AI in the Workplace: Michael Horn Hosts WGU's Future of Work Panel

Michael Horn Here. What you're about to hear is a conversation that I moderated at a meeting that Western Governors University pulled together in Boston in June around the future of work and learning. Represented on the panel were leaders from a variety of industries consulting, data and cybersecurity communications in conversation around where the future of work is going in a world of AI and what does it mean for education, both learning and training? Where is the puck going and what does that mean for threats and most importantly, opportunities for higher ed institutions and learners in their careers. Joining me on the panel were Paul Bingham, the senior vice president and executive dean of the College of Information Technology at Western Governors University Will Bass, formerly of Comcast, who is now working a portfolio of gigs Kara Witkowski, a senior principal at Accenture and Wayne Duso, who serves as an advisor to a couple of tech companies focused on both data and cybersecurity and who was formerly a vice president at aws. I found the conversation fascinating, so we wanted to bring it to you here at Future youe as a bonus episode for the summer. So enjoy this conversation on the future of work and learning.

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I'm Jeff Salingo. And I'm Michael Horn. We've got folks that are really have their pulse, I think, on the leading edge of what's changing both in the workforce and how we actually equip talent to be able to fill those rapidly changing roles. And I want to frame the future and have you all react to this upfront because there's depending on where you look, there's either lots of doom and gloom around AI and how it's changing the economy and the future of work and the workforce. But there's also opportunity. And so I want to start our Monday morning off with excitement and opportunity. We'll get to the pessimism that that perhaps you have as well, but maybe we'll start all the way at the end, Will, and work our way down. Just what excites you most at the moment about the potential of AI and emerging technologies in the workplace itself?

Yeah, no, I am excited about it. I read something recently from Reid hoffman, who founded LinkedIn, and he says that people break into kind of four categories when it comes to AI. You're either a doomer, a gloomer, a zoomer, or a bloomer. So you're either a doomer and think the world is Ending a gloomer and saying, my career's over and my kids aren't going to get jobs. You're the zoomer saying, boy, I'm latching onto this. This is the future and this is my ticket to greatness. Or you're a bloomer, where you're saying, I see the opportunity here, but you have to be careful and make sure that you lay the right foundations and gradually ease into the future. So I would probably put myself in that bloomer category. One of the things I'm optimistic about is I see AI making me better in the workplace. Some of the things that I've always maybe struggled with a little bit, I've not been great at math. I can plug that into AI, and now I can come up with some financial analysis really, really quickly. And so it's kind of like my secret superpower, so to speak. I also like the fact, having been in the workforce now for over 30 years, that one of the things that's really important is wisdom. So it's not just experience, but the ability to look at what AI tells you, what your outputs are, and then have the wisdom to say, this is how we should use it, this is how we should employ it. And so being able to draw across multiple career experiences helps. Lastly, I think of my wife, who's an entrepreneur, and I'm excited for her because AI is kind of allowing smaller organizations to thrive. Maybe you don't have huge numbers of staff that you can draw on like a large organization, but by using AI, you have access to that information to compete, and I think it's going to be a positive for entrepreneurs as well.

Thanks, Will. It's fun to hear what you're saying about Superpower. I feel like that's helping me, too. Whether it's Copilot or one of the Gen AI models, I do feel like I'm able to jump in and use those tools very easily, and they make me better. They help me put my thoughts together. They give me a starting point. I love your comment about wisdom. It takes wisdom to really review what is coming out of those models or what is coming out of the tools that I'm using. And so I get excited about being able to use it, but also being able to evaluate and put my brain around it as well. I'm a parent of four young professionals and their partners, and as I look across all their career paths that they've started down, I feel like AI is going to make an impact for each and every one of them, no matter which field they chose. And I get excited about that. I also get excited that all of us, I'll say myself, this category, all of us older generation, we can use it too and we can catch on really quick. I've watched my husband, as a realtor in the area, jump in and use it for his role as well. And it used to be that we'd have to teach him technology over a weekend. When he got his new cell phone, it was like, okay, which kid is coming to do dad's onboarding? Because it won't be me. But I think it's a tool set and it's a technology that so many of us can wrap our heads around and can have a lot of fun with as well as get excited about the potential. And in the work world, I'm excited for it too. I think it's gonna move us forward and I get a little worried at times. Sometimes I could wake up a doomer. But at the same time, I think as long as we're careful, I feel good about the tools, I feel good about the technology that's coming forward and I think we're smart enough to really to guide it in a good way, pass it to you.

