Gartner, Inc. (IT) Earnings Call Transcript & Summary

Hello, everyone, and welcome to this Gartner webinar: R&D Leaders, Enable Digital Transformation by Harnessing Data for AI and Beyond. This presentation will provide actionable objective insight, expert guidance and solutions that enable faster, smarter decisions and stronger performance on our most critical priorities. I'm Aditi Dogra, your moderator for this session. And before we get started, I have a few tips to help enhance your webinar experience. [Operator Instructions] This webinar is being recorded. You can watch this presentation again and find more great insights on-demand at gartner.com/webinars. Now I would like to welcome Gartner Vice President and Analyst, Atul Dighe; and Senior Director Analyst, Wally Puckett. Atul and Wally, thanks for joining us. And now I will turn it over to you.

Great. Aditi, thanks. Let me say good morning, good afternoon or good evening, depending on what part of the world you join us from. We're excited to spend some time together across the next 58 minutes or so talking about a topic that is becoming really critical for R&D leaders, one that is consuming many R&D leaders' time and attention. And I'm really pleased that I'm joined by my colleague and friend, Wally Puckett, who has the pleasure of chatting with R&D leaders on this specific topic day in, day out, multiple times a day. Wally, good morning.

Good morning.

Well, let's start with the tone from the top, my friend. We always have to see where things are going, and that means let's go in the mind of the boss. We did a survey very recently at Gartner, where we reached out to CEOs and asked them about 2025 and said, "What do you think is going to happen? Where do you expect things to go?" And probably no surprise, CEOs said, "We're going to be all about growth in 2025. And that growth is going to be technology led." And you see that in the survey data right at the top. Wally, you and I have been around the proverbial block a couple of times. We've seen this movie before, have we not my friend?

Many, many times, and it still is very, very important that we get this right.

Absolutely. And so there's a technology-led growth focus for our organizations, which probably is the reason or one of the big reasons why R&D leaders are so keen to understand where digital technologies are going, what's going on with all of the digitization activities and how do we advance on our digital journey. And that's also reflected in some data we collected at the end of 2024 regarding R&D leaders' top priorities. What you see here is the responses of R&D leaders about what are their top priorities for 2025. And we've sorted these based upon confidence level. And so what you see in the right-hand side of this page, in the blue shading, are the top priorities where R&D leaders feel the least confident in their ability to execute upon. And we look at these and we see a theme that continues onward here. How do I get faster? Reduce product development cycle time. Accelerate speed to maturity. How do I make sure I'm working on the right things? Understand the market potential of emerging technologies. And then you see the one all the way to the bottom right, implementing AI to improve R&D productivity. To me, Wally, this sends a message of faster, better, cheaper, with a little digital mixed in or maybe the digital is the catalyst, if you will. Wally, as you chat with R&D leaders every day, day in, day out, what are some of the big themes that are popping out? Are you -- kind of give some voice to some of the bullets that you're seeing on the right-hand side of the page here?

Yes. So what's amazing about this is they have less confidence in these very important things that they must do. So what we talk about day in and day out is, how do I get more confident in doing this? How do I actually become executional in doing these things, especially in the implementing the AI to improve R&D productivity because there's huge gains that can be made there relatively quickly, but there's little experience in R&D with these technologies. So we get a lot of questions about that, and we do a lot of giving advice on how to do that faster. Because if you can do that, that gives you another tool in the toolbox to actually impact on reduced cycle times, right, to actually impact on better products into the marketplace, better understanding of the customer and emerging technologies. We talk about it every day. It's really critical.

Absolutely. And we'll just use this as a little teaser, Wally, because I know we've got this coming towards the end of our time together. We're actually going to provide the group here today a little inside knowledge on what are the use cases for GenAI specifically that R&D leaders are using and to get -- where should they be investing to get traction. So that's coming. I know you've gathered that data, so I'm excited about that. But it's not just the GenAI, right? I mean that's one. AI is a broad -- that's one part of AI. AI is broader. AI is part of the broader digital thing. In fact, if we look at this and say, what are R&D leaders doing, I would summarize it to say that R&D leaders are rethinking the how of R&D, right? So we're really thinking through the process steps, how do we do what we do. On this page, you see some data that we gathered around what the digital transformational journey looks like and where folks are on this journey for how R&D is using digital systems. And you see the things here, digital systems are hosted and managed in a cloud; integrated with enterprise digital systems, right; we're at the beginning of our journey; we've automated some data flows; we've implemented some advanced technologies; the different digital systems used in R&D organizations are integrated with one another. All of these are indicators, are they not, Wally, of making some progress of where people are going and where they're investing.

2 years ago, this would not be the same. 2 years ago, they would be far, far less advanced as where they are today. And I would say they've really started the journey, but it depends on where they are, in what organization, what digital, what vertical they're in, if they're in chemicals or if they're in pharma. We actually find med device companies actually further ahead than others, which is interesting. But yes, this has changed dramatically over the last couple of years to the positive. And so looking forward to see what happens over the next couple of years, and it's all going to happen very, very quickly.

Yes. The speed of acceleration is fascinating. Now while you built a model on behalf of our Gartner clients, gosh what, 2-plus years ago.

Yes.

It's been a couple of years, around this notion of the digital transformation and kind of the maturity, kind of where are we getting to, where are we on this journey. And we've been asking this question now over the last couple of years since you built this model of where do people find themselves. And basically, you see the kind of levels of maturity defined on the page here from Level 1 where some things are digitized, all the way to the far right, Level 4, where basically everything is mixy, mixy, connected, connected. Maybe it's a matrix. I don't know. I don't even know what we call that. Where have you seen R&D teams and their leaders on this curve over the last couple of years? What's going on? Are people making progress towards the Level 4? Where are folks? Where have you seen that in the last couple of years?

