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The Crude Truth Ep. 129 Caroline Roche, Energy Industry Leader

Video Transcription edited for grammar. We disavow any errors unless they make us look better or smarter.

Rey “RT” Treviño III [00:00:00] The opportunity for energy and resource companies to create a wonderful AI opportunity and cohesiveness. We talk to the global industry experts on this episode of The Crude Truth.

Narrator [00:00:20] In 1901, at Spindletop Hill near Beaumont, the future of Texas changed dramatically, as, like a fountain of fortune, thousands of barrels of oil burst from the earth towards the sky. Soon Detroit would be cranking out Model Ts by the millions, and America was on the move, thanks to the black gold being produced in Texas. Now, more than a century later, the vehicles are different, but nothing else has truly changed. Sure there may be many other alternative energy sources like wind and solar and electric. But let’s be honest, America depends on oil and entrepreneurs, and if the USA is truly going to be independent, it has to know The Crude Truth.

Narrator [00:01:03] This episode is brought to you by LFS Chemistry. We are committed to being good stewards of the environment. We are providing the tools so you can be too. Nape Expo, where deals happen. Air Compressor Solutions. When everything is on the line, Air Compressed Solutions is the dependable choice to keep commercial business powered up. Sandstone Group. Exec Crue. Elevate your network, elevate your knowledge. Texas Star Alliance, Pecos Country Operating, fueling our future.

Rey “RT” Treviño III [00:01:38] Well, thank you again and thank you as always for tuning in to another episode of The Crude Truth. Today we are just blazing into 2025 and we cannot believe that it’s even the year 2025. It seems like yesterday it was 2000. I know. Oh my gosh. And here we are today. And my guest today is somebody that is a actual… You don’t get no better than this industry leader. This is somebody that has seen it all, somebody that is doing it all. Somebody that is truly creating it all Today, our guest is IBM’s global communications lead in the industry, in the energy industry, excuse me. So again, she is the IBM’s Global Communications Lead for the energy Industry, Caroline Roche. Caroline, how are you?

Caroline Roche [00:02:33] I’m great. Thank you so much for having me.

Rey “RT” Treviño III [00:02:35] Oh my gosh, thank you. And I’m, I am just tongue-tied as can be. And just so excited though, is really to have you guys here today. As my teaser said, and as I’ve already said again, I think now you guys are leading the way. I have on amazing individuals, trailblazers, but IBM, AI, in the studio, on The Crude Truth, Caroline Roche, wow, how are you doing?

Caroline Roche [00:03:04] I’m great, love being here with you in Dallas today.

Rey “RT” Treviño III [00:03:06] Oh my gosh. Well, Caroline, for those out there that don’t know who you are and why I am just geeking out, like there’s no one who are, please tell people who you.

Caroline Roche [00:03:15] Sure. My name is Caroline Roche and I lead the energy industry for IBM Consulting in the US. And it’s truly never been a more exciting time to be in the energy industry. I joined IBM 15 years ago thinking I would be here two years and go do something else. And I’ve just been, I’ve loved the, the only constant has been technology change. So there are always new and exciting problems to solve, but IBM has really transformed over the past several years ourselves, really starting in 2010, we started AI with Watson on Jeopardy, as you may remember. That’s right. So we’ve been in the AI business for a long time and never a better time to be one of the oldest hats in the in the business. Actually, IBM, a fun fact is we started AI in the 1960s at an MIT conference. So we really been in this business for long time and Now AI is all you hear about, but I think that that’s really allowed us to have a very mature perspective and offering in terms of really driving business outcomes with AI. So really exciting time to be in this industry and in this role.

Rey “RT” Treviño III [00:04:24] Well, you know, and with you being in this role and you who would have ever thought that even 2010 was a long time ago, 15 years, and I completely forgot about Watson until right now. I remember those Watson commercials now. And 15 years in today’s world and technology is a lifetime. Yes. So I can only imagine again what you guys have been doing in those R&D departments. Can we? No, we won’t go into the conspiracy theories or anything, but I can only imagine what y’all been working on in the R& D.