That's great. So I'm going to also provide a generational reference. So thank you for cracking that egg. Some of us remember, as I do, when the microwave oven came into our lives. This is in the 1970s. And I remember well, I even remember where it sat on our kitchen counter at my parents house. And it was so exciting. This microwave oven showed up. And the promise of the microwave oven was it was going to render obsolete all of your other kitchen appliances. You weren't going to need an oven because you could do it faster in the microwave oven. You weren't going to need the stove because with a couple of little extra implements, you could do everything in the stove. There were all of these things that it was just going to take. It was going to render all of them useless. And over time, what has happened? Well, none of those things became obsolete nor useless. It sat alongside all of them and made them more efficient or made your time in the kitchen more efficient. I remember, wow, you can boil water in this thing. Well, you can, but be careful because whatever you just boiled it in is going to scald your hands when you take it out that glass Pyrex bowl or whatever it is. I don't boil water in a microwave. I don't know how many of you do anymore. Well, now I use, although truth be told, I use an electric kettle. All it does, it added to it. But as the explosion of Genai happened more or less concurrent with the release of ChatGPT to the world, this was the kitchen appliance that started going through my head was, is this the microwave oven of the future that is truly going. That is going to claim to render everything else obsolete, but end up just sitting alongside and making everything else more efficient? Or will it be now it's out of the kitchen, but will it be a smartphone which actually has. If you think of the iPhone or the other smartphones, sorry, anybody who uses an Android which has rendered literally tech obsolete over time, we don't think of all of the things. Almost none of us have cameras anymore. We don't use a separate GPS in our car. We don't have to have a separate. You get the idea. And I've asked people over and over time like, well, what is it? Is it the microwave or is it the smartphone? And in either case, I agree with you, it's here to stay. And I would put myself in the category of this is pretty awesome. Let's see what we can do with it.

I think it's going to be both the cell phone and the microwave.

Yes.

So my perspective is as a lifelong product builder, so I'm a technologist and I'm an optimist and I don't see how you can approach any new technology without some level of optimism. There needs to be some caution as well. But I am, by all stretches of the imagination, super optimistic about what we are doing with today's AI. We talk about AI like it's brand new. It's not, it's 70 years old. If you want to go back and look at the original papers, we've been hacking at this problem for a long time. We just happened to hit its really interesting inflection point in November of 2022. And the inflection point was even more pronounced because it came at a consumer level, just like the smartphone did. If we tried to introduce smartphones into corporations as a business to business device, we'd still be on the BlackBerry. Smartphone would have died. But we introduced smartphones really to consumers and consumers really do drive a lot of behavior. With ChatGPT, that happened even more. So every CEO in the world thought they were behind before we even started. We had this hype cycle that's bigger than anything we've ever seen before. I'm still an optimist. I look at AI as a means to accelerate solutions in the same way. And yes, I've been around for a while too. The same way that the Internet accelerated solutions, the same way that cloud accelerated solutions. AI will do the same. Some people say, what about mobile? Didn't that accelerate solutions? It really was built on cloud and built on devices. So it was a little less of an accelerant. Might have been an accelerant in terms of starting fires, but it was much less of an accelerant than the other technology changes. If you look at AI in the same way that we looked at the Internet, in the same way we looked at cloud, and if you haven't studied those, it's worth studying, especially when you think about how you have to also help change the world. It is like that. The difference. That first one I mentioned, it took about 15 years to really catch on. The second one took about seven to 10 years to catch on. This one depends how you want to measure it. But it's three to five for it to catch on. So the challenge isn't so much that it's super scary. The challenge is it feels scary because it's super fast. For all of you. If you think you're behind, be less paranoid. For the one or two you think you're really ahead, be more paranoid.

I like that quote where you just left. It feels scary because it's super fast. I want to get into where you are. Like, you know, you mentioned that you do have your moments, Kara, where you wake up a little pessimistic about where this is all going, specifically where you're nervous around what AI and automation might do to the nature of work or workforces or the volume of jobs and things of that nature.

I'm just.

I'd love to hear that soundtrack and then we can sort of dig into the specifics a little bit more, maybe. Wayne, why don't you take us off here to start and then run down again?

Yeah, I'd start off by. And again, I'm coming at this kind of as a product builder, but what I do is I see this, again, raising the bar for talent, raising the bar for leadership. If you look at what I said a minute ago in terms of speed, there was a point where when you introduced the technology, it took a generation for that technology to flip over something else. And a generation is about 15 years now. That seems like a really long time today. Like, nobody can even measure 15 years. But if we go back and we look at technology and say, when were they introduced? When did sort of the visionary folks start using them? And then when did the majority of folks use it? And the majority is not a bump. Right. It's a long plateau. It's a generation now. It took me a while to kind of figure this out. I'm like, why was that? Well, if you look at the workforce, people don't change. Usually when you get out of school, you know something really well, doesn't matter what you do, you kind of like to carry that through and you get incrementally better, but you don't get radically better. And so 15 years later, that technology flips over. You're 15 years into your career, you can pretty much ride the next 10 years on that transition and you'll be okay. That was true when I started my career. Right. It was also true that it took like three years to launch a product. So that doesn't happen anymore. So if you roll forward to the Internet and to cloud, that was about seven years. It took about seven years. So kind of in your career you had to flip twice. Some people didn't and they just left their career and some people did. We're at a point right now where you have to flip your knowledge base roughly every five years. And if you don't, you're going to be left behind. So how many times is that in a 40 year career? It's every day. Right. So I kind of look at where we are today and I think of this in that perspective. I see this is how this technology is transforming how we think, how we mentor, how we sponsor, how we engage.

