Paul R. Daugherty Radically Human

It's a pleasure to be here, Aiden. Really looking forward to the conversation.

Yeah, that was an amazing event to receive that from the Taoiseach a couple years ago and yeah, that's another one of my connection points to Ireland, which is another reason why it's great to talk to you.

The books and really all the work that I've done ties back to experience back in my university days, a few decades, several decades ago now. Where I got into AI and also cognitive psychology and some fields which we can talk about a bit more, but it was really formative experience that really set me on this path of understanding the impact that technology has on people and how people use technology. And that's been at the heart of everything I've done over my career. And that's what led to Human Plus Machine, Radically Human and all the work that I did with the fundamental premise that technology is an amazing tool that allows people to be better. And I don't think AI is any different than any technology that came before. So what we're really focusing on with the research and work we're doing is how do we use this technology to help us as people work, live and play more effectively than we could before. So that's what set us on the course. I think what are the points you were mentioning in the intro there was that there was an interesting point between the first book and the second book. Which is in the first book, we talked about some research where companies that were the leaders in applying digital technologies, including AI, were outperforming the rest by a factor of two X in terms of revenue growth and other metrics, and that was impressive in and of its own right. Enough of a reason to put this on the agenda if you're a business or organization leader. But then what was interesting is after the first book, we did some new research, and this is talking to thousands of companies, 8,000 companies in the first iteration of this study, over 4,000 in the second iteration of the study. I think it's one of the largest ever, maybe the largest ever study like this done of the use of technology and businesses around the world. And we were really surprised the second time, this was after COVID, and we found that those leaders were just achieving a 2x performance advantage. It was 5x, it accelerated. Leaders were accelerating, and that was really the insight that led us to write the second book, Radically Human, which talked about the need for every organization now to figure out what this meant to them and how they could get an advantage. And we covered a second category of companies called leap progress, where they were using the technology to go from being laggards to change the game in their industry. So, and that then led to, with generative AI and everything else that came, that led to us writing the third book, which was the updated version of Human Plus Machine, because we believe the premise of that first book are more true than ever in the era of generative AI that we're moving into.

Yeah, no, and there was an interesting dynamic. It was definitely an accelerant, but there was also another thing that the pandemic did was when the tide went out, so to speak, it exposed the shaky foundation that many organizations had. Everybody had been talking about digital and building digital and all that sort of stuff. But when you were forced to create digital supply chains, when you were forced to do touchless contactless payments, when you're forced to allow all your employees to work from home, companies realized, hey, maybe I'm not as digital as I thought it was. And that's what led to the acceleration. So is that realization that we're not really where we need to be. It's like to extend the metaphor you were using earlier. It's like the emperor had no clothes in some respects as companies looked at it. And so we saw that coming out of that period, there was a 63% increase in technology adoption, meaning 63% of companies that weren't previously using some of these technologies adopted it very quickly. And that's the kind of acceleration that we saw.

We do this technology vision every year. I've been doing it for well over a decade. And it's been a remarkably accurate forecast of the trends that will impact business and that business leaders need to think about. And 2024 was a particularly important year, I think, from a number of perspectives. And I think before I talk about what the trends are, you have to back up and realize that this is a really important moment, an epochal moment in human history, I believe, in terms of what we're experiencing with generative AI. It isn't just another technology, it is, simply put, the most powerful digital technology of the 70 year computerization era that we've been in because it has this more human like characteristic. And it's already started to make massive changes. I think most of your listeners, I'm sure, have played around, experienced written a Shakespearean sonnet or something with ChatGPT. So you see the power and the power of this to transform business, transform organizations, transform the way things work is really stunning. And I think it's what I can, it's one of the four, I can come back to this. One of the four points that really surprised me in my four decade career with technology. And so we just have to put that in context. And then when we talked about the vision for this year, we singled out a few trends that I think are really important to tune into. One of them is AI, and we talk about a match made in AI is clever phrase we use, but it's talking about this human plus machine nature of AI now supercharged with the human like capabilities of generative AI. And we'll talk a lot more about this during the podcast, I'm sure. But that was the first trend. And we've all seen this one firsthand. The second trend we talked about was something we called Meet My Agent, which is talking about how AI would become embedded in agent personas so that you could interact with them. Not just prompt and ask a question, but you interact with, in my case, I can interact with a coding agent that can help me code or a testing agent or a research agent that can look for information. And this rise of agentic computing is really what stands to transform the way businesses work, more than anything else. The third trend we talk about is the space we need, talking about how digital will extend itself to three dimensions. We're stuck where I'm looking at you on the screen now, and your viewers are looking at these two dimensional screens. And it's kind of crazy to me that we live in a three dimensional world, but we've just accepted the fact that we can only do digital interaction in two dimensions. That's changing now. We talked a lot about the metaverse from previous years. Apple released their headset not too long ago. Lots of things moving in this space. Meta has the new glasses. Watch this space because it's inevitable and it'll happen faster than we think that we'll be interacting more in three dimensions than two dimensions digitally. And then the final trend we talk about is how this starts to move to the edge and impact everything we do. And we call this trend the body's electronic or the body electronic, talking about how devices and instrumentation and awareness of every move we make and ubiquitous computing in the environment around us really has some risks we need to manage for obvious reasons, but also stands to really add a lot of opportunity for us.

