Technology Strategy 2023: Into the Metaverse - CXOTalk #748
The future of technology, how will it affect those of us in the enterprise? What will the impacts be on consumers? Paul Daugherty is with Accenture. Paul, tell us about your role, your work at Accenture. Technology is reshaping the world like it never has before. I think of my role and Accenture's role as helping navigate through that.
There are really three things that I think about when I think about our business. It's our clients, our people, and our partners. Our mission is to help our clients achieve their goals and harness technology to achieve their ambitions. We're almost 700,000 people strong in Accenture right now and well on track to our goal of 50/50 gender diversity by 2025. That's a passion of mine as well: gender equality in computer science and tech with external roles on the board of Girls Who Code and other things in addition to our internal efforts. Then there are our partners. The technology
industry, what this is all about is really leveraging the power of all the amazing innovation across all the technology companies (the hyper-scalers, the enterprise application companies, the startups, and others) in helping leverage that technology – the first thing I talked about, which is the clients – in helping clients apply the technology to improve their businesses. That's, in essence, what it is. The formal title is Group Chief Executive of our Technology Business, which is about $38 billion of Accenture's revenue and about half of the people that I mentioned. I also have the title of Chief Technology Officer, which is about setting the technology direction.
It's all those partner relationships that I talked about. It's the roughly $1.1 billion a year that we spend in research and development on improving our business as well as the venture investing and things we do to drive innovation. Paul, you have just written this book. It's called Radically Human. It's an interesting title what does Radically Human have to do with technology?
Radically Human is the second book that I've written. The first was Human + Machine, which I'll refer to a little bit. What we're talking about in Radically Human is a step beyond where we went with Human + Machine. With Radically Human, we're talking about
what happens as technology itself becomes more human-like and how does that create the potential for even greater radically human potential capabilities that we can develop in ourselves. That's kind of the balance we're looking at. I view it as kind of a third step in the evolution of how we use technology. The first step was people using technology. We had to figure out how to use machines and green terminals, punch cards, and things back when I was initially programming. For a long time, we've been subservient to these machines. If you think about IT programs, it was about investing millions of dollars in change
management to try to figure out how to train people to use all this complex, clunky technology. We moved from that to the human plus machine era, which is what I talked about in the last book, which is about leveling the playing field between humans and technology and creating this human plus machine symbiosis, so to speak, that we talked about in that book. With Radically Human, we're talking about the next step now, as technology really becomes more human-like in a lot of different ways that we can talk about.
How do we take this new step into a really radical human era, which is really truly augmenting and maximizing the human potential in us all using technology in a more effective way? How do we accomplish that transformation that you just described? We talk in the book about some research we did. We did what we think is the largest ever research study of how businesses around the world are using technology. The largest-ever study of enterprise technology; we surveyed 8,300 companies. We asked all sorts of questions,
and we found out some surprising things in the course of doing the research. Another interesting thing was the research started before COVID, so we had a cut of the research before COVID. Then we repeated it more recently as we move to the post-COVID era. The dichotomy that we saw was really striking and it reflected this change in how technology was being used that we write a lot about in Radically Human. Before the pandemic, our studies showed that the leaders, in applying technology, the digital leaders – there were about 10% of companies that we identified in this category – were outpacing the rest by a factor of 2x, which was striking in and of itself. It was the way they were using technology that was doing that.
Then we redid that research, and we didn't know what we'd find – to be honest. But what we found really surprised us, which was that that gap had widened from 2x to 5x. The top 10% were now outperforming the rest by a factor of 5x. Again, it was how they were using technology.
We saw things like new technologies being adopted at a 70% greater rate during the pandemic than before, the pandemic being this forcing function around innovation. We talked about compressed transformation; companies needing to figure out how to leapfrog and become leaders in technology faster. That's the whole shift that we experienced. Then Radically Human, what we're looking at is, what are the underlying patterns in that shift? One of the things we talked about is what we call the IDEAS framework. It's an acronym. It's I-D-E-A-S. It stands for innovation, data, expertise, architecture, and strategy.
