Productizing AI and Internal Copilots with Eastman CIO Aldo Noseda

Hi there and welcome to Enterprise AI Innovators, a show where top technology executives share how AI is transforming the enterprise. In each episode, guests uncover the real world applications of AI, from improving products and optimizing operations to redefining the customer experience. I'm Evan Reiser, the founder and CEO of Abnormal AI. And I'm Sam Motamity, a general partner at Greylock Partners. Today on the show we're talking with Aldo Noceda, CIO at Eastman. Eastman is a roughly $10 billion specialty chemic company with more than 100 years of history. Their perspective on AI is especially valuable because they're applying it across diverse functions while navigating the unique safety considerations of the chemical industry. A few things stuck with me from this conversation. First, Eastman isn't only using AI internally, they're turning it into a product. They launched Fluidgenius, which uses AI to predict when thermal fluid in a customer's plant will degrade, so customers know exactly when to act. It's not AI for AI's sake, it's AI packaged as a customer facing service. Second, they've taken an AI for the masses approach with an internal engine built on industry solutions like ChatGPT, wrapped with both security and company customization. The numbers are kind of wild. 6,000 recurring users, programmers going from 5,000 lines of code a month to 40,000 with AI agents. And an AI powered IT help desk stood up in two weeks. And finally, Aldo made a point. I think a lot of leaders need to risk tolerance has to be situational. If you're building a coaching tool for sales, you can take some risk. But if you're opening or closing a valve in a manufacturing plant, you better have the right answer. Alda, first of all, thank you so much for joining us today. Maybe to start, can you give our audience a bit of background of kind of your career and your current role at Eastman? And like, maybe for people that aren't familiar with Eastman, maybe give a little context. I think probably people don't quite understand the impact of the scale of operations you guys run there. Yeah. First of all, thank you Evan, very much for inviting me to this podcast. Aldo Noceda, CIO at Eastman I've been with Eastman around eight years, coming from a very long career with Monsanto, 27 years before that and you know, started my career there with Monsanto there evolve, had multiple roles in supply chain, commercial as part of my career, but always coming back to, to, to it. About Eastman, you ask approximately a $10 billion company focused on both commodity chemicals as well as Specialty Chemicals, a company of more than 100 years trying difference in the world. In the last years we have been putting particular attention to this idea of circular economy as an add on to our specialty set of products. Maybe to start, like love to hear how you guys think about like approaching AI development, right? Especially in kind of your organization that's like so multifaceted and so diverse through the different parts of the business. Like you know, how kind of what roles AI play for you guys today. First of all, you know, you're right. There's a lot of moving parts on the AI journey, right. We had the fortune that we created a data science team many years ago. So we had the foundation and we started with statistics, machine learning, operational research, general artificial intelligence. And it was in the last couple of years that we really started to put attention to generative AI the way that we are approaching the problem. First of all, before we go there, we don't know how much we're living a historical moment or how much we're living a hype, but we are assuming that it's going to be important, that the impact is going to be really, really impactful, you know, and because of that we are tackling both the yin and the yang. We're tackling the how can we bring capabilities to our company to make a difference as well as mitigating risks. And we're treating both sides of the coin very carefully because we believe both are important. But whatever our plans today are probably going to be different a year from now because this moving fast and changing and we're trying to be dynamic in this perspective. Are there any particular kind of innovations where AI has like kind of augmented some of the kind of maybe the conventional research processes that you should feel really proud of, right? Or kind of what's had the biggest impact? What we are doing right now, we are experimenting with this model of trying to read the tech reports that we have historically in our archives and then try to bring combinations and knowledge that we didn't have before. Took a lot of time to pull it together and try to accelerate. I think we're going to see in the next year impact of what generative AI, on top of what we have done with data science can do to R and D. But obviously we are trying to apply the same concepts to many areas of the company beyond R and D. R and D is just one of the examples. The big difference is like before, like we said in R and D specifically, machine learning, statistics, operational resource, all techniques that help discover products. But you in general Needed structured data for those models to work. Are there any of the use cases that are maybe outside of R and D? Right. There's another maybe CIO listening and saying, hey, I want to kind of steal all those pro tips about kind of smart things with AI. What about kind of outside R and D? We at Eastman in the last years started to do something fairly unique for the chemical