Thanks. I promise I won't have bad analogies for our whole time here, but there's another one that is seemingly relevant to me and in terms of the job anxiety. And I think the first time I read it, although I've heard it several times now, was in an IBM report on AI that said IBM AI won't replace jobs, but people who know how to work with AI will replace people who don't know how to work with AI. And again, it's been restated several times. I was thinking this morning about this, kind of like, well, what do you think about AI? And while it might surprise some of you, I was not alive when the 1800s became the 1900s. But I suspect there were a lot of conversations somewhere around that point in time about, what do you think about this Ford thing? And I can only imagine all of the people who were stagecoach drivers and streetcar drivers, I don't know what they call them, pilots, coaches, people, coachmen, blacksmiths, who were all like, well, we're hosed. Like this whole Ford thing is just going to ruin everything. And certainly I don't know if any of you have in your current family any Stagecoach drivers? Because Ford did change the landscape of transportation, but somehow there are still jobs for all kinds of people. Because rather than being Stagecoach drivers, I was thinking about as I jumped into an Uber this morning, I was like, well, it's of kind, kind of like a Stagecoach driver, but way more comfortable for all of us. But I think it's just the nature of the work will change, but there will still need to be work. God bless them all. There's used car salespeople, there are new car salespeople, there are mechanics. And so again, the jobs I think are changing will continue to change probably more rapidly, but there's just going to still be opportunity for people willing to work.

That's a great, great thought about the Stagecoach drivers. Wayne, I like how you said flipping your knowledge. Sometimes it feels like we're flipping so fast that we're getting dizzy. Right. That we are asked to flip and flip again. But I do feel like the job changes will be about skills. And so it becomes how can we train our teams? How can we help people get ready for new jobs or for jobs that require new skills? And I think it's about the skills. I also, I like to think that it's opening up a lot of creative areas as well. There are kids that are going to really enjoy building models and doing some of the tasks that it requires. I think we're going to need our critical thinkers, right? We're going to need our teams to be really critical thinkers and make really good decisions about where AI is used and what, what is changing and what does that mean and you know, trying to make sense of it all. And so I think too what we see, I know I work with a lot of our state government clients and our higher ed clients and when working with state government they have a lot of vacancies right now and they also have a lot of folks that are going to retire. And in those, you know, when those folks leave, we may not need as many of the vacancies filled, but we sure as heck are going to need the current teams and their expertise of how work gets done as well as we're going to have to give them some new skills so that they can do work in a faster, smarter way as expected. So that's kind of my take on

it for jobs Will, thank you. So I'll indulge my pessimistic side here for a second and think about it was mentioned that I advise different organizations and proud to do that. But my most important advisory assignment is for my 20 year old daughter who's a sophomore in college. And so she's saying, dad, I'm not sure I know I want to major in business. Should I go into accounting or should I go into finance? Now I should be the perfect person to jump on. I have a 30 year career in business and I know career paths. But because of AI I'm like, you know what, I don't know. Accounting seems like that could be disrupted pretty easily through technology. But I hear there's a lot of openings right now for accountants and not enough people are majoring in IT Finance. That's sort of flying over things with strategic decision making with data. Maybe that's the better place. I don't know is the point right? And so how do I provide the best advice to as a manager, to my employees, to my own daughter, to organizations when things are changing so quickly and that's a bit uncomfortable. The second thing I'd say is there is a downside that AI can be a distraction. So for all the things it can do to help make up time, it can also waste time. So I think of Johnson and Johnson had this strategy called the Thousand Flowers. And what that was is going out and looking at every place that AI could make a difference in the organization. And so they had 900 and some odd, almost 1,000 of these little flowers that they were investing in. And when they went back and looked at it like 10 to 15% actually added any value and the rest were not adding value, taking time, taking resources, taking mind share. And so they pivoted and said we're going to focus on the critical few. How can AI help with drug development? How can it help with chatbots for our sales reps or for our HR folks having that focus. And I think if you get so caught up in I could do AI here and I could do it here and I could do it here, you might find that you're wasting the very time that you think AI is going to be gaining.

You will where you just left. That sort of makes me reflect that there's the longer time horizon, right of jobs that could get created. Stagecoach to Uber driver, perhaps with a few waves in between, but also the short term demands on organizations. And that's where I want to turn for this next question, which is how is the organizations you advise that you work with directly? How have you seen them both? Rethinking talent acquisition, but then also development in response to AI and automation, rather than just sort of being passive right around its emergence. Will let's Start with you. And then I want to hear Kara and Wayne on this. And then, Paul, I'll come back to you with the next question.

Sure, yeah. So one of the things from a talent perspective, there's a different trend that's happening that's maybe not related directly to AI, and that's that the desire for employees to become managers is actually plummeting. Not as many people are saying, boy, I want to get on that career path. I want to get that corner office. Well, corner office may not exist anymore. I want to have my boss's job. Do I really? Is that what I want to do? So how do I, as a corporate leader, say, where do I find those corporate athletes of tomorrow that I need to be the lifeblood of the organization? Part of that is if you recognize that hunger in someone earlier in their career, then get them into a leadership role quicker, immediately double down on that and say, all right, normally it would be six years to get this person into a supervisor position. Can I do it in a year and a half? Can I do it in two? He or she has the motivation and the skills and the drive. And so I may need to disrupt what I would normally do to develop that talent and put someone in sooner. And perhaps they can leverage technology to pick up some of the experiences and information that they didn't glean. But I think that's important. I think the second thing is to really be thinking about your talent holistically. Comcast, where I recently retired from, recently moved all of their talent functions together under a single umbrella. So there used to be a talent acquisition team, talent management, learning and development. Each had kind of their own cultures. So now those are being collapsed and say, how do we look holistically at talent? Bringing them in, bringing them up, and moving them out. That almost sounds like a stagecoach kind of thing. Rawhide. But how do we look at that process across the organization and make for a more integrated experience and break down those silos companies are creating? Walmart just created their first chief talent officer, whose role is to look holistically at those kind of challenges. So those are some of the green shoots that I'm seeing now.

