Kate Johnson, President and CEO, Lumen Technologies
Vimal Kapur, Chairman and CEO, Honeywell
In conversation with: Andrew Nusca, Fortune

Fortune Global Forum 2024: AI At Work - From Promise to Productivity

Photograph by Michael O’Shea for Fortune

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AI At Work: From Promise to Productivity

So artificial intelligence, I'm just going to stop there. No, I'm just kidding. We've not seen things like this before. Things that grow this fast that usher in change this dramatic. And I have this theory that for the last couple of years, we've been looking at A I as the thing to solve all of our problems. But right now, this is the time where the rubber hits the road. This is the time when things get messy. And to me the most interesting. So what I'd like to know first from you, Kate is what are you concerned about vis A vis the constraints to all that growth from here on it. So I, I think it's a multi variable equation, right? We're, we're networking company. Uh We run a big portion of the world's internet traffic on our, on our fiber. And uh A I needs data, data, needs data centers, data centers need to be connected, right? And so, you know GP U SI just spoke with a uh C level at, at one of the hypercars. And he said, you know, Kate, we used to measure margin return on every extra engineer we deploy, that's not how we think about it anymore. We measure the return on every GP U that we put in place. So the actual data center compute required for A I. And uh you know, there's, there's constraint around that um building out these data centers, there's constraints around power space uh and water for cooling. Um You know, we, we are connecting those data centers and uh we are not the long pole in the tent, but I think the real long pull in this in this build out is really uh human adoption, right? How do I use this? There's a lot of fear in the system. Um You know, there's, there's a lot of confusion as to what great looks like. It's a great uncertain, you know, terrain ahead. And so I think there are a couple of different things that are making us all think a little bit harder than usual. I would say that for sure. Maybe A I can think for us. OK, Vimo, what are you concerned about with this big, great big build out? I think I'm gonna pick up from what Kate said the adoption. But when I think from a industrial company perspective, so honey will serve three big segment, aerospace energy and buildings. I'm representing those customers. I think three things got thrown at them over the last five years, uh five G cloud and now A I and you can throw a lot of technology. But the question is what do you want to do with that? And I think for the last one year, it's clear that these three comes together trifecta and build a path of autonomy for these industrial sector. And what autonomy means is really solving the two fundamental issues which these companies are facing. First is a skill shortage, they don't have people to run their operations. Uh And second is how to maximize asset utilization, which is need for every industrial asset. For example, I always give is think of a plane. So the problem they would like to solve is can I have one pilot instead of two, forget the FAA certification for a minute. But ideal speaking, pilot shortage is a real issue plan in the whole world. And then of course, can I run plane as long as I want? So those are the two issues to solve the combination of A I, the data availability through five G and ability to do analytics over the cloud. When they all come together the trifecta of it, it becomes pathway to autonomy for an aerospace company, for an industrial plant, for a building. And that's what is really exciting thing about it that how do you unlock the capability of these technologies and the use? Will people use it? Will they adopt it? Are they going to worry about it? But the good news is that these issues are real and in context of industrial, these are revenue maximization strategies. These are not cost savings. And so I think people are excited and they want to proceed with that. You know, I'm, I'm glad you said that because look, the things you are both talking about are very concrete things we like that. Business likes certainty, right? But A I has been so open ended. It's been difficult to justify. There's a general feeling that we need to have it. Uh uh but to at what cost really? And we've seen the stocks of the big tech companies recently get traded down quite a bit because of extraordinary investments in all things. A I and maybe some concern that uh that it will never pay off. And so then what do you think of all that? You've just outlined a bunch of things that sound like just easily justified? But is it tough? I would say the, every time when you uh relate to us a tangible solution, then the adoption always go high. The, the, you know, I've been working for almost four decades now, when technology is chasing to solve a problem without knowing what the problem is, it doesn't scale. But when you invert it to say we are going to solve this problem and technology happens to be a means and not the end, then the adoption happens better. So I think we all have learned from our old uh uh you know, experiences and I'll give an example of cloud and we, uh you know, we started in 10 years back to say that we'll adopt cloud to do engineering for our complex projects. They're very global. People are spread all over the world. In 2015, there was not many customers who are willing to say it's a good idea. So what is this cloud thing? And what's this virtualization? And we said we'll adopt it ourselves because Honeywell is a scale and we demonstrated that it creates value and then everybody came together. So I think it's always where is the value and when the value comes in adoption always happens. I I'd like to add to that. There's there's this notion of start ups, right? And if there is just a new technology company, fill in the blank, any technology, any name of the company, we would allow that technology company to go through AJ curve, spend some money to eventually get the return. We're kind of skipping that step here. And I don't necessarily think that's the right thing to do because I think there are a couple of things at play. The first is that uh the the hyper scalar cloud companies, you know, social platforms, the developer of these models, they are seeing this enormous capability and opportunity and they're building out critical infrastructure ahead, you know, at the beginning of