Yes. In October of '23, we had a live meeting in Chicago that you and I hosted. And we had R&D leaders tell us, there were about 40-odd in the room, and they told us they were about 1.5. Some told us they were 0.

That's right. That's right. I remember people said, "Wally, where is Level 0?"

That's right. That's right. And so -- and now we see different results, and you'll see some. We'll show some data today. But I would say many of the R&D leaders now are saying they are at Level 2, which is encouraging. Connecting -- you saw that in the previous data, connecting their digitized systems to avoid the silos of data silos. So it's getting better fast, but it was interesting, it's where they've come in just 1.5 years or so.

Yes. And that's to your point around the acceleration of the speed factor here in that not only is there desire and interest from the very top of the org, the CEO, if you're publicly traded, your Board, if you're private, Advisory Board, other functions, we see this certainly across Gartner, where the digital transformation journey is occurring in all of the other C-level organizational constructs and kind of everyone is on this mode. So there is desire. There's interest. There's an investment thesis. Dare I say that this will lead to better outcomes vis-à-vis speed, efficiency, marketplace impact, profitability, top line growth on and on. And then we're starting to see it within our world of R&D in terms of people really saying, "Okay, this is real. This is not a passing fancy." And while digital transformation has been a term that's been bandied about for, I don't know, 20 years, long time, I kind of feel like it's really taking traction now. It's like in the cartoons where the guy goes -- where he's running fast, or he's [indiscernible] and eventually, goes [ p-ching ]. I feel like we're almost at the [ p-ching ] part, right? We're getting closer, which is exciting. I'd love to now invite the group in. We've got a big group of R&D leaders assembled here virtually, and we would welcome your questions along this journey. So as Wally and I are chatting, we'll certainly save some time at the end for questions, but feel free, as our moderator said, to use the question box to submit some questions. But we also want to do some instant benchmarking. And we don't want to lose an opportunity where we can compare notes. I'm going to invite our moderator, Aditi, back in just for a moment to give us instructions on how to do this, and then I'll walk you through the question. Aditi, please let us know how to do the polling.

Thanks, Atul and Wally. We have 2 polls today. [Operator Instructions] Okay, Atul and Wally, let's see the results coming in. [Voting]

Great. Thanks, Aditi. So what you'll see is the question that we've simply asked is we'd love to know what your goal is. What's your 3 -- what are your top 3 most important goals in wanting to digitally transform your R&D function? Why? Why do you want to do this? And we're going to give you the option you can tick up to 3. Please don't take more than 3 because it messes up the data and then our research team gets mad. So don't get the team mad at Wally and I. Let us do this. So we'd love to have your answers to this, and you see some of the choices, right? So are you trying to accelerate cycle times? Is it about process efficiency? Is it about productivity? Is it about cost optimization? Are you really looking to mine some data insights? Is it about specifically your lab? Or do you think this is really vital to attract talent? Or are you about increasing the digital quotient of your innovation? So some good choices here. And if there's something else, maybe we didn't have it on the list here, feel free to tick other, and you can type that into the Q&A box, and we'll gather that from you as well. And we see the votes coming in. Kind of live voting here. It's kind of fun, Wally, it's coming into play.

Yes. Look, look at the apply AI and machine learning is still in the lead, although the others are catching up. And we'll talk about this later, but one of the most important things about using our data and applying it is that we're going to learn new things that we didn't know. We're going to learn new things about our own data that we didn't know. And not just higher quality decision-making, but new decision-making would be really important.

That's great. That's great. Well, let's do some -- I'm going to do a count down. So I'll start at 5 and count down as we round this out, and then we'll take a look at the data here. 5, 4, 3, 2, 1, 0. Great. Thanks. Yes. So really interesting. So -- and I know exactly what multiple people are thinking. Could I have ticked all of the above? You want to have all the above. I want to do it all. I want to do it all. Aditi, thanks. Let's go back to the slides. Yes, I'd like to do it all. And I think that makes a lot of sense. Now as we're doing this, let's talk a little bit about the challenges, right? So we've talked why we want to do this. The boss wants it. We want it. We think it's going to help us become more effective at what we do from a team perspective, more efficient. All the reasons we just talked about, mining more insights from the AI and so forth. It's really hard, right? It's really hard. Maybe that's a good -- Wally, I guess, because it's hard, that's why you and I have a job, maybe I won't say that -- I just said that out loud. Yes, it is hard. It is hard. And here's the thing. We asked R&D leaders what's getting in the way of efficiency? What's really bogging you down? And you see what -- the data that is assembled here. Some of it's about people, making decisions, having the authority to make decisions. Some of it is about technology, the technical challenges, right? Some of it's about talent, talent constraints or process constraints. And certainly, budget, none of us have an infinite amount of money to spend. And so even though there's a lot of focus and attention on digital activities and digital investments, I don't think any one of us has a blank check just to do with what we'd like to do. The thing that we do know coming out of this is that there is a strong desire for R&D leaders to get access to data, right? We asked, hey, where do you think decision-making is going to primarily come into play? Just a moment ago, I showed you the data that showed, hey, decision-making, making decisions are hard. That's the big inhibitor. Well, decision-making is going to be primarily data-driven according to over 1/2 of R&D leaders. And so we're moving more into a world where the data is helping us make decisions. But when you think about who governs that data, this is on the right-hand side of the page, it's a little bit more of your mixed bag now. About 2/3, just under 2/3, are saying it's going to be the responsibility of the R&D function to govern data and analytics. Some of us, about 1/4, are saying, hey, that's going to be outside and 11% of us are probably the honest ones, no idea, right? We don't know. So I think there's a sense that the data is going to help us make decisions. It's going to help us go faster and smarter. It's going to find those new things, right, that people just talked about. And I think this is congruent with what we're seeing not just the journey that people have been on the last couple of years. But quite frankly, if we look at a couple of quotes here, Wally, from some luminaries, this is kind of the journey we've been on for a good while across the course of, dare I say, civilization. Is that too bold of a statement to make? What's your sense of the evolution of this, Wally? It's a fascinating thing because we seem to believe that data and more data will lead to better decisions.