What we're positing with the book is that you need to invert the assumptions you have on these five areas to really look at how you maximize your leverage of technology and approach innovation in a different way to create this radically human effect. Can you give us some examples of where organizations are taking this and putting it into practice? Yeah, what we're talking about here is again flipping some of the assumptions around using artificial intelligence and what it means to create these radically human capabilities. We're talking about new techniques like common sense reasoning, for example, moving beyond the machine learning and more basic approaches in technologies that companies may be using and the different results that you can create as a result of this. Companies like Effectiva (who I think you may have talked to in one of your programs) are looking at using emotional AI in this case that we're applying with one industrial equipment manufacturer to look at things like the safety characteristics of their human heavy equipment drivers so that they can understand alertness and awareness and anticipate problems before they might occur. Again, allowing a person to be more effective, to be more human in their characteristics by leveraging technology more effectively, that's one example.
Another example is in the data area. One thing we talk about in data is moving from big data to small, from maximum data to minimum data and back, from big data to small data and back. Big data in the GPT models that have billions of parameters and such are important for certain purposes. But in many cases, what we're seeing, it's the smaller data that truly
differentiates companies. Really understanding what's the small data that leverages you. This can come in a couple of ways. For example, in your supply chain, really understanding the small data that differentiates how you make your products using big data for other purposes, perhaps, but understanding the small data that can drive you to have greater supply chain advantage in the availability and the way you use your goods, for example. Another example of small data is what companies are finding by using smaller data on edge devices to do some of their processing because you can't always get it back to a cloud or back to your data center to process with the volumes of information (as you move to more edge-based architectures). It's a good example of the small data and what you can do on the edge being more important for some applications and the big data that you can use at the core of the organization. Those are a couple of examples. We can go into more, Michael, if it's interesting.
Paul, is the general idea here that technology advances and the way that we gather, use, analyze data, therefore, enables the technology to fit more seamlessly into what we could say the cracks, the crevices, the various parts of our daily life, our business life, so that it becomes more intuitive and, ultimately, therefore, far more useful? Yeah, far more useful. Again, it enhances our human capability. Look. We use AI continuously throughout the day already, so it's already a part of what we do, and it's become part of ourselves. The nature of what makes us human changes as we use technology, or the nature of our human capabilities change as we use technology in different ways. Again, using things like Internet search capability extends your recall powers and your ability to find things, your directional capability, and such (in many ways). That effect just gets magnified as you look at applying those kinds of concepts more profoundly throughout the way you run your business and drive your business processes. That
is what we are talking about and that is how you use the human capability in your organization. The E in the IDEAS framework is expertise. We're talking here about things like machine teaching – not machine learning but machine teaching – which is, how do you use human expertise to train your processes and your systems more effectively? We're talking about an example in the book from Etsy (the e-commerce company) where they use the human expertise of their designers to establish design esthetic, to train and run different design esthetic, patterns, and such that then enhance the consumer experience that people have on their website. It's an example, again, of machine teaching and human
expertise being leveraged in a very different way. I think, as you said, it's really that amplification of everything we do in maximizing the potential of the human capability. At the end of the day, it becomes a lot of the human expertise, the software skills, the ability of humans to work together to collaborate, to understand patterns, to do cross-domain problem analysis, and things like that that become real differentiators. What should folks in the enterprise who are listening do about all of this? What does this imply for companies and the way that we relate to technology and business? It's got a lot of implications for the way you think about things from a business perspective. We talk in the book about, first of all, the ideas framework in thinking about these assumptions differently: the intelligence, data, expertise, architecture, and strategy.
When you get to the strategy, that's one thing enterprises need to think about differently. We talk about three different new forms of strategy that we believe you need to incorporate in the way that you plan for the future. One is called Forever Beta. This is the concept that you're never quite done with your product. Think about Tesla as an example, the way Tesla
works in continual new software releases to your car, changing your driver experience as you go. That's increasingly a strategy and a model that companies will need to follow. Another strategy we talk about we call Minimum Viable Idea, which is taking all these ideas – the I-D-E-A things that we just talked about – and blending them together to create a new form, a new strategy based on leveraging the technology in this more radically human way. The example we talked about in the book is Lemonade, this startup insurer who has crafted a very different business model with engaging their customers in a very different way (leveraging the concepts we talked about in the book) to create a very consumer-centric way, a very different way of constructing insurance products. Then the third strategy we talk about is Collab, which is a play on words a little bit, which is collab, which is like collaboration but it's also collab, bringing the science and technology together, which we see has a big impact as we look at where technology is going. One of the companies we talk about in the book is Moderna.