industry, is that we started to offer to our customers digital solutions in the form of services. And we have four products in the market right now that we are. That we are offering. And one of those is the product is called Fluid Genius. And what is Fluid Genius? We sell something called thermal fluid. It's a liquid that you use in your plants in order to keep heat around the plant without the need to put multiple furnaces across your operation. So our customers that own that operation don't want that liquid to degrade over time because they want the heat. The problem is that this particular liquid, which the thermal fluid has a degradation once it starts. So we were able to develop an AI capability that predicts, you know, the degradation of the liquid over time. So now we have embedded that engine inside Fluid Genius, which is the name of a product, and our customers can put some data, put some sampling of information and can get a prediction from the engine of when their liquid is going to degradate. Why that is important because then they know when they need to stop. Stop or to create some maintenance for the plant. And which is, of course, translates into money. Our customers love it. You know, we are having a lot of success as another example where again, artificial intelligence, data science is starting to make a difference. And I could go on and on. Those are two good examples, I guess. So am I kind of understanding correctly like the AI will help? You know, there's sensors that will kind of sample the thermal fluid, then kind of basically give kind of preventative maintenance warning so they can kind of prepare to do that maintenance. It's not kind of like unscheduled. Absolutely. But the difference here is I think you're absolutely right. I think that the tick is certainly we work in data science and artificial intelligence as much as possible. The maintenance of our plans. This has been a niche offering for our customers in order to help them, you know, with the maintenance of their own plants. But you're right in conceptually, that is what it is particularly targeted for the product that we sell, which creates a very interesting combination. What would be your advice, like, to another CI out there about like, hey, just like, this one's easy, right? Just if you just do this right, it's a, it's a quick win, it's not that hard and like it's going to be like maybe a crowd pleaser. What would be your kind of advice about like, you know, one more thing to kind of hit the ground running, trying to get more AI transformation. Can we provide capability for the individual across the organization without the need of a complex heavy IT dependency? There were tools that were appearing in the market, you know what I'm talking about, the ChatGPT, the Geminis, the Microsoft Copilot. So what we had to do is create an engine utilizing obviously the base of the existing products in the market, but wrap it up with a solution that was not only secure but customized to the needs of the company. And we deploy that very quickly. Right now we have approximately 6,000 recurrent users utilizing that engine for individual consumption. That is what I call AI for the masses. And then the more sophisticated use cases, we are tackling two things. One, advanced use cases for growth or opportunity where we're using again advanced mathematics and generative AI for solutions like for example, the sales one that we evolved, but also agentic of where we can complement the work and the repetitive boring work of some of the individuals. And we're tackling the two at the same time. And you start with a small things. One example that's very well known in the industry. We have in information technology a help desk function. And the tier one demand of the help desk is not a very interesting activity to do by a, by a person is recurrent. It's always the same is you just read in the script and you have it. We loaded the script, we put it on top of the help desk and in two weeks we have the engine up and running for our users to consume. Even in the IT organization, we are seeing phenomenal progress in our performance around coding. Right. Someone told me we were in from 5,000 lines of code per month for programmer to like 40,000 lines of code using AI agents, which creates a phenomenal acceleration of software to, to help our, our business. A couple of weeks ago I talked to Adam, your CISO about kind of how they do things in security. It was talking about, oh, it's one project was jumped on the whiteboard. We kind of all work together. You know, how do you think about kind of like setting up the culture of the kind of the technology team more broadly to kind of help drive the innovation. I have this theme about, you know, staying humble but hungry. The double H. Right. You probably heard it before and I Push a lot to this. You know, let's be very respectful, collaborative, but at the same time let's go after it, let's push for it. I also share with my team sometimes, you know, how Italians cook a spaghetti, right. And they say, no, well, you throw it to the wall and if it sticks, it's cooked well. That applies to, sometimes to technology as well. You know, we don't need to be perfect the first time. You know, we just need to try things and see how it works and then push it forward. I think from a, from a business perspective, especially with this generative AI trend, the people at the beginning, you know, thought, hey, this OpenAI, this ChatGPT, this Gemini, they're like big Google, you know, search engines. Right. And that's what people thought at the beginning. And then they