Yes, it's really interesting. I mean the top quote is almost mind-boggling to think that we're now creating every 2 days as much information as we created since the beginning of civilization through 2003. But we hear this all the time. It's not that we don't have data. We're drowning in data. We just don't yet do it. We can't synthesize it. We can't make sense of it, right? And to almost becoming incapacitating, which is something we need to avoid, right? And that's that second quote, "Errors using inadequate data are much less than those using no data at all." And I get pushback from my colleagues in the Gartner, "Oh, you shouldn't say that. We want their data to be really great." The problem is the data is not great right now for most R&D leaders, and they have to start now. There's a journey in learning to use new systems to create a digital-first mindset, and you can't wait until the data is all perfect because you will be out of business. right? You've got to get started. But the last quote is the one I really think people need to take home. "The core advantage of data is that it tells you something about the world that you didn't know before." And this is from Hilary Mason. She's absolutely brilliant. And so this is what's really exciting about what we might know and what we might find out, something new, something we hadn't thought of, something that we would not have thought of without having data and analytics.

Yes, I love that. I love that. I love the -- to me, that kind of vision, if you want to call it that, of what data can enable. So it's not just decision confidence, like I know I'm making the right decision because I have more data. Decision quality. I'm making better decisions because it's being informed by the data. In a moment, we're going to talk about the people side of things because that people aren't absent in this, right? We're not just ceding over to kind of the machines learning -- running everything. But I do think that that kind of end-state is very appealing. Let me share with you some information and some data about where folks are on this journey, the digitalization of the majority of R&D operations. Basically, we asked, think about the majority of your R&D operations today right now, this was gathered at the end of 2020 survey -- February, so last quarter. Where are you? And you see the distribution here, whenever I see as a data -- as a social scientist, certainly not data scientists And I see the normal distribution and I'm like, oh, okay, did we ask the question the right way? But maybe this is really where folks are, that the majority are in that kind of middle, to your point of the slide of maturity we shared earlier, kind of in that 2, right, a little over 2 maybe, leaning kind of 1.5 to 2.5, which would be a normal distribution here. What are you hearing, Wally, when you have conversations with R&D leaders and they say, I'd like to understand where am I in comparison to everyone else? That's probably a question that comes. In fact, we've already seen a couple of questions come in the question box, like how do I know where I am? Benchmark me. What do you tell them? What do you tell them what someone asks you that question?

Well, we can certainly show them this data. And by the way, not in jest, but we actually added Level 0 there, you see.

That's right. I see.

So there's Level 0. So we heard you. So we're going to update the maturity curve and put Level 0 there. I'm hoping that number gets less and less over time. But it's important to them because they are overwhelmed by the sense of how big this journey is, right? I mean it's just huge. And they say, where do I start and where am I compared to everybody else? And so I give them solace by saying, "If you're 1.5 to 2, you're in a really good company. You're in really good company, right? So you're not behind yet, but don't wait because you could be behind because things change so quickly." So I actually have a couple of R&D leaders that look and said, "I actually don't believe there's 7% that are totally connected across everything." And I said, "Ah, they answered the way they did. We just collect the data, and we just present it to you as it is." So don't feel bad if there's no way you think you're ever going to get to that last maturity level. It's really a journey, and it's about doing things well as you go, gaining efficiency as you go, creating a more engaging R&D environment to create better and better products for society in the future.

Yes. And I love the notion of -- and I think that's purposely why we talk about this as a digital transformation journey. It's a process. And by the way, it's really interesting. I'd say even to the 7% who may or may not have been honest in terms of their answer, maybe a little arrogant, there's going to be a Level 6. You and I may -- we may do another session like this. That would be kind of fun to think about, Wally. We may do another session like this later in the year where you and I specifically talk about what's next and kind of speculate a little bit. But I think -- because this stuff just -- right? It just keeps moving. And so the moment you think you've arrived, you haven't. The moment you think you've solved it all, you realize you haven't. But I do think what we're seeing is not only the momentum and the movement, but the early indicators of success around some of these activities. We're going to share some of that in a few moments. But I want to acknowledge something that's really, really important here. It's hard. Right? We've already said this a couple of times. This is not an easy thing to navigate. In fact, let's do another polling opportunity here. And you'll follow the same instructions that our moderator, Aditi, gave us a few moments ago. [Operator Instructions] It's just amazing, Wally, that we have a QR code. I still don't quite understand how it works, even though it's a technology that's been around for like 40 years. But here it is, I'd love for people to kind of weigh in here. And here, we're going to limit it just pick one, pick one, kind of the biggest barrier that you currently face in digitally transforming your function, right? Is it not having a clear strategy or road map? Is it just difficulties in getting your data house in order? Budget constraints? Is it cost optimization issues? You don't have the technical skills? Hard to get the team to change their behavior? Is it hard to make the case to senior stakeholders? Do you need someone else like IT or other functions to get involved? Or you're just too busy? Or maybe there's something else and you can put that in the comment box. [Voting]

Aditi, if you wouldn't mind, if we can see the polling results coming in on this, that would be great. Here we go. It should be right on our screen here. The biggest barrier that you currently face in digitally transforming your function, and again, we're just asking people to pick the single biggest, the one at the top, or the biggest pain because again, someone was going to say, I could have done all of the above. And let's see what comes in here. As the votes are coming in, it's fascinating. So right now, at least it looks like the top one is getting the data in order, but we've seen a number of votes for the other factors, including not having a strategy or clarity around the strategy. Some of the team stuff, Wally. We don't have the skills. We don't have the will to do it, right? I don't want to. I don't know how to. I'm too busy.