This was a lesson learned during the pandemic of how Moderna pioneered a whole new science of vaccine delivery through messenger RNA, found the treatment, clinical trials through diagnostics, et cetera, at a very unprecedented pace. And if you look underneath at how they did it, it was a drug discovery studio powered by the cloud, convolutional neural networks, data used in the way we're talking about to drive a very different outcome. That's the example of that Colab strategy to action. To answer your question, it's a different way of thinking about the strategy. It's a much more dynamic living way of doing
your business and IT strategy together than companies have previously done. What about convergence of enterprise technology, consumer technology, and consumer expectations? It's all becoming inextricably woven together, if you think about how technology is proceeding. I'm not sure you can differentiate the two anymore. If you look at what's happening with technology – from artificial intelligence to the metaverse to Web3 to anything that you might mention – the worlds are blending together and you can't think about it as distinctly. I think that creates a great opportunity for CIOs
and for enterprise technology organizations in terms of approaching technology differently. Some of the implications we see (and we talk about in the book and in the other work that we do) is things like the product development and how you shift from an IT mindset and a service mindset to a product mindset and product development mindset, product management disciplines and such, which is becoming essential as organizations look to shifting from IT running the business to IT producing the digital products that enable the business, which is the journey that all organizations are on (to some degree of change) right now. That's a dimension of change. I think there's a whole set of changes around what it takes to integrate consumer experience with the enterprise experience, this whole idea of experience-driven technology, which is a whole chapter in the book. There's a whole chapter in Radically Human dedicated to this idea of experience and creating experiences in the right way. It's really essential. Those companies that create better experiences for your consumers (or your workers, for that matter) will be the winners going forward. That doesn't just mean in the B2C market. If you're in a B2B
business, it's how you work with your business partners and such, create those distinctive experiences in different ways and creating that experience design and design thinking kind of mindsets around it. Again, all organizations are on some stage of that journey, but it becomes increasingly relevant as you look at this. Then there's an element also around trust. Again, there's a whole chapter in the book devoted to trust because, if you think about experience, you think technology, and you think about AI, metaverse, and all these new technologies, the issue of trust increasingly comes up. I would be willing to make a wager with all of you that, as we look into the next decade, the biggest differentiator among companies will be the degree of trust that you can engender with your consumers and workers because the trust is going to allow you to get the data and have the access to create those experiences that are really going to differentiate you. Trust becomes a fundamental capability to think about and really understanding deeply what things that you do increase the trust, what things that you do erode or challenge the trust that you have with all of your stakeholders. It becomes really critical. Those are some of the things
that I think are really becoming center of thinking as you think about this consumer and enterprise blend that's happening. As we look forward, we can get into metaverse, Web3, and some of these things. It's even more pronounced as we look to the future of where digital technology is going, which is increasingly in that direction, as we talked about in the book. On Twitter, Louis Columbus, who is a great software industry analyst, really latches onto this idea of forever beta that you were just talking about. It seems that a fundamental aspect of all of this is really changing the relationship that we have to technology, which for many organizations (for many of us) requires such a huge mindset shift: how we think about the technology, how we think about technology development. For example, forever beta is a different way of developing software. What is, I think, one of the most challenging
jobs of any today is the CIO job because of some of these changes going on. The CIO has to keep everything running and all organizations are so technology-dependent. I think we saw, during COVID, what a lifeline technology was to keep every organization running. The CIO role needs to be so much more beyond that. It's about – as you said, Michael, and the question coming in – innovation. It's about thinking to the new and changing
the approach. It's about inspiration. I'm intentionally using I's with all these. These are the redefinition of the CIO is information and infrastructure to innovation to inspiration; inspiring the business colleagues in terms of what's the power and the new way to apply technology. It's the intelligence officer in terms of understanding different possibilities and what's out there. It's the inclusion officer in terms of looking to create the talent and build the inclusive and diverse talent that you need to build technology properly. I think the CIO role is increasingly stretching along these different
dimensions, which makes it hugely exciting but it also makes it a very challenging role. I think one of the biggest challenges that all of us (that are involved in technology and applying technology) need to really do and think about is helping reimagine what the future can be, reimagine how you can do the business differently because I think, too often, we get stuck in seeing new technology, just pasting it in, and applying it the same way we were doing things before. There's a real need to resist that temptation and look at how we create that change as we go forward, which is why we fundamentally wrote Radically Human. Can you elaborate, Paul? If you were a CIO (Chief Information Officer) today, what would you be doing with respect to all of this? I'd be trying to really create a learning organization because things are moving so fast. By learning organization, I mean a learning