started to realize, oh my God, this is much more powerful than that. And I think the biggest challenge that we're going to have is how the organization embraces the power of what appears to be coming and how the IT organizations can help ease that path for success. That translates ultimately to value. What about like maybe on the physical side, you know, maybe we're not there yet, but you can imagine the future us using more AI to control, you know, robots or machines. Right. Especially like an organization like Eastman where you have a hundred plus year history of like safety culture. Right. That requires a different level of trust. Right. And in some ways that trust kind of from kind of the IT leading this initiative is kind of built in the knowledge working world. Right. But eventually we'll kind of go in other places. Yeah. Maybe just, you know, continue on the cultural theme. Like what are some of the ways you kind of weren't kind of building that trust? Yeah. Well, this gets back to the risk scenario. Right. And you need to be situational. We also are very careful, you know, this models because in essence they are a probabilistic model. They can hallucinate, you know, they can create outcomes that they may not be correct based on the data that they are consuming and the time of processing that they have. And they could create errors. Well, if you are creating a coaching opportunity for the sales organization, I think you can take some risk. If you are opening or closing a valve in the manufacturing plan, you better have the answer. Right. Right. And I think we're going to need to be careful where we deploy those models and the level of scrutiny that we have in the environment. So if we are situational with the risk tolerance and how we think about the different aspect, I think we're going to be, we're going to be okay. But this capabilities, if we believe that this is historical moment and less of a hype, they're going to become more powerful. And as they become more powerful, we're going to need to also need to become more careful of where do we have outputs that could influence or change something that we don't want it to change. What do you think is like the most important part about AI that's being like a little bit under discussed. Right. Maybe across, you know, in the media, across your peers. What do you think we can talk more about as an industry? On the hard side, I think there is a lot of energy around the possibilities and I think the the world is missing a little bit this conversation that we talked about of the translation to value part of equation. And in the soft side, which is the change management, there's an assumption that everyone's going to jump into the wagon. And I think more discussion about psychology, human behavior is probably going to become important. So it's again the hard and the soft and in both I think this is going so fast and the discussions are so quick that maybe a little bit more on those sides could be convenient. But it's what comes to on top of my mind on those ones. Do you have any advice for maybe your peers out there, like things that have worked for you or maybe ways people should be thinking about the problem a little bit differently? Yeah. If you think about the adoption of again the organization that's trying to embrace AI and how we're doing, we launched a very strong training program. That was the first thing that we thought about. It combined with a communication plan, combined with a change management plan. But then it's also a little bit the show me story. I think as you get people embedded and trying things in a safe sandbox and they start to create more and more outcome, I think that there's going to be an acceleration of utilization and opportunities. And also I do believe on the guardrails to prevent a risk problem. But I also believe in the decentralization of the AI capabilities in your organization in order to provide a scale and acceleration with the help of everyone. All those thoughts to how to unlock the potential knowing that there's going to be a change curve and an adoption curve that will need to occur. Right. What would be your advice to someone's trying to follow in your footsteps and want to play a bigger role, that want to help transform organizations would be kind of like your coaching or advice or mentorship for them? Well, I think it transcends AI IT gets to any career, particularly information technology. And we talked about the hard and the soft. I think that IT professionals in general are very logical, are very well structured, have a very abstract and tactical view of things, in many cases, not in everything. The communication skills are not very polished. And you talk about the IT nerds, right? And how they work in their own thing. Well, not everyone is like this. Let me clarify that. But there is a little bit of a need to develop technology. Individuals that are also very good communicators. And now it's going to become even more important. Then the problem that I see happening is that the kids in their careers, especially in the IT career or computing science or whatever you call IT analytics, they are getting recognized by their, you know, logical results and performance. And not many times about their communication skills, but one day those same individuals become leaders. And not that they don't know how to talk, but they don't. They are not great communicators to influence, to sell, to convince. So I always tell junior people in the organization, push yourself to develop your communication skills. Go and take a class on theater, loose up, you know, and do