Caroline Roche [00:04:56] IBM has an organization called IBM Research that does really incredible things in terms of right now they’re working on solving quantum computing and has really been investing a ton in generative AI and foundation models. One of the things that I get so excited about is there’s very cool stuff you can with AI analytics automation data now. But I think in the next five years, you’ll see AI helping to solve some of these big problems that we’ve never been able to solve before, like using weather and better predicting weather for business operations more effectively. So IBM research is working on that. And it’s amazing when you deep dive on it. Like for example, I was sharing with you before the show that I lead utilities, oil and gas and chemicals. And we combined that several years ago because of the convergence in the industry. But IBM Research is working on foundation models for discovering new molecules. And when you hear that, you’re like, holy smokes. It’s kind of unbelievable the things we can do with technology and how it will power the business transformation over the next couple years.

Rey “RT” Treviño III [00:06:13] You know, you talk about how you guys combined the utilities, oil and gas all into one group. And that’s something that you guys are looking at right now. And in one of y’all’s papers, and actually in a lot of yall’s paper, y’alls call it an energy transition. I’d like to say it’s more of an energy addition. But what you guys doing is y’yall are looking at this energy need that we’re only gonna need. And y’aall are actually doing something and measuring it. And I find that. So smart because yeah, we know that we produce here in America, and this is just on the only gas side, we produce 14 million barrels of oil a day. We use 21 million barrels in oil a day. Okay. Well, how do we measure that in the sense of… Consumption use in the form of well, how do we do other things or our transition or our ad to to move, you know At the time that we record this we talk about the chemical side Which again as we talked about in the pre-meeting, I think it’s great because that you labeled it that because it’s Hydrocarbons, that’s what it is. It’s a chemical. You know the chemical compounds And chris wright the other day on on one of the media outlets said hey in 1970 something hydrocarbons were 85% of the industry energy. Today, they’re still 80%. So we haven’t done that. So by you guys taking it in and measuring it out, what are you all really looking to do and make that better as far as this energy tradition goes?

Caroline Roche [00:07:45] So, here’s what I’d say on that. Interestingly, IBM is an AI and hybrid cloud company. We believe that companies will use multiple clouds and on-premise software. I actually think it’s very similar for generation that it will be a hybrid generation or a hybrid energy source model because as you look at things like… Nuclear and other things, those are huge opportunities, but they’re gonna take time to build. And so there will need to be other sources of energy in the interim. Sam Altman said something that I think was super interesting. He, of course, is the founder of OpenAI. And one of the things he said is, the pace of our world progress will be defined by technology and the cost of energy. And I think that that is so true and such an exciting thing. As you think about, I think one of the stats, EPRI put out a stat that from 1975 to 2003, the CAGR for energy consumption was 2.5%. From 2003 to 2023, the CAgr was 0.15%. And from 2023 to 2038, it’s expected to be between seven and 15%. So we’re going from a pace of growth in this industry of basically 0% to up to 15%. And that’s gonna require a lot of change and transformation in terms of new construction, new connections, new sources of energy, businesses figuring out how to create energy and having their own natural gas generation and then also connecting to the grid. I think we’re seeing a lot of creativity in sources of energy and then also thinking about how do you keep up with this piece of growth in this industry. So it’s very exciting.

Rey “RT” Treviño III [00:09:36] You know, I want to dive into all of that literally want to start with keger for those out there. They’re like, what’s a keger?

Caroline Roche [00:09:42] Compound annual growth rate. So it measures the growth of things, I guess.

Rey “RT” Treviño III [00:09:50] Well, and then I like how you brought it up because when I tell people, and please correct me if I’m wrong, because I use this, so I put it out there in the world, that whenever somebody does a Google search, and let’s just say, will you please tell me what a dog is in a Google Search? And then if you did that in, can we mention the word ChatGPT? And then, if you put the same thing in ChatG, the same exact thing, please tell what a dog is. That it uses 10 times the energy for chat GPT to do what it does when you hit enter versus what Google does.