And more than 1/2 of my conversations are around behavior, around a reluctance and a fear of changing into this new way of working.

Yes. How many times have we, kind of jokingly in video conversations or even in our live meetings that you and I have hosted, kind of jokingly thrown out, and I've heard you do this multiple times where you've asked people to raise their hand, kind of ingest who's still using paper, pencil, lab notebooks. And the first time you told me that I was like, what do you mean? And then you said, "No, no. This is a real thing." And you tell the story of when you digitized one of the laboratories you were working on back in your old days in the running R&D in med device. And I still find that shocking. And by the way, that's not that old of a story, is it not? We're not going back into our time machine to say, "Let's go back 45 years." We're going back in our time machine like a couple of years ago.

Yes. Absolutely. We still have major clients that still have paper notebooks and paper notebooks are great, but you're never going to capture all the data in a way that you can actually make good use of it. And beyond which, we haven't talked about this, but a big part of this is capturing all the data that is implicit in people's heads before they retire or leave the company to take advantage of it in the future.

Yes. I mean the elusive like can I put a chip in and download your stuff is a very real factor. I think you and I hosted a session in Chicago just a few months ago. And one of the clients in the room I remember shared with us their biggest fear, which is one of their top 4 or 5 research scientists who are retirement eligible given the time horizon, would actually choose to retire in 2025 and out the door goes all of that. Like intellectual property, let's call it, just the knowledge, but also, by the way, like you kind of know where the stuff is. You kind of know how to get things done. So a very significant challenge as we come into play. Aditi, thank you. We can go back to the slides. This is super. Yes. Great. Thanks for -- by the way, thanks for the group kind of jumping in the polling questions. What you saw is just a mixed bag, like people are really thinking about their biggest barrier. A lot of it has to do with technology, but also has to do with people. And to your point, Wally, around kind of getting people to make the move, I think there are some critical skills that we have to impart and kind of amplify on our teams to drive things forward. These 5 on this page are germane whether we're talking about the digital conversation today, you and I could have been talking about technology road mapping. We could have been talking about creating an R&D strategy. I don't care what it is, people. And despite the fact that R&D works so deeply and intimately with technology, and uses technology and many of our clients create technology or processes that are technology enabled, at the end of the day, it's still a people business. And I think sitting on top of the technical acumen that's going to be needed to execute on these activities are these 5 competencies or skills thingies, I don't know what to call them, but I know when I see them. And what we want to do is really dial these up. And look at the top with me, smart risk taking. I think we've got to get back to the point where our team members are really trying new things and, dare I say, occasionally failing, maybe failing a lot. Scientific method. You got to try something to see if it works. And if it works, you got to try it again to determine why it worked. And if it didn't work, you've got to change to see what can work. But we just try, fail, adjust. And I think we've gotten away from that, particularly in corporate R&D. And I'd love for us imbue more of the mentality of we're just going to go make a mess. We just got to go make a mess. Now what are we making a mess of? We're making a mess of the big things, holistic problem solving. We're not down in the weeds. I want to elevate the level of focus that we've got around the way in which we think about what we're doing, right? And as we're doing this, we have the thing in the middle, challenging posture. We did an analysis about 3, 4 years ago -- recently on what differentiates or separates new product development success, kind of company A, company B, kind of apples-to-apples, oranges-to-oranges, similar companies, similar product sets and one of the big differentiators we find is the companies that are having a disproportionate level of success have what we call the challenging posture on the R&D team. The R&D team is asking tough questions of each other and of stakeholders. It's very interesting. Now it's not only of each other and stakeholders, it's in the network, right? So sometimes that network is internal. It's navigating the network of stakeholders, marketing and supply chain and manufacturing and IT and everybody else, but it's also sometimes navigating the network beyond. Academia and startups and co-development and other companies and so forth. And all of this is undergirded, which is why we purposely put this last with having digital dexterity. Digital dexterity is a concept that we at Gartner discovered again in a study where we looked at teams who were working on digital activities, and we wanted to see why some teams were having a greater degree of success compared to others. And we measure success based upon did they deliver the project on time? If not, early? Did they deliver it on budget? If not, under budget? And did they meet, if not exceed the expectations set for those projects? And what we found teams time and time again that were successful had what we call digital dexterity. They had the facility to deal with technology. They weren't all [indiscernible] and PhD scientists from IIT, MIT, Caltech, whatever. They were just regular folk. But they were people who are willing and able to embrace change, willing and able to use technology and understand how it fit in and willing and able to do the other things that you see above it. Take big risks, go after the big problems, challenge an appropriate network when needed. And I know, Wally, you spend a great deal of your time working with R&D clients talking about digital transformation, but you've told me that 98% of your conversation kind of circles back to the people, does it not?

It does. It does. In fact, they can do the best job of the technology in the world and then never get people in the team that just say, "I'm just not going to do it." Literally, I've had R&D leaders tell me they've just got people that are saying, "I'm not going to do it." And there's a couple of reasons for that. We don't have enough time in this presentation to talk about it. But yes, that's -- it's a big deal because people have to do this work. People are the ones that do the intellectual work of creating data, storing it and then how do we use it better is the other question.

Yes. For sure. For sure. Well, let's talk a little bit about that, right? And so part of the reason that this really matters is the intersection between data and people is what it is. Again, we've made that statement that this is a people business on the R&D side. We're using digital activities and technologies because where does R&D create data and how is it stored and used is a question that we get often. And I'd love to say that we had a very neat answer that said it's in these 3 categories or these 2 buckets, but it's really kind of a mud on the wall kind of slide, is it not?

It is.