There's a variety of thoughts on that one. The phrase never waste a good crisis comes to mind. And that's, I think what a number of leaders used to their advantage back in that period. COVID and the period following it, there was one CTO of a large manufacturing organization that I talked to and worked with. And they had visions and plans of using virtual reality, augmented reality to do inspections of their plants globally. So they didn't have to send experts flying around the world, could never get it off the ground, could never get adoption until COVID happened. And then by the way, they had a proof of concept. They had a minimum viable product within a couple of weeks. They got this new thing up and running and were using this new virtual augmented reality approach to do things and it took off and became the natural way to do things. And another technology leader told me that COVID had given them the business case now for innovation because people were willing to break through innovation. Think about touchless payments and things like that that accelerated during the pandemic. So that's one thing. The other thing I think is in the new book in the subtitle we talk about reimagining work in the age of AI, and that word reimagining we chose very intentionally. Some of the people listening might be Chief Information Officers at companies that would say, you have to think about that. I not just being, I think Chief Information Officer underplays what so many CIOs do. I think it's really a Chief Innovation Officer. It's the Chief Imagination Officer. 'Cause I think the technology leaders, whether you're CIO or CTO or whatever role you might have, need to be the evangelist and bring the imagination across the organization so that they can help the other leaders, help the C suite imagine where to go with the technology. And we're seeing that happen now with generative AI. There's been a shift when ChatGPT is less than two years old now, or it'll be just about two years old when many viewers are watching this. And it shifted. A lot of the initial, we did our surveys, all the research we do. Initially, people were saying, hey, this is great. I can cut these costs or I can do this process more efficiently using generative AI. But that's flipped. Now we see 60% of leaders looking at generative AI and the technology as a way to drive growth and improve their business, drive growth, and drive higher pricing, margins, et cetera. And that's an important shift because it reflects the fact that people have gotten past the maybe more mundane and obvious use cases to look at how this really transforms their business in a more fundamental way.

Yeah, it's funny you mentioned Field of Dreams. I just rewatched that about two weeks ago. It's such a phenomenal movie and a good message, as you say. Timing is the hardest thing to get right in tech, whether you're a leader, a CIO or executive investing in technology or an entrepreneur, an investor, the timing is everything. And that's hard to get that right, which is what I'd say with the technology that we have today, the phrase I use a lot is it's a participant sport, not a spectator sport to really engage and understand whether the timing's right and understand whether you can scale into your business, you need to be using it in some way and diving in, you can't just sit on the sidelines with this. So it's been true with many technologies, but I think it's particularly true with the AI technologies of today. And I would say that applies on an individual level, not just an organization level. For anybody listening to this right now, I challenge you. I hope everybody's using generative AI directly, whether it be they're using cloud or Gemini or notebook or ChatGPT, whatever you choose, Perplexity, use it, use a couple of them, because I think you'll see differences and you'll learn a lot and then figure out in the organization where do you get started. And the formula that I'm seeing for organizations that works pretty well on this timing issue is think about the no regrets things that you could do easily. Now, if you're a Microsoft Teams user, turn on the Teams Copilot, things like that, and organizations are doing that. Without those, the easy use cases applied in areas where there's clear value, customer service is an area where we're seeing broad and really rapid experimentation and then starting with adoption of the technology. And then as you're thinking about those types of cases, I can talk a lot more about what the table stakes cut kind of first category is, think about that second speed where you can gear it up and differentiate and drive your strategy and delight your customers and do things your competitors can't. That's the strategic level. And that might be drug discovery in life sciences. It might be capital projects, breakthroughs in the energy or other capital intensive industries. It might be new levels of personalization in retail. And understand, can you do those? And where are you on the timing curve for those things? And looking at those levels is what should I do? Start with now that gear speed one, so to speak, and when and how do I accelerate into that strategic higher gear.