Stay on that for a moment. Just the integration of those functions that were probably thought of as sequential or chronological before. What does the integration allow them to do that they couldn't do before? And why is that important at this moment in time?

Yeah, it creates a single organizational voice around talent. So rather than having the development, but you're talking about development during the acquisition, so you have that message throughout that, you're creating a single talent platform. So AI is not going to be as effective, or any automation is not going to be as effective if you don't have good data. And so having good, clean, repeatable, accurate data is absolutely critical. So in the past you had silo. This is the TA data. Oh, here's our developmental high potential list. It's on this spreadsheet. And here's this over here that we look at for what our leader development curriculum. So having that in a single area that can be drawn upon allows you to look at that holistically and it breaks down having a technologist for, for all the different areas and you're focused on it writ large at the talent level.

Kara, jump in here. You work with a lot of companies. Accenture also is obviously a talent magnet. How are you thinking about these questions of acquisition and development?

Yeah, so I'll talk about Accenture first. Right now we have about 70,000 data and AI team members.

70,000.

70,000 across the globe though, and that's about 7 to 10% of our workforce is devoted to data and AI. So we've built that. Accenture has built that by way of some acquisitions and then also bringing in talent and also reskilling talent. A lot of folks have moved to that segment and want to work in that segment. The rest of the team is all in development mode. We are always developing to make sure that we understand the new technologies. And we're reminded of that each and every week by way of required courses or things that we have to catch up on so that we can stay out front. It's very clear that in order to develop at Accenture, you need to know these things. You need to take the time to do the training and get up to speed. And so that helps, you know, by being a large organization, we can have these large measures in place, right, that allow our teams to know, what should I be learning, when should I be learning it, how do I keep moving forward? That said, we still need to attract a lot of talent. And so along those lines, we work with a lot of the universities to understand. We also work with organizations like Skills to Succeed, where we're bringing in talent from underserved areas and helping them develop by way of whether it's a summer internship or by way of a co op type program, you know, ways to build our team and grow. So I think, you know, there's lots of ways to get to the talent and to help them. I do agree that Talent in itself is a huge work stream. Now and it's really that everyone's watching the data. They're trying to understand how are we going to fill the need for all this new skill and new talent. And so it becomes something that you've got to watch that full work stream along the way and understand how you're faring as far as bringing that talent in.

Wayne, love to hear your perspective on this. You're working with a couple of data and cybersecurity companies right now. You've seen AWS put some significant initiatives into reskilling and retraining and so forth. How are you thinking about this question?

So I'd look at this question, I do look at this question as both tactically and strategically, if we recognize where we are, like why are we all so excited or distraught or something anxious about where we are? This is the first technology I've ever seen come in from the top. Every technology has been introduced, usually from the bottom. Somebody with a credit card or a little lab or garage and they would start tinkering and eventually they create something interesting. They'd show it to somebody and it would go all the way up and somebody says, eureka, let's fund this. This is the first time I've seen it all come down from the top. Two years ago, 90% of the CEOs were saying, AI, how is that possible? Like, CEOs have other things to worry about. But they were all saying it. So there's this tremendous pressure to do something. The Thousand Wildflowers, whatever Johnson came up with. So we need to just sort of ground ourselves for a moment and kind of again, look at history, look how things have evolved with other technologies. And tactically, look at the following reality. There's always a split in an organization. When change comes, doesn't matter what the change is, right? There's 20% of the people who are excited, there are 60% of the people that are thinking, and there are 20% of the people who will tell you, no, fill in your expletive way. This is Boston. So you know what the expletive is. And so, okay, so tactically, what do we do? We have to make progress. And if it's all top down pressure, it's just a bunch of excited molecules bouncing off each other. Nothing ever gets done. So tactically, what I would recommend is look at this as a bottoms up exercise, not a top down exercise. Because if you produce results from the bottom up, the people who are at the top will go, oh, look, something is happening. How are you doing it? Well, let's talk about how we're doing it. A lot of this is providing people with what they really want. They want agency over what they're doing. They want ownership. They want to be able to master this stuff. They don't want to just kind of like run through it. So there are a lot of problems that people can solve with today's version of AI. You want to give them the opportunity and pick from that top 20 because the top 20 will always go figure out something and they're going to drag along the top of that 60% of that 60 will follow because they don't want to be left behind. They just don't want to lead the way. Now the other part of that 60 and the bottom 20, what's going to happen is going to happen over time. We'll see where that goes. It's not your worry for today. Strategically, different story. So how have we thought about changing the makeup of our workforce strategically over time? It's iteratively and it's incrementally. It's not all at once. You will have your current set of top 20 that they are the folks that understand the culture, they understand all of the history of the organization and they want to make change. Those people are amazingly valuable. Then you have folks that understand this domain. They just got a Ph.D. or they came from one of the labs. This is my only pessimistic thought. It's a realistic thought, actually. A lot of the AI startups that are sucking up all the talent right now, they're going to go away. They're going to go away in droves. So there's going to be a lot of great AI talent on the street. You'll be able to hire them because they want stability. They won't want another startup gig. But how many do you need? Do you need 1,000 of them? Do you need two? Need three? The answer is generally two or three for a given organization, a given project of a certain size. So now you have great people that you already have in the organization, know how to navigate everything. They know where all the dead bodies are buried, right? You have three people who come in, two people who, whatever, who understand the technology, who can really start upskilling people through leadership. And then. And I've done this within, you know, So I started AWS Boston here about 10 years ago and we grew it from zero. That's me. I'm zero.