organization within IT, but also cultural change to help inspire the rest of the organization to be a learning organization because, with the technology moving as it is, IT needs to move faster and reinvent themselves every day to understand the new technologies and what the possibilities are. The only way to do that is by creating a learning environment, a learning organization. We have something we call TQ (technology quotient), which is a way we educate, inspire, and inform all of our people in addition to all of our other learning platforms. I'd encourage everybody to really think about that. The quote I keep making is, "You can't go hire the skills of the future because they don't exist yet. You have to figure out how to create them in your own workforce," in addition to refreshing and bringing in some from the outside. Then you need to do the same in the
business because a lot of the next generation of technology is about democratizing technology, access, and impact. It's creating the learning capability within the whole organization, so you're democratizing through low-code/no-code tools in terms of creating business capability. You're democratizing decision-making through data analytics tools and AI tools that are available to more people across the business and such. The single thing I would zero in on is a learning organization. The organizations that can learn faster and spread it faster in
the organizations, I think, are going to be well-positioned for the future. Let's go to some questions from Twitter. Arsalan Khan says, "Large organizations have tons of data that they can use for innovation. How can small businesses compete when they don't have that much data to work with?" I will just add, when they don't have that much data but also their level of sophistication and access to sophisticated skills is less compared to large companies. There are two sides to that. I think large organizations do have access to data
and sometimes more data and such and the skills. But the democratization of technology is real. The capability that you can easily access through the cloud platforms, the AI engines, and capabilities that are widely available is profound, and it really does democratize the access to smaller organizations. Yes, if you're in a really small organization, do you have somebody who has the time to go out and learn those tools? That may be a challenge in some cases. But it's very different than the way it was 5 years ago, 10
years ago, or 20 years ago where you need to stand up teams, data centers, and infrastructure, and make big investments to get this stuff working. You could access a capability through an API, a service, and a cloud platform, and funnel data very quickly to do some very profound analysis. We write about some examples in the book about very small organizations who have taken advantage of that capability to improve their businesses. I think there are two sides to that. Yes, the big organizations have an advantage. But the democratization of the technology combined with some new learning technology – we talk about few-shot learning, for example, which is a much simpler way (sometimes powered by the cloud platforms) to train AI algorithms based on just a few instances of data rather than hundreds of thousands of instances of data – those types of techniques stand to leverage the playing field more.
There's a real opportunity for smaller businesses because it's just so much easier and so much cheaper, for one thing, to build that infrastructure that you were just describing. That's right, and there's a lot of publicly available data and such as well, and different platforms are opening up different sources of data. It's all about figuring it out. Any small business is going to have this strategy of what differentiates you and then figuring out how you get that data, whether it be big or small. How do you access some of that data to create the advantage you need? We have another really interesting question, practical question, this time on LinkedIn. This is from Suman Kumar Chandra
who says, "How can CIOs bring themselves up to speed to decide which technologies will be most useful for their business and how to use them when there are just so many technologies on the market? Every day, new technologies are coming up. How in the world do we prioritize and find our way through this forest of great technologies?" That's again another reason why the CIO job is a challenging one because the old technologies don't really go away. They're still out there somewhere in the closet, and you need to take care of the current and the legacy technologies as you investigate and learn about the new and apply the new. I think there are a couple of things I would say. One is, it is about prioritization,
figuring out those things that do make a difference to your business, and in channeling or in prioritizing and allocating the appropriate amount of resource based on that. For example, if you take different categories of technology (with cloud technology), we see many organizations, most organizations moving rapidly to cloud. Roughly, we see most organizations having about 30% of their workloads in the cloud – on average. A lot left to go with the cloud migration still. What's important about that, if you look at that 30% statistic on cloud I just talked about, if we ask another question about how much are you really leveraging the power and innovation of the cloud, only 13% of companies say they're really doing that. So, 30% of the workload is in the cloud; only 13% believe they are leveraging the innovation because migrating, getting the workload to the cloud doesn't, in most cases, deliver the full potential yet. That's kind of part of the answer to the question of prioritization. To the extent you can get to
the cloud, which is not just about cheaper compute – it's about standardized platforms that allow tremendous access to innovative capabilities, services, and such – it becomes the platform to drive your innovation at greater velocity. That's one thing, I would say, that we hear from customers (as we help them move to the cloud) is, getting there is one thing but then really growing and innovating the business in the cloud is where they want to go from there. It's kind of prioritized like that. I'd say that's a strategy to prioritize so you can get to a more innovative, higher velocity innovation environment.