I've been, there's been kind of a second punch, so to speak from that. We talked earlier about the acceleration of COVID. Generative AI has thrown a new thing in there, a new level of awareness. So in companies that are looking, saying, even with my acceleration after COVID, boy, I'm still not where I need to be to really now add in the generative AI capabilities I need. So it's really like the second punch or the second moment of awareness for a lot of organizations. And we call it, I call it the digital core. Like, where are you with your digital core that runs your business? Every company is a technology company. I've been saying that for 10 years. And it's becoming a little bit more adopted now, which means every executive really needs to be a technology executive, be literate to some respect, and you need the right modern foundation going forward. A digital core now, when you move into the AI era we're moving into, needs to have some new characteristics. Data becomes more important. Not just your transaction, your structured data, your data lakes and such, but the unstructured data. Have you mastered synthetic data? Do you know the synthetic data? Meaning the data that you artificially create, but that you can use to train your systems better or drive your business better. Do you understand that? So where are you with the data agenda? Most companies are quite a bit behind. That's the first part of the digital core. The second part is your application foundation. Are your applications ready for generative AI? Some of them may be trapped in legacy, old legacy systems, or you might be on old versions from some of the enterprise software providers. Well, guess what? They're not providing generative AI on the old version. So if you don't upgrade to the latest SAP, Salesforce, whatever you might be using, you can't get the generative AI. So there's the data, there's the applications, and you need a new architecture. The generative AI and the new AI technology are changing the architecture, your architecture, more than any technology probably since the introduction of the PC and the network. Do you understand those new changes, new layers, new modules, and things you need in your architecture? Things like switching capability to move between different foundation models? How do you integrate different models and ensure they use data consistently to drive the right quality results for your organization? Do you have the guardrails in your architecture to enforce responsible AI? This is a technology issue, not just a policy or principle issue. That's the architecture piece of data applications. Architecture, less than 15% of organizations from the work I've done and the surveys that we've done have that modern digital core in place. So most have a lot of work to do. It doesn't mean you shouldn't start down the path and most are starting down the path, but you have to in parallel build that foundation for the future.

Yeah, this is an interesting one. The answer is different for different organizations, but I think there's some. There's been two big problems for CIOs, and I agree with you. I have many, many CIO, CTO types of friends. I think it's one of the hardest jobs out there because of the increased pressure for innovation combined with the reality of what you have to run today, like keep it all going. And the two things that have been almost impossible for many technology leaders to do is build a business case to modernize, because modernization on its own, nobody wanted to pay for unless there was some other business benefit generated. The second thing that was hard to justify were just data for data's own sake. So as a result, people, companies have under invested in their core applications and architecture. They've under invested in their data. I think the only advice I'd give is that I think the fascination and interest in this new technology now is, like I said earlier with the COVID example, it's giving a new lease or new business, a new life to business cases around this. So how do you tie in some of the modernization along with the value you're driving as you implement some of the new capability? For example, if you're implementing some new customer service agent related capability into your front office, which many, many organizations are, I can give you some examples in a minute. Then how do you upgrade some of the data and the data capability at the core of it, which we're seeing organizations be able to do. So that's one thing, it's giving it a new way to package how you drive the innovation and business case to tackle some of these would have been intractable problems around modernization and data. The second is that the new technology provides you with new techniques to use. One thing we're seeing that's interesting in the mainframe area, as an example I'm sure some of the listeners have mainframe systems still out there. There's still a lot, a ton of mainframe systems out there driving a lot of value for organizations. Well, one of the thoughts people have with generative AI is, well, will generative AI help me move all this mainframe stuff and translate it real quickly? Well, it doesn't necessarily do that better than the prior technologies we had to automate mainframe migration. But the interesting thing is generative AI can really help you understand what those legacy systems were doing. It can help understand the transaction logs, documentation, as well as the code and what was happening, and therefore help your engineers now have an agent that can help them forward engineer or modernize the app more effectively, which is a little different than people think. So generative AI isn't the silver bullet that's solving the migration. It's helping our engineers in this human plus machine way now solve the problem more effectively. So those are the kinds of things that I'd suggest people think about as they look to build this business case to get to the modernized digital core of the future.