Patient zero, whatever.

Zero is a perfectly good number. And we grew it to almost 5,000 people in five years. How in God's name would we do that? Well, we didn't steal them from everywhere because not everybody was right for the culture, the university system. So who comes in with the latest and greatest skills? Students. Who will tell you you're wrong every minute of every day? Students. And they're right 50% of the time. So if you take this top 20, a few of those, you hire a few that really understand the technology down deep, they may not know your organization or your business, but they do understand tech. And you hire great graduates that understand the technology because places have done a great job in educating them. You don't need to be scared of this. But it is incrementally and iteratively. It's not all of a sudden right now, because everything's being driven from the top, it feels like everything needs to be done all of a sudden. That will create a Johnson Johnson situation where you have 10 things that are good and 990 that are not great.

So it's interesting, there's this duality, right, if I'm hearing it correctly, of the bottoms up pressure from something that has been direct to consumer, creating this sort of down pressure from CEOs. We have to act right away. The danger of like thousands of projects going on, who knows what's actually paying fruit. And then I hear one other sort of duality in there, which is 70,000 folks focused on data and technology and so forth. At Accenture, you're not talking about downsizing them to two or three, like you could bring into some of these organizations. You're talking about upskilling them. But surely we're going to see some of that as well, and some of that migration. And so, Paul, I would love to ask you this question, which is, as you're working with organizations and thinking about how, you know, how to help them upskill, how to help them reskill, how to help them get those talent, those playlists to stay up on the latest and greatest that Kara was telling us about and close these skill gaps that are created by these emerging technologies. What are you recommending? What do those strategies actually look like on the ground? And where are you seeing the biggest gaps themselves? And then all of you can jump in after. But Paul, why don't you kick us

off at this point? Yeah, thanks. It reminds me that while we're in, I think Moore's Law came out in about 1965, I think, and the speed with which technology was changing, essentially the power would double every two years and the cost would go down, and the size of the devices and the chips at the time would shrink every two years. And we're certainly seeing with AI this exponential increase in the pace of everything except one thing. All of our supply chains are digitized, except that supply chain that supplies human capital. And the ability for humans to learn is not changing exponentially. It's essentially it takes a three year old just as long to learn how to. I guess 3 year olds don't tie shoes, some of them do. But it takes a young child just as long to learn how to tie a shoe now as it did hundred years ago. And so that's the one interesting thing is we are using technology to, we think, teach better, teach more efficiently, personalize things, personalize on the fly, adapt learning. And yet the human machine is still learning at essentially the same pace that it has learned forever. And so that's one thing is everything's moving faster. But somehow this training and upscale skilling or reskilling is going to continue to move at the pace of human learning, which is roughly a very straight line over time. The other thing that I think that we're seeing and as we work with partners is sort of a step level approach in, and this is what we're doing internally at wgu, so I can refer to what we're doing with our own employees, is to say we have single course offerings that we offer. For example, in the AI realm we're saying it doesn't matter what you do at AI, we want you to be comfortable with working at wgu, we want you to be comfortable working with AI. So we have a single course that we offer problem solving with AI. It's essentially prompt engineering for everybody. Then for those who, that's enough for me and I think that's enough for my job. Great. But for those who want more, then we have a AI skills Fundamentals. That's a certificate offering, which these days certificates seem to be something of the rage and stackable certificates and whatnot. But this is essentially a grouping of courses that help give some more foundational knowledge about AI. And then for those who really want to dive in, we provide a really great tuition discount for our own employees who want to go in, say to a masters of Computer Science and really get into the nuts and bolts of AI and machine learning or a master's in Software engineering and really get into the nuts and bolts of AI engineering. And this is essentially what we're seeing with those partners that we're working with too is rather than a sort of one size fits all, they love the catalog approach and sort of that step level to be able to say, well, some of our people, this is enough. And for some, we want the full buffet offering.

Gotcha. I'd love to hear from all of you innovative learning models, partnerships that have helped organizations start to close this gap. Give us some case studies, give us some specifics on what you're seeing out there.