the J curve, that's what's happening right now. Why did they do that? Because they're seeing the enormous impact that large language models can have on uh the acceleration of productivity certainly at the individual level. And so the hype cycle is kind of around the the productivity boost that, that a worker or a human gets from an A I model in everyday tasks and activities. I think the return really comes when large corporations start to really understand how to change process and business outcomes. But that takes time and it takes focus and it takes intention. Uh and I think that's where we are right now. So I, I, before the naysayers get too excited, I would just go back to the history books and say fastest diffusion of any technology in the history of mankind mini J curve coming our way. So what would you offer the executives in this room who are searching and hunting for that? Roy who are under the gun from their CFO S and all, you know, maybe we attach it to certain clear use cases and you know, what's the best way forward here? Keep the timeline short. And I, I think it's really about intentionality. So the first thing and I don't know about everybody in the audience, but I got pretty excited about A I and let everybody play and then I looked at my bill and I said, ok, let's maybe not play so much. And let's be intentional about the, you know, 5 to 7 things that we know can really make a difference. That's individual worker. I think the next step is functional, how can I improve sales productivity? How can I improve uh the customer experience in in support and service? Those are, those are no brainer places to deploy this kind of technology for instantaneous return. I think when you put all of those you know, functions of the future finance of the future operation of the future sales of the future together, you you have a a new construct of a company and that's probably where the thinking should be. But from the perspective of I need an A I strategy, it needs to be intentional and I need the infrastructure like like a new network to support it. That's the work that needs to begin today. I think what I'm going to add to Kate's point is that uh the collaboration between the ecosystem is critical, we should not repeat and learn what others have already figured out. For example, Honeywell knows the domain of its customer but not necessarily best in class and large language model. So why should we try to be expert in that and let it be left to tech companies like Google or Amazon or Microsoft. But we know what we know and similarly take it to the customers who can adopt it. So one of the critical success factor is if we want to be success, be successful faster, we have to collaborate, which hasn't happened in the past because failure was more passed on to somebody else. Like you take the risk, I'm not going to take the risk. So if you want to have a high return, the risk has to be shared. It means co creation has to be done. We have to take a risk of sharing our capability with somebody else. The risk of maybe losing it and customers have to take a risk of adopting it to a certain degree if there's a trust on that. Now you said a word that I'm gonna ask a question about in a second. But I just wanna remind the audience we are going to have room for audience questions. So start thinking about it and I'll come to you in a minute. Failure. Uh We love that word in technology, don't we? Um You know, a I there's concerns for individuals, for employees are writing so very high about artificial intelligence. The surveys are out there. The anxieties are real. I think everybody has different anxieties about it in different directions, but it's all anxiety is my point. No one's calm, certainly not the CFO. So um how do you manage failure? You don't want to give a I a bad name, right? But you clearly have to try some things to fail. Let them play as you said, right? So changes in today's word if you're not willing to change, I think you're living literally in the past. Uh The adoption of the new technology is uh is a, is a new normal if you resist it, I'm sure it's gonna come and get you sooner or later. And A I is just one of it. I mean, I'll remind people there was robotic process automation five years back and you go five years back, there was virtualization. So this is, this is gonna keep coming. And the trick is companies have to grow so that we need more resources, but that gets offset by productivity which is going to create. So I think that's a virtuous cycle. One has to think about it. If you are scared, it's a doomsday for everybody. It's not create innovation. We just keep scared about it and nothing is gonna happen. I, I think your point is spot on. Uh and I, I think to your, to your point, Phil, it's um more than ever you have to create a culture that embraces this change that in, that is vulnerable and agile and oriented around growth and learning very different than traditional, but more important than ever because I think A I introduces an uncertainty like we've never seen into the workplace onto the global stage. And uh you just have to be prepared for anything and that's terrifying unless you have the comrades, you know, going into it with you. And I think that's, that's certainly the culture that we're building and we're seeing companies that are most successful in this trial period are, are the ones that have that kind of culture. And I can't say that we've seen it um to the degree that the technology itself, what we're gonna get from it is so uncertain, let alone all of the dynamics around that. All right, let's go to the audience for questions. We've got one right over here. If you could just wait for the microphone, please. Thank you, your name and who you're with when you get it. Thank you. Thank you. Good afternoon. My name is Ashton Jackson. I'm a reporter at C NBC. Uh My question for you is in such a fast evolving field. What skills or qualities do you believe employees should develop to work effectively on alongside A I technologies? Especially now when people are so worried about job displacement, I mean, from my perspective, it's this notion of trying to get it right? Um Instead of being right, you know, a lot of people are focusing on having A I answer the question for them rather than figuring out. OK, what can I have this do for? And with me and that shift in mindset is it's, it's probably the number one thing is I'm not gonna show up every, every day to try and be right. I'm gonna show up and build an engine to get it right. And