And to that point, the question I'll add one more, just in case people use it that way. I think the question that every board or CEO or C suite should ask if they see a generative AI use case coming forward. Hey, we're going to use it to improve customer service. Can you use it for knowledge management? We're going to improve our supply chain over here. We're going to improve IT development with generative AI. The question I would ask, and every executive should ask, is not just how are we achieving that? How is that putting our digital core in a better position? Every step you take needs to move your digital core into a better position. I think that's a question that you should be asking. Otherwise you're just bolting on capability that's going to make you more fragile in the long run. If every step isn't also improving your digital core, preparing you better for the future.

You know, I started saying this back in 2012 when the title of our technology, we talked about technology vision earlier, the title of our technology vision in 2012 was every business is a digital business. And as I went around on the tour, talking to companies about that vision, I was widely attacked at the time because people were saying, why are you saying that? My business isn't a digital business, blah, blah, blah. Other than the industries that were obvious. But within about 18 months, everybody said, yeah, that's right. Like, yeah, that's where it's going. And that's where I started also saying if every business is a digital business, then every leader needs to be a technology leader. Every CEO needs to be a technology CEO because technology, the way you do digital, the way you use technology is going to differentiate your business. And as a leader, you need to understand how that's happening. I think that's become true in many organizations. Just a few examples. I'd highlight Walmart, maybe as one example globally of an organization who's embraced it, going from the bricks and mortar stores of 10 years ago, it's an amazing company. But look at what they're doing now in e-commerce and use of AI in their stores and use of data in very ambitious ways and kind of fighting against Amazon and the other leaders there. Amazon themselves are amazing at technology, obviously, but it shows how Walmart's really transformed. The CEO of Walmart, Doug McMillon, has really embraced technology, really changed his team and changed the whole company approach to embrace technology. I'd look at another example happening right now, the transformation right now I'd say is in the insurance industry. Insurance as an industry I would say over my decades in tech has been a laggard in adopting technology. Generally speaking, insurance, I think most insurance execs would probably acknowledge that they've been a laggard in adopting new technology. It's borne out by the surveys. If you look over the years, this time it's different. Insurance executives that the CEOs I talked to in the insurance industries are really looking at technology as a difference. How does this impact the way I look at risk? How can I use AI models in new ways? Insurance is largely a language based industry, it's policies and it's communication with customers and it's regulation and stuff like that. And so it's really ripe for this new development in technology. So senior leaders are leading and they're learning about the technology. They're meeting directly with the AI companies and it's this change coming that's cycling through the industry. So those are a couple of examples. And this is really true in every industry that the differentiation going forward is going to be how companies really put this new digital core in place and use AI. We're entering this AI era, use the technology to reimagine the way their companies work. And that's what comes down to leadership.

We started this in Radically Human, and then we built on it even more in the new book. But if you think about automation and people using technology, you can go back through generations, you can go back industrial agriculture, you can go way back with technologies, but look back to the industrial age. It was machine led automation. It was assembly line standardized processes. Frederick Taylor scientific method, if you're familiar with these things, that was the machine led era that we started with. Then the second era came about with computerization, that I alluded to earlier roughly 70 years into the computerization, which gave us a little bit more collaboration and processes and such. And maybe you got to have human plus machine in some cases, still a lot machine led in terms of the technology, but the technology fused together in different ways. And in that era, what really mattered was we're moving from standardized manufacturing types of processes to human plus machine processes that were put together, but there were still pretty static processes. Now, in this third era that we're just moving into, it's really human led. It's more adaptive processes. And that's really why generative AI is so exciting because it's technology that has many human like characteristics. It's got human like capability in certain areas. So rather than processes and interaction being defined by keyboards and different primitive ways of interaction, it's human level interaction with the technology that we're dealing with, which can really give us superpowers in terms of the power of people and the abilities of people. And it leads to the need to change the way you think about processes. And that's again why this word reimagination comes in there. And we talk about this radical change in the way we do things. A good example of that human led thing, we talk about is a very simple example is in the design field. There's a chair called the Elbow Chair, which won a lot of international design prizes several years ago. It looks very different than any chair you've seen, but it's structurally amazing in integrity. The design is beautiful and it was designed in a human led way by a designer using generative AI, in this case from Autodesk. And the designer talks about this and says he would have never come up with the variations that the generative AI came up with. But it was then his human prompting, then his human led filtering and exchange and finalization that led to the award winning process. And that's what we mean by kind of human led and more adaptive in the way it works.