Yeah. One of the things I want to touch on as I navigate towards that answer is to pick up on the idea of the individual and how you build those skills for the future in AI. And it's really kind of two sides of the equation. One, it's what skills and knowledge does the organization push towards the individual and then what does the individual pull from the organization? And both of those, I think, have to be operating in synergy. In the case of the organization, you have to look at your jobs and say, have I broken them down by what skills are essential to success? You know, have I done that effort? Rather than talking about having X number of years in this job and having done that, it's what do you really need to do? And we've taken our jobs and we've broken them down into the skills and then how do we push those skills to our employees? One of the things Comcast is doing is something called skill forward, where every month they select one or two key skills and they push that through the entire organization. This is what we're focused on, this is the spotlight, because this is a foundational skill to your future career. But then on the other side, if an individual's not going to have that curiosity, that self directed nature, the desire to grow, how do you inculcate that? Because if they're waiting for the organization to do everything for him or her, it's just not going to happen. You're not going to rise to the same level of potential that you would have. If you can understand, how do you embrace the opportunity? So that's where you invest in things like mentorship and coaching, being able to say, hey, this is as much on you as it is on the organization. So how do you, as the individual say, I'm going to take advantage of what's being pushed towards me, that I'm also going to be pulling from the organization in terms of innovative things that I'm seeing in academia. One of the organizations that I work with is Holt International School of Business, which is here in Boston as well as globally, and they're using AI to create mini cases for business school. And so rather than going through this long extended where you're doing interviews and you're writing up cases and you're Iterating on it. They grab a series of executives that are working in various roles in organizations. They send them an AI interview on their computer. They start telling a story. How did you solve this challenge? What did you do? And meanwhile the business case is being written behind the scenes. And so when it spits out, it's not going to be what a Harvard business case is. It's been tested for years and have all of this. But it's real world and it's practical and they can use. These are what actual people are using in the role. And so you can push two dozen business cases in the course of one single semester of a class. And as you meet someone interesting, you say, hey, that's an interesting challenge you're working on at Accenture or wherever. Would you mind if I sent you a link for a little interview with an AI bot to talk about what you're doing? And then boom, the business case is being generated. And so I think that increases speed to market and increases the hyper relevancy and then when it's not fresh anymore, throw it out. You have nothing invested in it. It was a couple of interviews and generated through technology.

I like it. That sounds like a great way to build a business case and jump right in.

I suspect the Harvard Business School publishing company is quaking.

Absolutely. You know, wayne mentioned the 20% and you know when we. Sorry. My day job is leading change management. And so as, as my career developed in change management, so many of the dynamics are around helping people understand how they're supposed to change and being okay with it. And that first 20% are the trailblazers or the early adopters. They'll jump right in and learn super fast because they've got that drive. They want the next change and then others are going to sit on the fence and they're going to wait and watch what happens to that first change. 20%. The fence sitters probably need more intervention. Right? They need different ways of learning. So we can assign all kinds of courses to them, we can make sure that they're taking their online training. But they're probably a lot of those fence sitters are probably going to want some hands on experience. They're going to want a mentor or coach to guide them. And so I think it comes down to the learning models have to flex for the type of learners and we know that people learn differently and not everyone is going to jump in and grasp everything they need to know from an online module. And so I think having the opportunity to talk about it is really important. I Love the different models for education now as far as getting being able to jump in for a course here, a course there's there being able to take a degree program in the evenings or around work. I think a lot of our young people feel like they have to step away for two full years to go back to do their mba. But it's nice if they don't have to do that. Right. And not all of them can afford to do that kind of a jump out for two years and then come back. So I think those programs are so critical to allowing our young people to grow and keep progressing in their careers the way they want to, but also fitting their lifestyle of letting them do a class here, a class there. And so I think we have to keep that wide spectrum of learning opportunities open based on the different learners and the folks that we know they have different ways of learning. So I think that's important.

I have so many thoughts.

Where do you want to go?

I don't know.

Right now I'm blue screening because there's so many

generational reference as well.

Yeah,

it's like an Android thing I wanted to bridge. So a couple things. This will be like everything that came before it and nothing like what became before it. If we think about how we bring people up to speed in this world, it is going to be very different than how we brought them up to speed in the previous world. And it just happens to be about the speed at which we need to change. I will look at this again tactically and strategically. Tactically. A lot of what the early adopters will do is on the job training. They will learn how to do this using the term is snackable bites. With what we have with either YouTube or other like mechanisms today, people can come up to speed on any topic really fast as long as you have quality content out there. And there's a lot of garbage, but there's a lot of quality content out there. So I think about enabling learning through results and those results being driven from the bottom up based on things people want to do. So then there's the famous Google 20% for 20% of time, do whatever you want. So a day, a week, you do whatever you want. This is a great time to think about that model. But instead of saying do whatever you want, do what you let them do, what you never let them do. A lot of people live in work environments where there's toil. We don't really often think about toil but do any survey once a week on people and see what they love and what they hate and they always hate the same thing. They hate doing work that's repetitive, non value add and doesn't move their career forward. That's signed by dear boss, I quit. This is an opportunity to actually enable that due to speed because a lot of that toilet work we don't let happen because it takes too long. It can't be broken into little small pieces that they can do. We're getting to a point with some of that toil where these tools, these automations can quickly accelerate how people create solutions. And let's assume that the first three fail. Who cares? They've learned something. Failure is the best teacher, not success. So there's going to be a lot of these failures but it's not on things that the CEO is looking at. It's a lot of things that the team is looking at. And the team is totally cool with going to the lunch table saying I tried something, I failed. They're not totally cool doing that during performance review periods. So tactically use on the job training and allow bottoms up solutions based on things they want to do but we're not allowing them to do to be the fodder for the early days. When it comes to the strategic piece of this, it goes back to the answer I said before. You got to find some folks that understand the technology really well and get them introduced to your world, put them on a tiger team, have that tiger team produce a result for the organization that now can be seen at the top of the company as something that is producing a business and customer valued outcome. A lot of this stuff is just do something. Nobody wants to just do something. What problem are we really solving? So the organization needs to learn how to crawl, walk and run against problems that the business actually cares about and more importantly that a customer will come into the business say thank you for doing that. So I would break it up into those sort of tactics and strategy.