Everything we do, all of our workflows, and again, here, we've given you kind of an illustrative gate and phases. All the tasks we do, some illustrative -- just illustrative tasks, right? The systems we use, if we think about those systems, and I see some great questions coming in the question box around what does it look like? And hey, where does Excel fit on this? If you're using Excel, is that old school manual? Well, it's not quite a slide rule, right? Not quite a slide rule, but where does it fit? I mean these are questions people have. And the point, I think, Wally, that you make is it's yes, it's all, right? It's [indiscernible] files on my laptop. It's the data that I wrote down in my lab notebook. By the way, I've got a pencil here, you and I are still old school. I love that we do probably take notes with a pencil. It's that. It's what's in my brain based upon my 30 years of experience. It is the analytic software that captured some stuff from the experiment, right? It's the diagnostic devices that we've got attached on everything that we do. It's all of it. It's all of it, right?

Yes. And let's make a point here. We talk about, oh, do I need to get all my data together? There's a question about my historical legacy IP and other data. We'll talk about that in a second. But here's the thing. You are producing with these digital systems. And many of you have digitized some of your workflows, right? You've digitized some of these tasks. You are creating data that is going to now be in a format, findable, accessible, interoperable and reusable that you can begin to do some data analytics now. And you're going to say, I don't have a big enough data set. And the bigger your data set, the higher quality, there's no doubt the better output you're going to get. However, you can start where you are and create synthetic data. We don't have enough time to talk about that, but it's a powerful technique that you can use in the future. But think about it. Every day, your scientists and engineers are doing these tasks. They're doing them typically some -- most hopefully in a digitized system, even if it's not interconnected. So there at least is a silo of data that you can harness if you can connect it in through an AI engine. So let's just think about that going forward.

Yes, I agree. Agreed. Yes, that's a great point. And I think that's the mentality that we have to bring to the table. So let me do this. I know, Wally, in the next couple of slides, I'm going to have you walk through these. This is kind of a part 1, part 2 because one of the things that you do in addition to talking every day with R&D clients about their digital activities and other things around running the function, is kind of lead some of our activities, gathering some data of kind of where are we, right, in this world. And so the next couple of slides are all in servitude of answering the question of what's the current state of digital transformation in each of the different kind of workflows. So some of the stuff we just showed you on the previous kind of spaghetti chart with all, right? You look at some of those. What are you seeing here in the data? Lead me through because I know we've got 2 slides of activities here to kind of lead the group through.

Yes, they can see that. I don't want to spend a whole lot of time. What we've done is we've done some survey where we actually looked at those workflows and those digital tools you could use. And we ask you, where are you, right? Where are you? And interestingly, this kind of coincides pretty well with where we thought on that digital transformation curve, right? So you see here that, for instance, for data and analytics/visualization, more than 60% think you're at intermediate stage, which is exciting. Product design, 61% think you're at an intermediate stage, 21% think you're advanced. So we've got data for all of those workflows in this slide. And if you show the next one, you'll see a similar thing, right? In this part 2, you'll see that -- you're going to see that we are getting some maturity in each of these workflows and activity types that we talked about. So you are beginning. You are starting to get it done. And the problem is that for some of you, you're just at the initial state and you're kind of asking, how do I get past that initial state, right? So yes, that's what we've done in this slide. And, Atul, if you go to the next slide, you'll see that as well. Can you bring the next slide up?

Yes, you should be seeing The next one was the top challenges.

So -- and look at this one as well, right? So in that next slide, you saw that there were other of these systems that were also mature. And then when you look at this one around managing the data, this is really interesting as well. You are telling us that you have data that is poorly integrated across different systems and leading to silos. We're going to talk a little bit more about that in the future. But you're exactly where we thought you would be. This is a perfect -- this answer is exactly what we thought. You're digitizing individual work activities or tasks but you're not linking them together, right? And in order to get all the great data analytics, you need to link them all together. You need to tell the whole story around where that data comes from to get meaning from it. You have availability of dynamic data for timely decision-making. This is a challenge to you. You don't have it on a real-time basis, which is going to be the key to really be successful. So if you just look through these challenges, they're all around what's going on with my data. Access to data is restricted or limited. Data is often inaccurate, incomplete, inconsistent. And usually, you're talking about you don't have all the metadata that you need to really make that sense of it. Clear policies and procedure, this is your responsibility. You must set out policies, procedures, data governance and access rights and decision-making before you begin. And then look at this, the large data volume makes it difficult to manage and analyze. It really does. And we are a little bit concerned about cybersecurity because we're now putting many things on the cloud, you saw early in the presentation. But listen and understand, no great gain happens without some risk taking, right? You just have to understand the risk. We want you to move from risk averse to risk aware because we want you to get the benefit of what's going on, understanding what risk might come from it.

Yes, that makes a ton of sense. And I think certainly, I love that we're getting a ton of questions coming in, which is fantastic. I think some of them we're addressing as we go, and we'll save a little bit of time towards the end as well just to kind of specifically answer questions we don't get to. But we have several questions coming in about like what are the ones at the kind of Level 3, Level 4 of maturity doing? And what you're showing here is where people are stuck. And I would pause it that many of the organizations and teams that are at that further down the road on maturity have overcome some of these challenges, right? They've done the integration. They've overcome some of the talent piece. They've utilized the 5 competencies that we showed you a few slides ago to really dial up kind of the people side of things. And so it's both end. There's a description of the absence of what people are doing, which then we flip on its ear -- on its end to say the activities that are absent the most, the few are doing to achieve some results. And I think that's really fascinating. The good news for me, as I look at this last few slides of data, is most of us aren't alone, right? Most of us are kind of in the mode of this kind of making progress, some a little bit further down the road than others on particular issues, but most of us are kind of making that move. Now as we're doing this, you introduced to us, Wally, I guess, about a year ago, the concept of a Lab of the Future. And oftentimes, and we know that some R&D organizations, including many who are represented here in this conversation, actually do have a lab, right? You're a chemicals company, you're an exploratory company, you have a -- you're actually -- there's a laboratory that your people are doing stuff in. And I know some folks who are joining us here, they don't have one. They have kind of a virtual lab or they have kind of a lab-like thing. There's experiments, there's process. So let's just assume that this -- when we're talking about Lab of the Future, we're talking about kind of it all, just kind of a nice umbrella term, right? And I think the critical part of this is the people side. Again, as always, like the people part of this becomes really, really important to get their buy-in, to get their support moving forward. But there's also a systemic or systematic part of this. And so while -- let me walk you through some of the slides that we've got here and kind of what this might look like because a few folks are asking kind of like how does this actually play out, right? So walk us through this progression of kind of creating, if you will, the Lab of the Future. This presumably would -- the next few slides will share kind of what would it actually look like if we were to diagram out kind of on that Level 3, Level 4 maturity, right?