You look to things like artificial intelligence. I think every organization needs to be investing in artificial intelligence in different ways. Do you need to be doing, again, the most sophisticated deep learning neural network models in every part of your business? No, probably not, but you should be looking at where it can make a difference. And also, have teams looking at studying the new techniques, studying the new, evolving techniques, the common sense, emotional AI, and other things so that there's some effort being applied on that and some effort being applied on scaling the real capability and creating the centers of excellence for that that you need to drive. But as you look at newer things, metaverse is coming on the scene and probably the most talked-about technology. It's probably the biggest surge of interest we've ever seen in any technology (over my 30 years) has been what's happening with the metaverse (in recent times).
That's a technology where, again, it's not just for tomorrow. There are implications of it today. Depending on your industry and what part of your business you're looking at, there are moves you need to be thinking about today and making today, in addition to areas you need to be learning about and applying. For example, thinking about augmented experiences for your workers and virtual experiences for onboarding employees and training employees, which we do at scale in our company, as an example. That's today. Thinking about at-scale NFT-based products and how you're going to leverage traditional currencies and such, certainly should be understood, and maybe dipping your toe into those as well, but at least in the understanding curve. I kind of look at it as a prioritization across the spectrum and really keeping an open mind as you look at the prioritization. Be sure to subscribe to the CXOTalk newsletter. Hit the subscribe button at the top of our website.
Paul, the metaverse has become very important to you. You just formed, I believe, a new business unit focused on the metaverse. Why? Why does the metaverse matter that much? What we've done, to think about the metaverse, we started this in Radically Human and then we released our technology vision and expanded on this concept. We really believe that businesses
need to frame the way that you think about the metaverse in a different way than you hear about, generally, in the media. The metaverse isn't just about consumers. It's not just about NFT and cryptocurrency. The metaverse is really about the next stage of how we create digital experiences. We call it the metaverse continuum. I'll come back to what I mean by continuum in a minute. That's an important way to think about it. If you reframe your thinking this way, then a lot of aspects of the metaverse become relevant immediately. Today, a fundamental driver and why I believe you can't ignore the metaverse is because it really is driven by fundamental technology advances that are happening. Whether
you want them to or not or whether you like it or not, they are happening, and there are opportunities and generally positive changes that are happening with technology. Web3 is a big part of that. Web3, the technologies coming out about Web3, do enable a new set of experiences that are enabling the metaverse capabilities.
Web1, of course, being search and basic data on the Internet where Google Search was the killer app. Web2 being (about a decade ago) social, mobile, apps, and such came online. You saw the sharing economy companies being the killer apps as 4G and other technology proliferated. That was Web2. Now we're seeing Web3, and Web3 is adding two new capabilities. Web3 is adding the Internet of Place, which is shared, collaborative spaces
not just through virtual reality, but through 2D reality or 2D interfaces as well: phones and laptops. It's also creating an Internet of Ownership for the first time having real distinct, provable, verifiable identity, authenticity, and ownership, which you couldn't have in the Internet where if something forwarded once, it could be forwarded a million times. That Internet of Ownership and Internet of Place is creating a potential for transformation bigger than the jump from Web1 to Web2. That's why I believe the next decade of digital transformation is going to be bigger than the decade before. Michael, you mentioned we just introduced our Tech Vision this year (earlier this month). The title was "Meet Me in the Metaverse."