if I'm a customer obsessed, that's a very powerful combination. I would say, I mean, I look at it that we all have to think about elevating our capability to one level up. Everybody has to think how to be a manager because you don't need a lot of work, which was done. The front line is gonna get automated or is getting automated. So how to manage work has to be a skill you have to learn because you may not get the opportunity to do that with your own hand. And that's a different paradigm because we were lucky to do a lot of work with our hand as we grew up and probably the current next generation may not get it. But it doesn't mean they don't have work opportunity, they have to do it just differently and adopting that skill and thinking about it is I think is key to their success. Yeah, that was a great question. Let's see. Is there any other? Forgive me? The light is quite bright. Oh, yes. Right here in the front, please. Thank you. I'm a Attorney general of Financial. I had a question for both of you. How your companies are dealing with the integration potentially of public data, large language models in your internal data and how you see those working together, either the good or the bad from that. I mean, we were, we were fairly uh conservative in our deployment uh using enterprise models so that we can, you know, keep the learning on the inside and we're looking on the horizon thinking about what that means for our customers moving forward and, and how to keep pace with it. So I think it's still something we're trying to get right for, for a company like ours, it's less of an issue because we always work on a model of protecting our customer data. We make money by solving problem and not by selling data. So it works on ND A with pretty much everything we do even today for the last forever. So it's uh but at the same time, uh have to be very conscious on the whole art aspect of the data privacy. You cannot just uh rub it off and always actively working to use the tools which are there. And what legislation is coming ahead, always concern tends to be that any Broad Bush legislation should not put it all together for different sectors because industrial sector has different set of opportunities and different set of issues versus consumers which have a legitimate issues on misuse of A I. But that's probably not gonna occur in context or industrial. So how do we separate the domains and make sure that we do the right thing? So for sure, I think one of the things to consider though is the pace of learning when you just have it focused on the inside versus what you gain from the outside and how to balance the two. But obviously, customer data protection is of paramount importance period. That's a great question, Tom. Thank you. Oh, yes, we have one right here. Uh Hi, my name is Sebastian Picardo. I'm CEO of Whole Room for a lecture retailer in Canada. Um I wanted to ask if you could share two or three examples of things that are a company that is relatively early uh at early stage in, in the A I journey, what are two or three examples that companies could test and learn and see some uh productivity gains? I I think the the easiest and the most advanced is probably in sales. What does this customer need at this point in our life cycle? Tell me everything I should know about them and what products and services are, are the least or most likely to, you know, uh hit a nerve with them in terms of delivering their business outcomes. We're using that today. It's drawing, you know, efficiency uh and productivity with our people so they can get more done and spend less time researching. The same is true in, in customer service. Obviously, you know, we, we've all bounced into uh you know, a I bought land in, in the customer service. The question is how can you take all of their service history when you're, when you're a technology company, for example, and they have a real service issue that's technical based and waste zero time in terms of getting to diagnosis and to fix and then how can you inherit that fix into the support system forever? A I has been um a game changer in terms of our productivity curve there. I would say I would, I always think in 22 dimensions. First is the segments your skills are hard to acquire. How do you make A I A for people to use A I tools? Example I always give is I can drive in New York with Google Maps with but not without it. I will be, I'll be doing rounds of this block forever. But the tool is helping me to go on to those tools which make a a lower skilled people. Higher skilled person is a great use, use cases. That's one and second is I think one thing which is underestimated is how to reduce cycle of a capital project. Everybody builds something right? You build assets, building plants, you can heavily automate the whole capital engineering process because you can observe images, data pictures, you can also observe the engineering drawings, engineering processes and heavily automate them. That's something which is under discussed today. But I'm a firm believer that's going to be a huge advantage to anybody who builds capital assets because the design and engineering takes like forever because engineers have to redo it every time. But A I will naturally using LLM uh it can dramatically reduce cycle time. So it's more on a capital cycle efficiency versus skid efficiency. One more you got technology debt. Um And you're worried about aging workforce with respect to caring and feeding those systems. A I can be very effective in terms of understanding the code learning and being able to patch and fix as well. Very good question. Thank you very much to finish us out here. Lightning round. Um What is, do you feel each of you that the ambitions, the great ambitions of A I that I outlined at the very beginning are finally coming into alignment with what is possible right now. I, I it's, it's too early to, to tell. I think we're at the very beginning of it. Obviously, I'm, I'm uh I'm pro a I, I think it's important for the competitiveness of the country, innovation, health care, education. Um but it's super, yeah, swing super super early in, in the cycle, I would say using it in the sole context of autonomy. We're trying to give it a North Star to say use A I to solve a problem of skills and asset management, asset maximization. And I remain very optimistic that because we have a North Star, it's gonna, it's gonna scale very quickly in the industrial segment. Fabulous. Thank you so much. Give them a round of applause. Thank you. Thank you.