Yes. I would just say, first of all, Gartner has been talking about Lab of the Future long before I got here. So a lot of the...

Okay. Okay. I'll give you credit. You should have taken credit for it.

Yes, I know. I know. I want to give full attribution. But here's the thing. Here's the thing. This is illustrative. When you think about these, this now goes back to those same workflows we talked about in the beginning, right? So the innovation management, the project portfolio management. These are all workflows that you can digitize, right? For many of you, it's electronic laboratory notebook. For many, a laboratory information management system. For the engineering-based companies and for medical device companies, it's the computer-aided design and engineering and even the ECADs as well. These are all systems that you could digitize. And if you simply digitize them, it's good, but it's not going to be connected. You're not going to be able to use data in a real time because it's going to be difficult to get to all of those individual pieces of data. And then you see the business systems on the other side. And those are really important to you as well. You're never going to do really great about new innovation until you know what your customers are saying about what you have right now. And that comes from that customer management relationship system. Let me answer one of those questions about why did some people -- companies get to Level 7 already or that level that was very high. Why did 7% say they were at the fifth level? Most of them probably had already heavily invested in digital systems like CAD systems and others, right? So they already had to do a lot of data governance. Many of them had put a really detailed product life cycle management system in place to create a digital thread or backbone to do this. But the most important thing is that they tied their laboratories to the business systems and to the business outcomes of the company. Then they were able to show real productivity to the C-suite about why we keep going, right? Why we keep going. Look at this. Now if you now take and use a product life cycle management system, that's what Gartner has been recommending for years, as the backbone of your digital thread, you can now tie each of these silos together into some type of democratized data repository. I'm showing a data lake. It could be any that you've already chosen, right, a data warehouse, a lake house, whatever it is. It's all tied together. And then in the next slide, we're going to show you where we really want you to be. And that is in order to create the Lab of the Future that's truly effective at affecting business outcomes, you also have to be tied into real-time data from those business systems, the ERP. And I'm just not just talking about the shallow ERPs but that you might have some HR system in. I'm talking about sales. I'm talking about those things that happen. The managed manufacturing execution system for those of you that actually make physical products, you've got to be tied into that. And then that competitive intelligence comes back to innovation management, right? Competitive intelligence and patent management. So this is our aspiration for you. And for those that do a better job of creating that vision that this is all nice to have, this great R&D, but it's insufficient on its own. It has to create business outcomes. And those that get mature quickly there tend to make more progress more quickly.

Yes. That makes a ton of sense, Wally. I mean just at the highest level, and again, there's a great kind of visual picture here of what we're doing. I mean, we've got to unlock this. And I do think it feels like -- and again, that's why I do like the metaphor of the Lab of the Future or the actual physical lab, even though it doesn't apply to everyone, but I think there is like imagine a door that we've got to unlock and open and kind of leave the door open, so everyone can come in. And it's really moving from something that's much more digitally enabled, but it's the combination of the people and the things and the business, right? So it's not enough that we've just taken our lab paper, pencil, notebook and now we've digitized it. That's great. That's a good start. But it's how does that connect even with something else like end point-of-sale data that you might have if you sell something in the marketplace, right? How does that -- your inventory management system in the warehouse? Like you're like, what do I have to do with that? I make -- I'm the innovator. I make the new product. It's their job to make it and sell it, but it kind of matters to us, too. Like it's that level of kind of intelligence, we can call it, or what even some have dubbed wisdom to get to. Aspirational, certainly, no doubt about it. But I think certainly something for us to strive towards and keep moving down the road. Now in our time left, and we're perfectly on schedule, one of the things that we had promised a few at the beginning, the AI thing. I know it's a hot topic. Everyone wants to know about the AI, particularly the GenAI coming into play. Let's talk a little bit about that, right? Digital transformation provides that infrastructure, right? If you've got the infrastructure, then you can use some of these AI things. And, Wally, you have been part of the team at Gartner, which has been looking very closely at AI and kind of specifically the subsize of AI, GenAI and asking the question, what are the early use cases that R&D leaders have been investing to gain back? And we've actually organized them in terms of feasibility and value. How easy is it to implement? How feasible is it to do? And how much value is it driving? And so let me just kind of walk the group through this and, Wally, jump in. So you look at this and one of the first things, again, just maybe have you comment on this, and this is -- I always say this, we should put a little asterisk that says as of, like as of February 2025 because tomorrow, someone's going to be like, I got a new bullet point to add on this. So when you talk with clients, when you talk with R&D leaders about GenAI, what are you hearing? You're hearing some of these things. Is there any color or commentary you'd give on what are the specific use cases people are doing to use GenAI around?

