective way to drive the kind of systems for transparency and visibility that those executives and board would ask and be particularly interested in, because which board is not interested in leveraging opportunities and mitigating risk? 

21:22

I mean that is what the board is for. So if a CSO is able to align with those objectives, then that's sort of like that positive energy that you get from being aligned and then the systems then connect back to your operating profile. I'm a big believer that you have to start with what your operating profile looks like. Once you know that, once you have an idea of what your operating profile, your market profile, look like, you can start thinking about what type of systems do you want to invest in. In some industries that may be IoT. In others it may be AI-driven. In others it might just be boots on the ground. 

22:02

We've got to implement operational systems that allow us to compile, collect this data, that allow us to compile, collect this data and build a level of a foundational, you know, sort of starting point. You can't go to the moon and you don't even have, you know, the fundamentals in place, and I feel like in many cases, CSOs, you know they are also hostage to the fact that the report's due in March. So now you're going to react to that and that's what you, but you have to sort of walk and chew gum at the same time, if you will, which is, you've got to do that report for March, but you've got to be thinking about next March and whether you're going to do this the same way next March. So systems that can enable that, I think are really the top priority, and then that will align with the board's objectives of opportunity and risk management. 

22:54 - David (Host)

I think you've made a great point there. Mukund, which is historically the focus of chief sustainability officers, has been on digitizing the data collection for a reporting output, and really now they need to be thinking about risk management. They need to be thinking about performance improvement, and those may be very different digital systems that you're thinking of which are actually not tied into that whole flow of information that goes from suppliers and product LCA and internal ERP systems and then out to different stakeholders. So maybe I was asking the wrong question that we shouldn't be thinking about what are all the technologies you could buy. It's more what you're saying in terms of the executive vision and then the operating context of the business. Let's switch over, then, to talking about AI. Lori, when you think about potential for AI, before people leap into that, they obviously need to consider what are the guardrails, what's the framework, what thinking do they need to do before they start using AI tools for sustainability use cases? What would you recommend in that context? 

24:07 - Lori (Guest)

That's a great question. Ai when it comes to some of the I would say the concerns that research is showing, three main topics the digital divide, which is ensuring that there's equitable access to digital technologies that's occurring for long-term sustainability. Job insecurity people are concerned with technological advancements leading to job displacement and instability. It's very important to consider how AI displaces manual jobs and increases automation. Upscaling talent and ensuring that there are established methods in place to combat displacement has to occur sooner rather than later. Data privacy is a big one. Protecting data privacy and security continues to be a major concern. 

24:59 - David (Host)

Okay, so actually you know, before people leap in, then they need to put all these guardrails in place. Look, and I know, at Benchmark Gensuite, this is something that you have been working on already, and so you have experience engaging with your customers. Where do you think they've got to so far in terms of seeing the pluses and deltas of the use of AI for EHS and for sustainability? Absolutely. 

25:27 - Mukund (Guest)

Yeah, so I think in the corporate sort of enterprise world, it is, as you touched on it, still a very data-focused activity, because you are dealing with big volumes of data streams, but you are working on, essentially, a way to present your story, either in a way that's mandated or in a way that you want to present it. That's one part, and then the other part is the operational kind of sustainability improvements. How do I use this data, then, to drive improvements in my enterprise? And what we have seen is, you know, in the work that we have done with our customers, such as Parker and Eaton and Corning, is that we have really focused around looking at, you know, on the data side, being able to use AI for anomaly identification and analysis and insights, being able to use it for comparisons and sort of identifying what that data set looks like relative to things I might have already captured before, things that can help me spot, because that's otherwise a very manually intensive activity. The other part of that, going to the reporting side, is where, of course, there's a lot of excitement, which is how do I then, since most you know, this is one of the most interesting things about sustainability data that is different from financial data, and something that CFOs and finance folks have a lot of conflict with, which is, you know, there's a lot of malleability and ambiguity, and sustainability data, which is different from financial reporting stats, if you will, while there is sort of a fixed number on greenhouse gas, but, as we all know, when you get down to it, there's a lot of ambiguity there. It depends which emission factor you used, and blah, blah, blah. You can go on and on. So you certainly have a lot of ambiguity there. But, with that said, I think what AI is helping and that we are seeing already early signs of benefits in is one being able to use that data set in ad hoc reporting. 