Our Tech Vision ten years ago was titled "Every Business is a Digital Business." When we said that ten years ago when Web2 was coming online, people disagreed. They thought that was farfetched and such, but it really quickly became reality.
The last ten years have been about every business moving to become a digital business. I think, with Web3 coming, and the metaverse, it really is about a decade of even bigger transformation than the one we've seen because the new technologies fundamentally changed the inside of how you do digital. They don't just wrap digital around what you were doing previously, which is why this is something every business leader and technology leader needs to understand going forward. The final point on it is, what I mean by the continuum, as you reframe it, is it's not just about the consumer things. You read about Bored Ape and different things you might see out there, Roblox and such. It's about employees and what you do. We'll onboard 150,000 people in Accenture using
virtual reality into our Nth Floor metaverse this year. We've hosted our leadership, board, and many clients in the metaverse. And so, it's creating those new experiences for employees and workers, not just consumers, and changing the way you work. It's not just about 3D experiences. It's about the 2D and how you blend the 2D and 3D. It's not just about the virtual. It's about reality, how you combine augmented work and digital twins, edge, and such in to create a streamlined and integrated virtual to real-world and back type of experiences. It's a continuum in terms of
time, as the metaverse, Web3, and these technologies mature over time. If it's not obvious, I believe this is a big deal. I think this is going to be one of the things (along with a few others that I can talk about) that will define the next decade when you think about how you apply technology. What's the timeframe for adoption of these technologies, of the metaverse, do you suppose? Like any wave of technology, it gets a label after it's been around for a while. We were doing cloud work before the cloud became commonly used. It was called utility computing and other things, as you will recall back in the day.
Similarly, augmented reality, virtual reality, blockchain, digital currencies, all the constituent components of this digital twin technology, these things have been around. What's happening with the state of technology is the standards are converging, the scale is increasing, and as some of these new technologies are coming online to enable you to do this in an integrated way as an enterprise. It's already happening. We have companies doing interesting things. As I said, we'll onboard 150,000 people into what we believe is the biggest enterprise metaverse, which is our Nth Floor. We're doing interesting work for clients around augmented reality so that you can use a digital twin to simulate performance and then use augmented reality to change plant performance in real-time, things like that, blending the experiences together. For trading organizations, very sophisticated organizations,
in real-time, using the technology in many different applications. I'd say the retail and financial services right now – an overwhelming interest and a lot of experimentation – the real work we're doing with clients is around creating their metaverse retail presence, their metaverse products. How do they bridge the real products and the digital products? Things like Gucci's purse. Gucci's purse sold for more in their metaverse than it did in the real world. Things like that are raising a lot of interest and attention.
One point that I find very interesting is, you're thinking about the metaverse, thinking about digital twins as part of the talent culture and training because, historically, we've thought about digital twins as being manufacturing equipment, simulating a jet engine or a nuclear reactor, for example. My view is that we're on a journey that's going to happen relatively fast for the enterprise itself and business itself to be a digital twin. The analogy I'd use is to think about an annual report a few years from now.
The annual report today, you read it on paper. You post something online. It's kind of one-way. You think about an annual report where you can step into the company's board room, interact with the executives, and hop off to a retail store and see how consumers are engaging with the product. Then go over to a plant and see how they're manufacturing it. That's not too far in the distant future. Think about the engaging experience that you can create there. As an executive at the company, think about having that same kind of capability, so you can see a digital twin of the business and simulate different kinds of operating characteristics and environments. Change and reconfigure the business as a result. That is the path we're on,
I believe, with enterprise technology. Again, the current wave of digital has been a little bit of wiring the plumbing to allow that to happen. The next stage that we're entering into is a real exciting stage to really run businesses differently. This is why the connection to radically human comes back in because I think this is a really interesting future in terms of the potential of workers, the potential of leaders, the potential of consumers to use services in a much more compelling way. Wayne Anderson says, "What kind of antipatterns
have we picked up during this tech and human symbiosis that will hold us back from developing as radically human, as the way you've been describing it?" What do we have to change? That is something that we need to work on. To be honest, that's why I wrote the first book, Human + Machine, because I was concerned about the antipatterns and we wanted to put them out there and show how they could be corrected. A lot of the work of Human + Machine was showing positive potential and how to overcome things like the fear of the technology, the fear that it was going to put everybody out of their jobs, the fear that it was going to take over the human race and all these things, which we fundamentally don't subscribe to. I think some of those same patterns are still out there because there's a fundamental human behavior that we're the ones who create technology, so technology doesn't create itself. We create technology, and then we fear it. [Laughter] There's this dynamic with every technology that comes about. We're seeing this with metaverse right now and a lot of backlash against metaverse
(justifiably so) because there are a lot of real considerations around trust and privacy, inclusion, children's access, and many other things that are real concerns in the metaverse. But that's true of the real world. It's just a mirror of the real world and a mirror of us, and they're all things that we can deal with in the right way.