So you mentioned something interesting there, the 20% time that Google you mentioned many things that were interesting but 20% time for the Google employees and maybe the this gets to something that I was thinking about as I've heard all of you reflect which is one of the biggest things that anyone faces is poverty of time to do these things right. We're all stretched in a million directions. I see a lot companies saying you should learn this and yes, snackable bites I think will help with that. Others, I'm curious, what other strategies have you deployed to give people the time to do the learning that they need to. What have you seen Carrie you're nodding so well.

It's interesting because I think for a lot of our client work we're seeing the leadership teams are the ones that really need to learn fast. They want to learn fast because they want to guide their teams and yet they have no time to learn. And so I think it is really hard right now. I haven't seen the magic model for that. Right. Of how do you give people time? We try to do it in bite sized workshops. Right. How much can we teach during a workshop and allow them to experiment some and do some activities? And we do find that if the leadership team pulls away for half a day or a day or two hours even, we can move that forward just a little bit. But I think people are really crunched for time and it is hard to fit in unless they carve out that time out of personal drive to say, I'm going to spend X hours a week learning. There's a copilot and one of the Microsoft Outlook apps. You can get it to schedule learning time and it can schedule focus time and those kinds of things on your calendar. And I don't know if any of you have tried it. I tried it and I still seem to move my learning time just about every week. It seems to slide off, it falls

back down because it's not the highest priority at the moment or the urgent and perhaps less important, as Stephen Covney would have said.

That's right. And so it falls off the calendar. Right. And so then you're okay, I guess, if I've got to get my learning in, when am I going to do that? And people are struggling with how to manage their calendars that way. They really are.

Will.

Paul, have you seen anything that you're saying that you'd say this is something that folks should look at around this problem?

I'll actually pick an example out of academia. Duke this time. So Duke is developing something called Duke GPT, which is their own version of a bot. Right. And their own version of how you access AI information. And the thing, the theory is that it draws upon trusted sources so it fences off things that they don't want to introduce. It has high security around it and then it's exposed to the entire academic ecosystem of the school. And the thought is that you customize and build out and adapt this Duke GPT during your four years or two years, however long, and that this becomes your lifelong bot and you're developing it in school and now you're using it later on in life. So I'm thinking with all of the things out there that you can learn, how does an organization, a large company, create their own GPT and say this is the one to use, this is the sandbox we want you to use. Now if you try to put proprietary company info in there, it's going to flag it because we know what it is and it's going to tell you you can't do that. And you know if you're trying to go to North Korea to pick up information, but it's going to be that safe sandbox. But then you're able to put promote it and someone will say, all right, this is the tool that I'm to interact with to help guide my career here. So it's just sort of nascent at this point, but putting that stamp and brand on it and saying we've taken something generic and we're customizing it and here it is for you, maybe something to think about.

Yeah, we're doing that at Accenture as well. Accenture GPT, well, it's called Amethyst and it is our sandbox to get our work done as well as play and learn. It's right on the main portal. So what happens is it's that hands on learning, it's allowing people to just jump right in and use AI to get our work done. It has the safeguards and which is great and it'll tell you when it's pulling from a public model versus Accenture built model, proprietary. But it does allow all of us to get our hands in there and play with it on a regular basis and use it to get our work done.

So I have a couple more questions before I go out to all of you, so just start getting ready for your own questions. But Paul, that just sparked something that real world learning because you remarked to me I have 19,000 folks in the college that I would love to get internships for. And this to me seems like maybe the most important thing. As education institutions right now. How do we integrate with companies to give real world work experiences as part of their education to stay on top of this? One of the central challenges that I observe is companies would love it in theory. But again, priorities de risking that working with people new in their careers and so forth. How do we bring these two groups together and get more real world experiences? I'd love to hear from all of you, but Paul, it's top of mind. I know.