27:48

We get a lot of ad hoc reporting, sustainability reporting requests. You alluded to it. For every supplier request an enterprise sends out, it is the supplier to somebody else, and so you're on the receiving end of similar disclosure requests. And one of the areas we collaborate with our drivers over the last 18 months is a tool called Responsio, which is leveraging the use of AI in an AI-powered workflow, which is to say, as you get the request, can you essentially put it into motion, look at the data sets that you already have so you can craft a response. You still have to look at it, but it's done the lion's share of doing the work. 

28:33

So extremely exciting because it's for all the reasons, Lori, you mentioned about the power of generative AI in harnessing it for essentially telling your story better, because that's what a disclosure request is, and we're certainly that's where we're seeing opportunities. You know, there's obviously uncertainty in terms of all the regulations being either in development, not finalized, et cetera, but the disclosure side and the data governance side isn't changing, and the ability to use this data for essentially operational good is also a big driver. So this is where we're seeing AI come in, be able to help analyze big data streams and then be able to help us generate a cogent story out of it. Okay, okay. 

29:23 - David (Host)

Interesting and, Lori, one of the things that's interesting that you brought up earlier on is the question about job insecurity and how do people respond to it, which I think, as you roll out any innovative technology everyone needs to be conscious of. Do you have any insights in terms of different generational groups and how a chief sustainability officer who's trying to leverage AI to improve their sustainability outcomes should think about engaging those different generational cohorts? 

30:01 - Lori (Guest)

I'm glad that you brought that up. There is an importance that needs to be placed on circular economy, which dissects all generations. Digital AI solutions are supporting the implementation of circular economy principles. This is promoting the sustainable production, consumption expelling, renewing back to production in this bit of a cycle. So, no matter the generation, digital AI solutions are supporting the implementation of supply chain optimization. Simply put, ai-powered supply chain management systems are optimizing material flows, reducing waste, identifying opportunities for resource recovery and reuse. We are seeing optimization in ways that AI algorithms are analyzing data to optimize the supply chain operations. 

31:04

So, when you're thinking about the planning, the sourcing, making, delivering and returning, there's an upside there. When it comes to resource recovery, ai is also identifying materials and waste streams and facilitating recovery. In terms of the product lifecycle management, ai is helping in designing products for durability and recyclability. So, again, when it comes to waste sorting and recycling, we definitely are seeing an upswing with the AI driven robotics and computer vision systems. 

31:34

Folks are becoming more upskilled with a lot of the cybersecurity principles and I know it's a bit of a culture change and we're moving toward that automation. But I do believe that with time, we will see economies invest in more automation technology and this is learned behavior and I think that in terms of the impact on jobs, based on surveys that I've seen, there are some apprehensions. As increased AI occurs, it will lead to a bit of a more understanding and a variable society In some cases. I'm very big on human in the loop. When it comes to just AI technologies in general, I am very much an advocate for humans being the final decision makers when we're dealing with anything of automation, and I do believe most are on that same page. 

32:39 - David (Host)

Well, I think you've opened Pandora's box there. So one of the questions I have we at Verdantix have an AI applied research practice and the analysts in that team tell me 2025 is all going to be about agentic AI, so AI agents. So let's maybe push the envelope a little bit for sustainability, technology innovation. What do you think I mean? Is 2025 going to be a year for sustainability, ai agents, or are we still quite a way away from the matrix appearing as part of what CSAs do? 

33:24 - Mukund (Guest)

I think, given where we are today, I think it's going to be a two-part effort. One part is people are still going to have to focus on the immediate and the now, which is I have to compile data, I have to put it in, I have to clean it, cleanse it, do all the things I need to do and get better data insights. So I believe that we will be using in 2025, we will be using AI to assist us, help us in that effort, hopefully start to get AI in workflows so that we can really, if you will, turbocharge those workflows and make them more productive for ourselves. It may, and hopefully it will, lay the groundwork for the agentic outcomes we're looking for, the AI agents that may come in the future, beyond 25. But I do think anything we do in 25 will be subject. 