Caroline Roche [00:10:28] Yeah. And so I think, you know, it was interesting when, um, deep seek came out with, uh, their kind of more affordable foundation models, our CEO, Arvind Krishna really said at the time, this is what we’ve been saying all along that we believe you don’t need if you’re going to be doing, you know, asset optimization. You don’t need a foundation model or a large language model that can write poetry. You need a foundation model that can do asset optimization And so I think we have really been, from an IBM technology perspective, building smaller and purpose-built, specifically trained models. And why does that matter? Well, they cost less and they use less energy. And as you think about, again, this growth rate of expected energy demand. Are the ability to generate that energy, whether that’s through drilling or wind or coal or whatever, we’re constrained. And then the ability to manage that on the grid from a utility perspective. And so looking at how can we be more purpose-built and intentional about the technology we’re using, inclusive of AI, I think is very important.

Rey “RT” Treviño III [00:11:46] When I have, when I get to talk to individuals and even it solidifies its day with you guys as excited as you are about AI and where we’re going, I saw a funny meme the other day and it said something along the lines of, hey, you know what, I graduated college without a smartphone or AI, so tell me how I am and then when I’m hearing the true industry experts, I mean, again, I can’t get no better. You know, somebody tell me that she’s wrong today from IBM, please. I challenge somebody that’s like, look what AI is going to do when again, you’re somebody that did all these things before AI and you’re like, look how it’s going to change things. I want to make a kind of a funny point, but also the dive into it is that you mentioned the weather and the weather predicting and getting better because, you know, even in our line of work in oil and gas, the supply chain and logistics with weather can be effective, Totally. Well, I’d like to think that if we can predict the weather 100%. First of all, that means we got meteorologists out of jobs. So hopefully we can then get world peace going, right? But that is just so huge in the sense that look what you’re doing as far as getting more data and getting more efficient.

Caroline Roche [00:13:06] Totally.

Rey “RT” Treviño III [00:13:06] Because you said it yourself that, or the quote that that gentleman said about, hey, and how cost effective energy is going to be in the future.

Caroline Roche [00:13:18] Well, and I think to, you know, fundamentally, the meme about I graduated college without smartphones and AI. Well, I think it’s not about that so much as you think about all the things that like, are total time sucks, that things you don’t want to be doing. Who wants to call a call center and sit on hold for 30 minutes? Not me, right? And so you think about some of those things. And what what would you do with those 30 minutes. If you hadn’t sat on hold, if you haven’t been constrained. Who has had an appointment at their home where they said the window is eight to six? And you’re like, well, I have a job, like what do you mean eight to sex? So you think about some of those things and you think how can you be more efficient, more precise, how can not do admin stuff but really spend your time. Thinking and growing and being more strategic from a relationship perspective. And so I think that that’s really what’s exciting. Like one of the examples that I was giving someone earlier was you think about drone technology and the ability to use drones, to look at facilities and equipment and manage them. That right now we’re constrained. I mean, even thinking about 10 years ago, using drones to fly out over a rig or look at a drill or manage a grid, that was like, you know. Art of the future, and so now we have that, but now we’re constrained by the ability for someone to look at the video footage and then take action. And so I think that there’s a lot of opportunity to drive more automation in that, use AI to review the video, to train the footage. But you mentioned, I think IBM fundamentally believes it won’t be. Replacing humans but augmenting humans in terms of making them better and how they do their jobs and be able to make better data-based decisions.

Rey “RT” Treviño III [00:15:16] Yeah, let’s talk about that augmented humans there. Just for a brief sec for listeners out there, like, well, no, they’re gonna put a chip in us. No, that is not what you mean. No, no. Just making it more efficient.

Caroline Roche [00:15:27] Yeah, giving us better data to make better decisions, I think is really what it is.