This is a long topic, but the reason, it's the reason we wrote the first book, even before generative AI came along, because there was in the mid, in when, with the advances in deep learning in the early 2010s, 2013, 2014, same discussion was happening. And in fact, Geoffrey Hinton, who recently won the Nobel Prize, Jeff Hinton famously said in that era, we need to stop training radiologists today, because in a few years, we will need none of them. Brilliant guy, won the Nobel Prize, totally wrong on that topic. I think a lot of people misinterpret that. The way that this technology will really be applied in the way that people work. And that's why we wrote Human Plus Machine, because we think we need a roadmap to think about how to apply this to achieve the right impact. We also did some research and we're publishing this in some new articles coming out in Harvard Business Review. And there's been some other studies from MIT and Stanford and other institutions about why human plus machine leads to better results. And one of the examples we talk about is in a chemistry type of process, a researcher on their own had about 60-ish percent accuracy and ability to get something done. AI model in this case, using something like AlphaFold from DeepMind from Google was in about the same range. A little bit better, but about the same range. Together, it was like 90 plus percent accuracy in what they could do. And that's been repeated in many, many fields. And it's subject of a great paper from MIT coming out now. And that really is the roadmap. We're trying to provide a human plus machine. If your goal is to use generative AI and try to automate what your people currently do, go for it. I'm sure you can do a lot of that. But is that going to position your business better? No, because the real, I believe the real differentiator will continue to be how your people use the technology in driving your business. And that's what organizations need to focus on. I, another thing I've been saying for a long time, for about 10 years is, the companies that will be successful are those that invest more in the people than in the technology, as much as you need to invest in the technology. So that would be the philosophy. Now that said, there's some big problems that we have as a society and as organizations to face. I believe there'll be more, there'll be more than enough jobs. And just look at what the internet was supposed to do in terms of displacing jobs, and look at the number of new intrapreneurs on platforms like GoDaddy and eBay and other platforms around the world. Millions, millions, millions of new jobs and businesses being created in new ways because people are empowered with new skills. That's going to be multiplied exponentially with the human capability of generative AI.

So the only way you have to worry about the net total of jobs is if you believe every job has already been created. It's a zero sum game. We're just automating the jobs of today, and I don't believe that's far from the case. I think we probably only invented 5 or 10 percent of the jobs, the jobs and services and products that we as humanity need. So that's the philosophical type of answer to it. But the practical answer is then to like work on the human, the missing middle and the human plus machine jobs that will create the opportunity of the future. The other thing I'd say though, a big issue is we already have, and this isn't being talked about enough. We already have too many people on the wrong side of a digital divide. Whether you look at global South versus developed economies, or you look in individual countries, socio economically or by different groups from an inclusion, diversity perspective, we already have a digital divide that's unacceptable and leading to political issues and unrest and various things that gender value and all this technology will make that worse if we don't do more about it. Those on the wrong side of the digital divide to start are going to be further on the wrong side, and that is a big problem. I don't think that's being talked about enough. We're so distracted by artificial general intelligence and all these other things that I don't think are the real issues, that we're not spending enough time on the basics of let's help those on the wrong side, digital divide, get the right skills so that they can catch up and be effective going forward. It's one of the reasons why with all three of the books we're donating, all net proceeds, all royalties to non profits that are helping mid career displaced workers, because we believe those are the ones that need the help most. And we've been able to support many great organizations with that along the way.