Yes. So really quickly here, these are 20 use cases that we put together that really kind of mimic the workflow from ideation to commercialization, right? Some of those major task and workflows. And then we compare them to each other. We compare them on value and feasibility. And we at value, we rated innovation as the 50% weighting and then we had decision-making and executional excellence at less. And then on the feasibility, it was, can it be done at all was weighted at 50% and then how you would do it. And so then this tool that we use within Gartner then compares them with a rubric that we score by. And it gave us these 3 quadrants, one likely wins, another maybe a little more difficult and then the one, which is the marginal gains. But what we want to do right now is to lead you to some high -- some low-hanging fruit kind of use case.

Yes. And that was a question I asked you, it was like, was the juice even worth the squeeze? These things -- and probably the time that everyone had for 46 minutes in this thing, probably in the last 46 minutes, each one of us have received some inbound query from some new friend on LinkedIn, who is pausing to us that they've got the magic bean. Buy my magic bean, it's going to solve all your problems, right? And so what I love about what you and the team have done is you've basically kind of pressure test that magic bean. You've asked is the juice worth the squeeze and you've put them into these different categories, right, like the likely wins, calculated risks and marginal gains around the specific use cases coming into play for GenAI. And just kind of in the interest of time, let me just go to the ones that are the top use cases with likely wins. What are you hearing from R&D leaders? What kinds of stories could you share with the group here briefly around what people are doing to be successful in using GenAI right now?

Yes. So these are those that are the lowest-hanging fruit. And here's why I'll say that. Product design is really, really important. This is a hard workflow, but it may not be the easiest one to do. Material design is another one. You can actually get some outside vendor help with this one. But look at the 4, the technical literature review, the sentiment analysis, ideation, patent analysis, this is brand-new stuff, and let me add one more here. Data management is coming up. How do I end manage my internal data? Why are these low-hanging fruit cases? Because these can be well done using a large language model, which you already have access to, whether it's Gemini, whether it's CoPilot for Microsoft, whether it's OpenAI's ChatGPT or any of the other commercial large language models like those, you can use those in their native form to do these use cases. And I've got great information from R&D leaders and literature to show that ideation is well augmented by a large language model. Sentiment analysis is instantaneous. Technical literature review can be done on a smart basis. Instead of just doing a Google search with keywords, you use a smart prompt with AI to get more of what you want. And patent analysis is almost instantaneous now. In fact, there are now many, many vendors on the outside who have now added AI to their patent analysis machine. So you can get early information about how novel this idea is and then does it have freedom to operate. So these are the low-hanging fruit. I call them that because you don't have to build your own model here. In the other systems for product design, you may have to use something like a generative adversarial network or a variational auto encoder or a newer diffusion method. Those cases are not pretrained, so you have to train them on your data. So that's why we call these the low-hanging fruit where there's a lot of juice for the squeeze, and it may not sound all that great. But if you think about how much time you do technical literature review, how much time you take to do that or how much time you take to get within your own data in your data management streams, each of your researchers are spending an enormous amount of time doing something that they could cut in 1/2 or more to save a lot of time. So that's where we would like you to first look.

Yes. It's fascinating. A couple of questions on this, Wally, that are coming in from the group and that kind of are in my brain as well. The first one is, this notion around -- you and I have talked about prompt -- being good prompt at, what are prompt engineers…

Prompt engineers.

Ask me the prompts, right? So the way I think about it, and correct me if I'm wrong, my brand goes to this of like, again, old days for you and I, maybe not everyone else on the line here, maybe just you and I. When we did research pre-internet, right, pre-Google, we had to go to library. And you had to -- you got really good at kind of doing research in terms of -- remember, we'd have those a compendium of articles and then you look at keywords and then you track an article down, then you'd go, you'd have to go to the microfiche, print it out, highlight, all that stuff. It took a lot of time. And there was a skill set associated with that. And then all of a sudden, we entered in the modern era where there's a little box that shows up on your screen, you can type a query. And remember the early days where people learned how do you actually Google something. Like Googling was not even a thing, but we had to learn how to be good Googlers, right, or how do you Google. Is that the same evolution that you see with some of the AI activities? Like at some point in time, all of us will be good at doing the prompt thing. But right now, it's a skill to learn. Is that a fair kind of comparative set?

Yes, it's incredibly important. And it's not really straightforward. You think, oh, let me just -- give me 100 new ideas on X. You'll get 100 new ideas. But then, what about those ideas are really interesting? So what you find is that prompts really that are well done have some characteristics. One, they're very clear with the detail of what you want. Two, they're very concise. And here's the most important. They are constrained, right? So as an example, I've done a search recently just playing with our own internal GPT model. And I asked this question. I said, provide advantages -- some advantages to new Band-Aids, 5 new ideas for great improvements to Band-Aids, rate the ideas and rank the ideas on novelty of the idea, customer benefit and feasibility with equal weighting and do it in rank order. Now that's a really good prompt because it has all those characteristics. It's clear, it's concise, but it has constraints. And before I get a flood of questions about, wait, constraints, isn't that going to ruin creativity? I can show you good evidence that constraints actually add to creativity and improve innovation. So I just need to dismiss the myth that constraints are not good for creativity. They really are. So yes, prompt engineering is something that you need to learn and practice and get better at. It's an iterative process and you just have to go do it. And we do have some great content on prompt engineering, but it's something you can just get started on.

Yes. I find that to be really interesting, right? So one of our clients I was chatting with said they've actually designated a couple of people on their team to be the lead learners on prompt engineering, and they are having them spend a lot of time kind of getting good at that and then teaching everyone else how to do it. I think eventually, we'll get to the point where we're all conversant enough and familiar enough with the tools to be able to do that, but having kind of some lead learners makes sense. One other question then on this, and we'll start to wrap up. What about the intellectual property piece? How do we handle that vis-à-vis kind of the AI, GenAI models? People are worried, appropriately so, of if I ask stuff, if I put stuff, is that now available to everyone in the world to kind of see? If I'm doing searches on patent things, does that now then signal to the outside world that I'm interested in said thing and/or give my thing away? How do you -- how are you helping clients navigate the intellectual property piece because we know the lawyers are involved?? How do we do this?