Rey “RT” Treviño III [00:15:33] Here I am. We’re making that joke and I’m now working on my masters as I told you. And AI has been there. And like you said, you can do several things a lot more quickly just with what we have to the public. And so talking about what we had with the public, I want to talk about something else that you mentioned, the grid. In our post-production meeting, when we were talking, I mentioned, hey, when President Trump says drill, baby drill, which here in Texas is not worried about it. But really what he means is build baby build and it’s about the infrastructure when people are like, well, we’re going to switch over everything and again, I know we’re not trying to talk politics or anything like that, but people are like, oh, we are going to go to wind and solar tomorrow. It’s like, yeah, that’s great. It’s still going to take us a hundred years to update and transform that infrastructure to handle only wind and solar if we flip the switch tomorrow. So I wanna talk about the infrastructure with you because we know it’s old, we gotta get it updated. And how do you see AI helping out with this addition of the infrastructure?

Caroline Roche [00:16:38] Totally, so I would say we’re seeing companies focus on three things. One is the first and major area is really looking at how to have more resilient energy operations and more resilient companies. And so that’s where we see manufacturing companies looking at having, there was a term on the utility-centered business for a while that for a While I never thought it would come to be but prosumer, producers and consumers of energy. So you’re looking at, you know, there was the power outage at Heathrow, but really I think you’re seeing people being more who are totally dependent on energy for their business, whether that’s manufacturing or hospitals or whatever, being producers of energy as well, and then consumers. And then you see people looking at, okay, from a resiliency perspective, there’s the, it moves into operational excellence. So as we’re producing and consuming energy, when do I want to produce based on the cost of energy? And when do want to consume from the grid? And so I think we’re seeing a little bit investment in being able to both produce and consume. To drive operational efficiency. And then you were talking about, again, I think it will be a multi-generation source approach. And so how do you have the technology to understand what do I use when and how do I do that in a cost efficient way? And so I think all of those things are important because you said build, baby, build. And I think really the need that we all have is for a lot of investment in maturing our energy infrastructure. But how do do that? While also managing your current energy infrastructure. And so what you need to do is be more efficient operationally so you can dedicate resources to building and new growth and construction. And so I think a lot of investment in operations to be more automated and efficient, not to lose jobs, but to be able to dedicate those resources to those people, those humans, the cost associated with it in terms of growth.

Rey “RT” Treviño III [00:18:48] You know, I’m glad you mentioned resource companies in there, because even in my teaser, I separated energy and resource companies. And a lot of people really in the industry just think they’re all one in general. So we won’t go into the details about how you got like actually a production company and all this and but there is a difference in the two. And the way that AI and correct me if I’m wrong is taking the energy whether it is oil. Coal, wind, solar, geothermal, nuclear, excuse me, that’s the energy and then the resource company and we are doing our best, is that correct, that I’ll be doing their best to combine the two to flow more efficiently.

Caroline Roche [00:19:30] Totally. So I think we’re seeing a lot A, we’re lot of convergence across the energy of traditional utilities being in energy generation and building things like wind and solar and other things like that. We’re also seeing oil and gas companies do that as well and diversifying their energy mix. We’re seeing oil and gas companies having chemical and loose products. seeing chemicals companies do that. So, I think we’re really seeing a of convergence in terms of. Where the energy, what the energy industry is doing and how they’re doing it is really coming together. In terms of what IBM is doing in this space, of course we have IBM technology where we have a lot of technologies from an AI automation. We have Maximo, which does a lot of work in asset management perspective. And then the traditional like. Technology enablement in terms of hybrid cloud. Red Hat is a key technology we have that manages multi-cloud environments or hybrid cloud environments. So we have a whole technology portfolio enabling this. I am also on the consulting side. And so we implement IBM technology and also other partner technologies like. SAP, Oracle, Salesforce, Adobe, we partner very closely with AWS and Azure. So really looking at how do we look at all of this technology enabling operations and how do help our clients be more efficient so that they can continue to invest in the growth in this industry.