34:29

If we build an AI agent, it is going to have so many sort of challenges because the data set and the learnings, the knowledge base that we have for it in the sustainability space, is still not evolved enough sufficiently and rails and the applicability to the question that we're posing whether it's an organization or a commercial enterprise, it doesn't matter. 

34:54

That data set and that information set has to be relevant to it. Otherwise it's going to generate some BS, which is essentially another word for hallucinations, which we all look at and say this is great, but this doesn't apply. And to your point, Lori, about the fact that a human looking at that will say, well, this is crazy, I'm not going to do this. And then you sort of go back into the well, it doesn't work, whereas I think if we focused on using AI to assist us and to help us drive more effective, impactful, productive workflows, then we can start to build a capability for an agent that could do more of the autonomous things that we would like it to do, so that we can ultimately then review something without having to do all that other work. 

35:45 - David (Host)

Lori, we're going to have to wrap up soon. 

35:47

So here's a question for you, going back to your experience at NASA wrap up soon. So here's here's a question for you, going back to your experience at nasa, when you think about the way that ai is going to generate benefits for companies or public agencies working on sustainability topics. If you were to think about the space shuttle and you can either go to the store and you've got a million dollars to spend on a project and you can buy a pre-built space shuttle okay, so there's nothing you need to do. You just buy the space shuttle. It's all built for you, um, and it's. 

36:25

I'm talking about a toy, a toy space shuttle, oh, or else, yeah, it would cost a little bit more than a million dollars, um, or else, um, you need to go and buy lego blocks to build a space shuttle which is potentially going to be better than the one that's pre-built. So, a chief sustainability officer, if you're like the way they need to think about AI, is it like going and buying a pre-built space shuttle, like you go and buy SAP or Oracle ERP and you implement that, or should they really be thinking it more in terms of Lego lot? 

37:05 - Lori (Guest)

That's a great question. I know that depending on the program, the project, the size of it, that answer could vary. You've got to really understand your program because for some programs I would say Legos would work. There are some programs, whether it's medical in nature or space-based, where just IoT is used and it can optimize resource usage. But then there are other programs where I do believe a bit of a COTS or a commercial, off-the-shelf, all-put-together approach works. 

37:43

The reason I say that is because every program is unique. I've been on different programs that have different requirements and different timelines. I could see the benefit of both and I could actually argue for both based on that approach, and I could even say that a hybrid of the two is possible. And the reason I say that is because, when I think of all of the key performance indicators and the things that go into what you're discussing, it's important to consider the carbon emissions, the energy efficiencies, the usage efficiencies. You'd want something that is just ready-made, but others that are very specific, especially programs that deal with national security and have very unique niche requirements. You're going to have to go Lego bit by bit, block by block. 

38:43 - David (Host)

Yeah, that's really interesting. I was expecting you to say everything has to be Lego, because you can't just get one big solution that is based on AI. But as you were answering, I realized actually, if you look in industrial software, what they do is they build advanced process control systems, which is kind of creating the pre-built system, which is a little bit monolithic. So you've corrected me in terms of what my hypothesis was. Mukund, if you could close us out, what's your view? What are you seeing? Do you see any of these really big wins from AI, or is it more okay the Lego blocks approach, where you have to look at lots of use cases that collectively give you that space shuttle impact? 

39:31 - Mukund (Guest)

Probably more of the latter right now, because I think the industry is evolving, our state of the art is evolving. It won't become a science until we evolve the art a little bit further and that's going to take a little bit more effort. But having said that, I think we're going to take a little bit more effort. But having said that, I think we're going to find solutions very quickly in the market. Initially that will purport to be the complete fill-in-the-blank solution. But the reality is that in any enterprise there's going to be sufficient organizational and operational complexity that it will need to be adapted to address the fit for purpose. So we will have a fit to purpose sort of exercise that will be needed and it's probably going to take some time to get there. 

40:27 - David (Host)

Well, that's wonderful. I love to finish on a very sensible, practical note, as opposed to some big bang theory. So, Mukund from Benchmark Gensuite, thank you very much indeed, and Lori Coombs, thank you as well. It's been great listening to both of you today. Thank you for listening to this episode of Curiosity Applied by Verdantix. If you enjoyed today's conversation, then please leave a positive review and subscribe to Curiosity Applied wherever you listen to your podcasts. Special thanks, as always, to my amazing production team.