Well, so I will tell you that the commercial GPTs have done a much better job now at providing some security for your prompts and they don't retain them as long as they used to, but they do retain them, but they don't make them public. So for the commercial ones. Now for the open ones, be careful. For the open models, be careful of those. But here's also on the intellectual property side, people ask, can I actually patent something that was actually thought -- that was created by a GenAI idea? You actually can, as long as the natural person, according to the USPTO, contributes significantly to each claim, that is, is involved in a reduction to practice. So you can patent that, that is patentable. The other patent concern we have is that if I use something that -- from a GenAI model that was in the public domain and part of its pretrained information, is it possible I could be conflicting with someone's in-force patent? Could I be -- can I be doing that? And the answer is yes, you could be. So as with any of your inventions, you have to do a really thorough freedom to operate and make sure that you're not using somebody else's patent information. And that includes trademark and copyright as well, right? So all of those are concerns.

Yes. Yes. So tread lightly. Wally and I are not lawyers. Talk to your own lawyers. But I do think that there's a fair bit of latitude in terms of running moving forward. So let me just summarize this a bit. And what are we doing here? We're creating the future. We're really creating the future of R&D for R&D, with R&D, whatever you want to call it. And I think there's 3 key elements to this that we have to think about. The first is we've got to anticipate. And I think we've got to really think ahead of where things are going. So Wally, you've given us a lot of food for thought, not only of the current state of affairs, but a bit of a kind of a window into where things are heading going forward. And so we've got to anticipate that. As we anticipate, we've got to accelerate. Speed matters, right? Speed matters a lot. We've got to go really quickly. And as we're doing this, we can't go alone. We can't go alone. We've got to bring our team along with this. We've got to bring the rest of the organization along with us, and we've got to advocate for these activities. And so even though we started this session with some data saying CEOs believe the future is growth oriented, 2025 is growth-oriented, led by digital investments and digital technologies, there seems to be agreement. We've got to reinforce that by saying, here's what that means. Here's where we could go. Here's how we can get there quickly. Here's why this makes sense. And then lather, rinse and repeat over and over again. I'm going to say this, and then I'm going to turn it back over to our moderator, Aditi, to close this out. 2025 for R&D leaders, I hate to say this, Wally, because it makes me sound horrible. I may have to chat after this. It's going to mean a lot of meetings. A lot of meetings with stakeholders. You're going to have to kiss a lot of babies and shake a lot of hands because we got to go old school, change management like you and I did when we started our careers to say, how do I get people excited about the thing I'm working on? And that's going to require a fair bit of lift for us as R&D leaders to say, how do I get the rest of the organization because I'm anticipating where things are going. I want to get there quickly. I need you to come with me. And that's the challenge at hand. Wally, as always, fun. Let's turn it back over to Aditi and then we might have a couple of minutes to grab a few final questions. Aditi, thanks for moderating this today.

Yes. Thank you so much, Atul and Wally. So audience, before you go, here are some resources to take you deeper into the topic. We invite you to explore how your peers are planning to invest in R&D technologies in 2025 to calibrate and inform your own technology investment strategy. Learn how your peers are leveraging Gartner to achieve success in their R&D role with Gartner for research and development. Follow us on social media and get the latest insight and trends. If you want to learn more on how Gartner can help you achieve your mission-critical priorities, contact us via the methods on this slide. You can download these resources from the bottom of the page, plus you can find more Gartner insight, access the presentation slides and see upcoming and on-demand webinars at gartner.com/webinars. Okay, Atul and Wally, I'm turning this over to you to address the question.

Great. Thanks. It looks like we've just got a couple of minutes left. So while we just took a -- maybe take 2 quick questions. The first is, where do you get started? Where do you find yourself on this? And maybe a better way of saying is, what would you recommend as a great next step? How do I figure out? I want to move forward. I want to make progress on my digital transformation journey. I want to get deeper into GenAI and the activities that we've seen. What do I do? What should I do today?

Great. So you need to first assess where you are. And then you need to create a vision and a strategy. Now understand this is not a 10-year strategy because I won't help you do that. I'll help you do it 1 year. For AI, I'll help you with 6 months. So it has to be flexible, but you have to have that strategy. Secondly, you have to get your governance together. And you should have that already, but if not, be sure that you have your governance together on how you're going to use data, how it's going to be managed, what its format is going to be. Excel is not going to be a good digital repository for you unless you strictly control how the data is entered and what field it's entered on and what its format is. Otherwise, it's totally not a really good searchable system. But to do all that work, aren't you really better off with an electronic laboratory notebook or LLMs. And then third, you have to create a road map. This doesn't happen all at once. So there's a road map that has definite milestones along the way and learnings that come back to you, right? So you create that road map and then you get started. So first things first. And I would just recommend that if you don't have a product life cycle management system, you think about doing it. None of this is easy. It's really difficult, but you will need that to get going. Otherwise, how will you create the connections? And if you have another way of doing that, that's fine. So that's how you get started, the tool, and we're here to help.

v Wally, thank you. As always, a pleasure. And thanks to the group here joining us. Thanks for the questions that came in and the interactivity with the polling. We enjoyed it very much. So with that, I'm going to wish everyone a great morning, afternoon or evening and turn it back over to Aditi to round us out. Thank you.

Thank you so much, Atul and Wally, for sharing these incredible insights. So audience, before you go, just a quick reminder, you can download the presentation slides and other helpful resources from the bottom of the page. Lastly, please rate this session. [Operator Instructions] All right. With that, I would like to thank again our presenter. And of course, thank you all for joining us. Have a great day. Goodbye.