Rey “RT” Treviño III [00:21:05] You know, I just wanted to highlight something there. The way you mentioned AWS and all these other groups that y’all are collaborating with. And collaboration is so important. You can’t isolate yourself no matter what industry. I just want to make that point real quick because a lot of people are always like, oh no, I got to be here. You know I want to ask you exactly where you think AI is going to go from an IBM standpoint. Like, so you’ve been there since 2010. You’ve been their 15 years. Watson’s been around for 15 years, interesting. Correlation, is there a correlation there? I’m wondering, and so as a whole, I kind of want to switch it up a little bit, but and maybe ask you some random rapid fires, maybe. Just about AI, but where do you see IBM and AI going here? Like in 15 years? Because when I ask you that question, my assumption is, so we just got done with 15 years. So with way technology works, that means the next 15 years is almost going to be like 30 years. Oh, yeah. So where do you see that going?

Caroline Roche [00:22:09] So I would say if you take a step back and you think about 2010 to 2015, it was a little bit of like, okay, wow, this technology is possible. But people were kind of dipping their toes in the water of what does this mean to me? What is the investment I need to be making in it? Is it real? Is it really for business? And what does it look like? I think over the past maybe five to eight years, we’ve seen people really drive a lot of efficiency and optimization within a single process. So you think about something like, the call center or you think about something like finance or you about something field operations, you see those processes have been really transformed by both kind of platform-based technology, whether that’s your SAPs or your Maximos, as well as centralizing data together and starting to use that data in a more efficient way. So I think we’ve seen a lot of progress and investment in driving more efficiency from that perspective. I think the next couple years will be connecting the dots. So not just efficiency within your call center, not just having AI listening to calls and augmenting agents, but then making sure that the call center knows what the field is doing. Or making sure that your finance decisions are based on weather and your resource load in the field. So really kind of connecting business functions and making better decisions based on the connection of data between those business functions. And then I think, you know, five, 10 years from now, like I’ve become a little bit obsessed with the promise of solving some of these bigger rock challenges that we thought we’d never be able to solve. Weather, I think is a great one because I think we all. You know, experience a weather forecast that was wrong, and one small thing really changed the full weather forecast. And it’s not that AI is going to be more accurate than the physics-based models today. But instead of running five scenarios that you can do in a physics- based model, with foundation models, which are here, you may be able to run 1,000 scenarios with quantum computing. You may be able to run a million scenarios. So what does that mean? That means in a weather event, you may able to better predict where damage will be. You may be, or when you’re thinking about where you put a wind turbine, you may better predict the exact location that will have optimal wind. So I think that there are a lot of things that we’ll be able to solve that based on the compute power and based on cost we haven’t been able to solve yet today and so I think that that’s the really exciting promise of the future.

Rey “RT” Treviño III [00:25:01] You know, I want to talk about the human error versus the, I’ll just call it computer error, error. Couple of, we’re getting ready to make a transition to a new modern software. And I was sitting there talking to my father and as a lot of people know, and I mentioned, hey, he’s got that software background. And he immediately goes, because I was like, hey, we are going to use this list. And I said, we’ll type it all in if we have to. Like, that’s what I’m saying. I don’t care. We’ll type it all in, he’s like… If we, he used some fancy computer word of transferring it over, he goes, but if we have the computers basically transferred over, that eliminates the human error. And I was like, oh, okay. And so how are we able to continue to do that with AI to where AI will be able to not have that computer error? Like what are we doing there? Cause I understand about the AI, but how do we do that?

Caroline Roche [00:26:01] Well, I would say a couple of things. One is, you know, there’s AI overall and generative AI is one portion of it. Historically, AI, pre-generative AI, historically AI was really good at math-based tasks. So I have this number of things and I have number of this things and I wanna predict that. That is kind of traditional AI analytics that we’ve been able to do. What was really groundbreaking in generative AI and particularly large language models is historically AI was not really good at reading things and understanding context. And I think, again, part of the exciting moment we’re in from a technology perspective is when you can blend reading a document or reading a manual, understanding that model and then combine it with predictive AI. And so being able to say, okay, I understand how I’m supposed to maintain something based on reading the instruction manual. And I understand based on my models where I think that there’s going to be damage. And so that I can say, okay, if I have person A, B, and C, who has the right skills for this? Based on where they are and get the right technician to the right place at the right time. And I think we’re at a moment where we can do that. Now you were asking about human error. I think a big part of that, obviously, there’s the discussion around drift in the models, because the models are learning themselves. And a big part of it is being able to structure the prompts. And so we talk about prompt engineering as a thing. And I think it’s kind of going to be a new skill, just like using a smartphone was 15 years ago in terms of how do you use this for business. And so I think prompt engineering and knowing what you’re asking the AI to do is going to be a whole new skill that many people need to learn how to do. So anyway, I think some of that will be what you are asking the Ai to do and how you’re training the AI will be a key part of that.