Yeah, in the 19,000. By the way, Mike Morris is here. He's our senior director who's responsible for our cybersecurity programs. Mike I think single handedly has brought most of those students into the program. So I want to give credit to Mike for his great work. We think we'll see. Check with us in 12, 18 months. But we think that just like we can virtual machines was this really crazy idea, like, wait, what? I can create this whole digital infrastructure that actually doesn't exist, but it does exist in a virtual realm. We think that we can virtualize the work experience. We'd love to partner with companies who at least lend us either these are the skills we would love our employees to show up having, or these are the scenarios that our employees need to be prepared for. They don't have to actually use live data from the company, but if they help us understand that environment or the context that their employees are learning in or need to be prepared for, we can virtualize that experience and integrate it into the student's journey. And I think this is going to become increasingly valuable for our students. Working to be graduates and then moving up or moving into the workplace, moving up within or moving into the workplace. The other sort of interesting scenario that exists now is increasingly, even for entry level roles. Companies are saying, well, it's an entry level role, but we want you to have three to five years work experience. So a student's like, whoa, what am I? Like what does entry level mean? And then with the automation or the efficiencies that AI grants even fewer potentially the entry level roles are the ones that seem to be most affected by this AI wave. And so not only is it more challenging to get into those roles, but if you think of sort of entry level, will you gain then the experience that's required for the more advanced roles? Well, what if we're no longer hiring as many people? Where in the world are they going to get the experience that's required for the more advanced role? And so again, I think virtualizing that experience as much as possible so that it's a real life scenario, it just doesn't happen to be at an actual company. It's the bet we're making. We'll see what happens.

Wayne, Kara, Will, I'd love to hear from you all.

I'll just jump in real quick. You've brought up two points today which I think are amazing, which is the stackable approach where folks can come in and learn some of the most basic things like prompt engineering, move up the stack to, we'll call it AI engineering, and then move up further into the stack, into the hard science behind this technology. That approach is going to allow a lot of folks who want to go beyond the Snackables from YouTube to be able to upscale without having to make that two year commitment for a master's degree in computer science. I think this is amazing. The other piece of allowing them to gain this, you know, the first five years, if you would, of skill so they can get the entry level job. That insight is one that everybody should take away because what we expect from our graduates, not quite today, but pretty much tomorrow, will be what we expect from a fifth year senior engineer or a senior contributor. Today. That's just life. We can't make that easier. We're not going to make that go away. It's always been the case what I needed to know when I graduated from college as a computer science person and what people know graduating in 2025, boom, it's like night and day. That's why I'm wrong 50% of the time when they talk to me. That's why I'm like 70% of the time when they talk to me. So I think this approach and this multifaceted approach that you've articulated for the university is spot on. Now how do you implement that at scale that becomes more challenging and I think it's really scary. A lot of people are now talking about, well, I'm not going to send my kids into computer science. Like computer science is now dead

wrong.

It's now more needed than ever. It's just going to change. It's going to accelerate what we need them to understand when they graduate, including what was said earlier about what skill do we need to give them that they're not learning today. I want to go back to Aristotle. There was a time where if you were a physicist, you were a philosopher first. And in fact, at some point around the 19th century, we decided to split philosophy and physics apart because one was science and one was, I don't know, something else. I think we need to get back to that model somewhat, which is teaching insight, teaching people how to have high judgment, and then having them appreciate the craft of what they're doing. And today we really kind of focus a little bit on the craft. Craft minus those are the things that are going to get people to really understand how to move themselves forward and become valuable alongside AI. It was said earlier, AI is not going to replace anybody. People who know AI will replace everybody. So we have to give those philosophical skills, those softer skills along with the craft and the hardest skills apply. A lot of what you said today in terms of how you could see stacking knowledge over time to make that all work. That's brilliant, Kara.

Yeah, Paul. I love the idea of a virtual internship and I can see how it's doable. We have one area of work we've done with state government where they have caseworkers who need to work with children and families. And the situations are very intense, and yet you need to teach someone what to do in those situations and how to make the right decisions. And so we have virtual training packages that we offer in that where the scene is played out in a family's home and the individual, the caseworker, has to decide what action are they going to take. And then they get feedback on that action. The scene plays out for farther, so you start to see how that could work. I love that concept of internships. I have an intern that starts for me today. I had to just give him some reading material for the morning. It's hard to bring them in because again, it's the time punch. You don't want that student to just sit and stare at a screen all day. You want them to have an opportunity to jump in on a project and work side by side with you. But it takes a lot of time to do that.

So I also think this is a great idea. I'll touch on it in a second. When you think about AI, there's kind of two realms that you see a lot out there in the press. One is the actual technology component and how you're using AI and all the technology. The other is the branding component. How often do you have a vendor, if you work in a company that comes to you and says you want our solution because it has AI in it, it was built with AI. And when you come to have a proposal funded, if you can come in and say, and we're using AI, that makes it a stronger business case because people think we should be doing that. So paying attention to how you leverage the brand component of things as well as the technology component. So I'm thinking about this idea of the virtual internship or virtual co op. So you slap a name on it. This is our AI powered co, op, right. And so you could go to Northeastern, which is a great co op school. And full disclosure, I did my doctorate there. I think they are very, very innovative. But it's hard. You've got to go overseas. You have to leave. You have to complete school over five years. If you could get 80% of that through a virtual co op, but then bring in other technologies, not just AI. But how does virtual reality factor into that?

Right?

Where you actually feel like you're in that workplace, you're actually plugging in and so you're having that immersive experience. So you're getting 70% of the CO op experience at a fraction of the cost. And this is branded as boom, sponsored by wwgu. Would be a thought there.

Hope you all enjoyed the panel discussion. The conversation we were having continued with some Q and A, but given the length, we will leave it there and hope you took something valuable away that you can use. We'll see you next time on Future You.