something that nature, because it's going to be invaluable in your career to develop those skills that normally at the beginning of your career are not being trained. And in general, the DNA of the technologist is not conducive to the communication angle. It varies by person. Of course. When you think about kind of your leadership team, what are some of the things that you're starting to value more than maybe two years ago versus and less? Yeah, no, I think even at my leadership team or the next level of the leadership team. I was in a town hall yesterday actually. Right. And there was a very strong leader that was talking about her career. And she said, my career grew and 10% of my opportunity came because of my performance, 90% because of my relationships and my network. Wow. You know how important this is. Talking and communicating and interacting. So that for me was a little bit of shock. I don't know if it's 10, 90%, but. But there's a weight that many times we don't think about it. And I tell my leaders, especially those that are trying to influence and push for change in evolving world, you need to be out there. You need to be out there selling and convincing and influencing and challenging and all those good old things. And now to your point on AI, there's also this aspect of interacting with natural language and visuals in an environment that again goes less to the hard and more to the soft. And I think there's also going to be a play even for the more junior people that are going to be using these technologies. Maybe. We'll see. So although what we like to at the end of the show is do have a bit of a lightning round, we basically ask you like four or five questions that are very, you know, hard to answer in kind of like the one tweet format. We're kind of looking for like a shorter answer. I'm going to kick it off with a couple of questions and forgive me advance for making these hard to answer kind of succinctly. Okay, so question one is what is the best way for a CIO to stay up to date on current enterprise AI trends? Or any advice for an incoming CIO about how they stay up to date with the latest technology given, you know, AI is changing every hour, every day. Networking either with your peers or with technology companies that are evolving in the market is key. There's a lot of information in papers, but chatting with people that understand this thing makes a difference for this next one. It doesn't have to be a work thing or an AI thing, but is there a book you've read at some point in your career? It's had a big impact on you and if so, I'd love to hear what it is and why. I always go back to this traditional book of the seven habits of Franklin Covey. You know, it has some fundamentals that not when you read it, you're saying, okay, table stakes, but it's fairly good at least to organize your life and organize your thoughts and the way you're thinking. So what's a kind of upcoming technology you're just like most excited about something you're kind of paying attention to, at least personally. Well, I am just trying to learn here about the evolution of quantum, you know, computing, just because I believe this combination of, call it software from a AI perspective, now expanding to the use of analog aspects of voice or visuals combined with power connected to the quantum. I don't know where it's going to go. People are saying 10 years from now, I don't know. But it's a very interesting thing that I'm reading. Do you think in the future, right, all your software will personalize for Eastman or you actually want it to be kind of more platformatized? So you're kind of taking some of the best practice from some of your peers in the industry. Like where do you think the really will end on that spectrum? If you remember, we talked about this person that was doing 5,000 lines of code, now 40,000 lines of code. So they are developing software, a capability that took us two months to build. We just built it in 10 minutes the other day. So this is real. The, the, the question becomes, you know, how sustainable are going to be those solutions and what is the quality levels, how enterprise, if you want to call it, are going to be ready. And I think there's going to be transition to answer to your question, where this would go or how much is going to be a bunch of agents developing software and the traditional packages start to fall apart and you see the stock market on some of those or there's going to be a play or platforms to build are going to become more important. Even I don't have a clue, to be honest. But I see the trends coming and I ask my team, let's experiment, let's start to build capabilities using AI agents. But if there is a critical piece of enterprise software, don't jump the wagon too quickly. We need to also make sure that they are sustainable, high quality. So again, being situational on these decisions while all this evolves, I think is in my opinion the wise thing to do. Well, I appreciate you joining the show, Aldo. Hopefully get a chance to speak again soon. And thank you so much for joining us. Hey, thank you very much, Ava. I'm you thank for everything and I'm glad that it was a really good conversation. Thank you very much. That was Aldo Noceda, CIO at Eastman. Thanks for listening to Enterprise AI Innovators. I'm Salmo Tamity, a general partner at Greylock Partners. And I'm Evan Reiser, the founder and CEO of Abnormal AI. Please be sure to subscribe so you never miss an episode. Learn more about enterprise AI transformation at enterprisesoftware.biz blog. This show is produced by Abnormal Studios. We'll see you next time.