Rey “RT” Treviño III [00:28:10] I kind of want to totally now I want to just kind of go into something totally different for a minute here. What kind of like, this is just kind of, again, with how you are doing things and at IBM and in the role that you are, are you reading any good books like right now? What are you, what are you writing?

Caroline Roche [00:28:31] So my goal last year was to read 24 books, I did 26. This year, my goal is 30. I’m at six right now. I kind of rotate between like spy thrillers and business books. So some of my favorite books in past year, I read the geek way. Okay, which is a book about the, you know, super seven or whatever the technology companies and how they’ve changed how they run and operate their companies using data, using flatter organization structures, encouraging people to challenge things. That was a really great book. The other one that I loved was shoe dog by Phil Knight. So the story of Nike and how he transformed that industry. Much like books, I love podcasts. I travel a lot for work, and so I listen to podcasts a lot. And my favorite podcast that I’ve been listening to is Acquired, which deep dives on different companies. So I’m listening to the Ikea episode right now. I just listen to Costco, Meta, Microsoft. So those are some of the favorite things that I’m reading and listening to right now

Rey “RT” Treviño III [00:29:49] Um, if you were going to use an AI right now for something, what are you going to

Caroline Roche [00:29:53] IBM Watson.

Rey “RT” Treviño III [00:29:55] But do we have all like, okay, the public stuff, the free stuff.

Caroline Roche [00:30:00] I would say I kind of rotate between them.

Rey “RT” Treviño III [00:30:04] What’s out there?

Caroline Roche [00:30:05] Chat GPT, of course, Chad, right? Okay.

Rey “RT” Treviño III [00:30:08] Okay, that’s the only two I know of.

Caroline Roche [00:30:09] And Google has Gemini. So I kind of rotate between them and try out different ones. IBM consulting created an IBM consulting assistant where you can leverage some of the different models. So I actually use that almost every day for work. I’m a wordy writer. So I’ve created an AI agent that helps my emails be more concise that is trained on my tone. So I actually use it actually every day and in it I also use it to read proposals or white papers and summarize them for me. So I’m actually using it quite a lot.

Rey “RT” Treviño III [00:30:46] I want you to know I did not use AI because I thought about it to read the paper that Ken shared with me to talk about this. I didn’t do it. I’m like, no, no. I said, I’m going to read this thing. This is the IBM Communications Lead. So IBM has an Institute of Business Value that does…

Caroline Roche [00:31:00] Does research. And so we just put out some papers that we sent you before the show. So anyway.

Rey “RT” Treviño III [00:31:05] Yeah, and I want to die not I mean, um, so the IBM one, I think the crude truth needs to get an access to that and get a shot is what I think that would be really cool. Yeah. Um, let’s see. I’m trying to think like I just because so fascinating with what y’all are doing. And I want talk about the environmental impact, is that okay? And I feel like it all ties in and I’m trying to think of the way to do it because I feel. There’s a lot, you know, kind of like we didn’t talk about what we do and don’t want to talk, but What impact do you see AI having in a positive way and is there a negative?