One of the things we talk about in Radically Human, and we also talk about in our Tech Vision, is this idea of responsibility and trust. We believe that, in the Web2 era, as an industry or as a technology industry, we didn't start Web2 with enough focus on responsibility and setting it up to avoid the antipatterns, as you say. We're at a point with Web3 and metaverse where we can get it right this time. That's why we (as an organization and individually) are really
focused on all the elements of responsible metaverse, which is trust and safety around use of the Internet. It's data privacy and identity, which again there are very promising solutions to with the Web3 technologies. There's inclusion and behavior on the metaverse, and a variety of other things, that we all need to focus on. I think it's all of our obligation to really study and understand these things so that as we build our early forays into metaverse (or with any new technology), we're applying it in a responsible way and avoiding, as you say, the antipatterns. The other big issue that I've been very focused on for a number of years, and still very focused on, is how we create the right environment and skills to enable this in a lot of those that don't have the digital skills to succeed in the new environment. That's why we've been very focused on reskilling and talent creation. How do we create people who can build these new
jobs that don't exist at scale yet: the world builders of tomorrow, the AI ethicists, and such? That's why we're donating all the net proceeds and royalties of Radically Human to organizations who are focused on reskilling those who don't have current access to those skills that they can have viable skills in the new economy that's being created. Bhagya Subbareddy says, "Can you share a few tips with CIOs on how to bring business along the journey of the reimagined future?" Specifically, she's talking about managing change for large, global organizations that are risk-averse. Two things: One is education, and really just focused on the education because I think that goes a long way, education case studies, and ways to help people understand the impact. The second is advice that we talked about, as we're releasing our Tech Vision, which is an approach of think big, start small, and scale fast. Think big and educate around the big potential. Start small and in a focused way
where you can prove the results. But do that in a way with the architecture, the foundation, and the approach that you can scale it fast and meet the expectations of the business as you go. Arsalan Khan comes back, and he says, "To be a data-driven organization, we need to have culture and executives that have no veto power over data recommendations. Do you agree or disagree?" You need a clear and unified data governance approach, so it's clear who can make the data decisions. I think, in too many organizations, the data governance is fragmented and not clearly
understood. And so, you end up with different versions of the truth and all sorts of things. I think I agree with the question, but I think it comes down to a single, agreed-upon data governance approach. Shailesh Pachori says, "What is the mantra for success when it comes to adopting current technology?" What's the foundation of how to be successful? Business value and human acceptance, so implementing something that clearly improves the business so people see the value of doing it, support it, and rally behind it; and the human acceptance, doing it in a way where some of the constructs we've talked about today – the machine teaching, the experience, the trust – are thought about in a way where people are accepting, using, and maximizing use of the technology to increase their own potential. Okay. With that, unfortunately, we are out of time. We've been speaking with Paul Daugherty. He is the group chief executive for technology at Accenture. He is also Accenture's chief technology officer. He wrote this book. It's called Radically Human. His previous book was Human + Machine. With
that, Paul, thank you so much for coming back and being a guest on CXOTalk again. Thanks. It's always a pleasure talking. I look forward to the next time as well. The time really flies, so thanks to you and your audience. Speaking of the audience, thank you, everybody, for watching, and especially to those folks who ask such insightful questions. I love the CXOTalk audience. You guys are so smart and so experienced.
I certainly learn a lot from your questions. Before you go, please subscribe to our YouTube channel, hit the subscribe button at the top of our website so we can send you our newsletter, and keep you up to date on who we have coming up as guests. Thanks so much, everybody. I hope you have a great day, and we will see you next time. Bye-bye.