Caroline Roche [00:31:47] Hmm, that’s an interesting question. Well, I would say I think from a positive way perspective, truly I believe kind of the possibilities are endless in terms of the ability to become more efficient with AI. And so we’ve talked about some of those examples today and I think ultimately efficiency is going to drive good outcomes in terms the environment. Again, like I think about, for example, being more efficient in crew, crew routing and optimization, of course means that there’s less repeat and waste, both from a like time spent perspective, but also in terms of carbon emissions, in terms getting to the right place at the right time, et cetera. So I think that that is exciting. In terms of a negative thing. I think one of the things that the industry is going to have to work on is with the CAGR, as I was calling it, with this growth of seven to 15%, there’s gonna be a lot more energy used. And so thinking about, again, how do we tackle that problem? And I think what I was saying earlier in terms of using more of these fit for a purpose, specifically trained models that are cheaper and more energy efficient is going to be a key piece of it. So. You know, I think really the industry has been doing a great job of that. If you look at what I said, I think 2003 to 2023, the growth rate was zero. And I think that that’s because a lot of these technologies have been becoming more energy efficient. I think we have another wave of that in terms of thinking about how do we tackle this huge growth in demand and also have positive impacts from a environment perspective.

Rey “RT” Treviño III [00:33:36] You know, when you talk about it, it’s got to be reliable, abundant, and cost effective.

Caroline Roche [00:33:43] Totally.

Rey “RT” Treviño III [00:33:45] And at the end of the day, that’s where we’ve got to be on everything. I mean, we are, that is our energy source. That’s what, you know, oil and gas really a lot of people see as. And we’re leaning on AI to do the same thing. Yeah. And I think, again, that correlation is there. Caroline I cannot think you know, how can people go out there and other than me sending out thousands of emails How can people get with the see those papers work? Can they see that sure if they’re interested in the IBM? Wow if they are interested in IBM AI information and being able to get that how can they get all that so

Caroline Roche [00:34:23] So of course, IBM.com is a great source of that. I mentioned the Institute of Business Value. We do research on a lot of different topics, but in particular around Business Transformation with AI and Hybrid Cloud, Cybersecurity. We just released a paper on the effects of grid modernization. So you can find that on our website. And again, it’s called the Institute of Business Value. And finding me, you can me on LinkedIn. So that’s probably the easiest place to find me, but also probably iobam.com as well.

Rey “RT” Treviño III [00:34:59] Again, as we’re doing the recording, I want to kind of leave you, let you have the final word today because again, we’ve talked about the AI, but everybody out there that are anti-AI or something like that, how would we leave on a positive note just to talk about AI?

Caroline Roche [00:35:19] I would say, again, as I reflect on why am I excited about what I do for work and where I am, I think that there’s never been a cooler time in terms of working in technology and working in energy because I think really the pace of progress is going to be around how do we deliver and enable more energy to more people and more businesses at the right time. AI, I think, is going to be a huge source of cost efficiency. Then also, again, I think it’s really all about using data to make better decisions in ways that we haven’t been able to do. I think that that’s really the promise of AI is it’s going to power progress and enable people to make better decisions more quickly and more accurately.

Rey “RT” Treviño III [00:36:06] And that’s The Crude Truth. Caroline, I cannot thank you enough for this opportunity. Thank you to your team for even getting in contact with us. I want to have you all come on because even the grid modernization, that we got to talk about it. I think every… There’s so much more that we could unpack. And I’d like to maybe try to discuss that off air. But again, I just cannot thank you enough for this time. Everybody, it’s coming. We need to learn how to work with it, keep it safe, all those good things. So to everybody out there that’s already using AI, if you have any AI questions, I tried to ask what’s the best public one to use. We’ve got great books. The Geek Note.

Caroline Roche [00:36:53] The geek way.

Rey “RT” Treviño III [00:36:54] The Geek Way and Shoe Dog, I’ll be reading those books. And so to everybody out there, just thank you again for always tuning in, and we’ll see you again on another episode of The Crude Truth.

Narrator [00:37:06] Again, The Crude Truth would like to thank today’s sponsors, LFS Chemistry, NAPE Expo, Air Compressor Solutions, Sandstone Group, Exec Crue, Texas Star Alliance, Pecos Country Operating, and Real News Communication Network.

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