Vicki Lynn (Argyle) 0:00
Vicky, hello and welcome back to the Argyle AI Leadership Forum. My name is Vicki Lynn with Argyle, and it’s great to have everyone joining us today. I’d like to introduce our speakers, Jason Cerrato, Vice President of Talent-centered Transformation at Eightfold AI, and Greg Vert, Human Capital Applied AI leader at Deloitte Consulting, LLP. We are so excited to have Jason and Greg with us for a fireside chat titled, “from automation to transformation: How AI is reshaping the way we work.” Welcome and over to you, Jason!
Jason Cerrato 1:16
As mentioned, I am the Vice President of Talent-centered Transformation at Eightfold AI, but we partner with Deloitte quite a bit, and I’ve had a chance to partner with Greg in the past, and we’ve done these events. And the last one we did was a little bit a time ago, but I still go back to that session and review those materials, because I’m so fond of the way Greg and his team are thinking about this, so I’m very excited to get some of the latest and greatest thinking and have this discussion with Greg. Once again, Greg, thank you so much for joining me.
Greg Vert 2:00
Yeah, thank you, Jason. Always a pleasure to spend time with you and to collaborate with the eight fold team. And as mentioned, my role at Deloitte. I’m the applied AI leader for our human capital practice. Our mission is to make work better for humans, and humans better at work. And AI is obviously having a big impact toward that mission. And I’m really excited about the conversation today, and I love the way you phrase that to kind of kick us off, because your introduction talked about kind of the two sides of this transformation and of this conversation. And one of the things that I’m so interested in is, you know, AI is driving this transformation and causing a lot of the needs to transform, but it’s also enabling the transformation as well. So I love how you’ve introduced this topic to say, you know, this is not unfolding quietly. And one of the things I often say is we’ve been talking about the future of work for quite some time. And one of the things about the future of work is it’s always ahead of us, but I think more than ever, it really is here, and we’re living through it currently. So as you share with us some of the latest findings. How does that resonate with you?
Greg Vert 3:06
Totally resonates. And you know, one of the things that I’ve been saying a lot lately, when I get a chance to talk to clients and others about this topic, is we’re now in an era where technology is not the barrier, right? Things are moving at the pace adoption is moving at the pace of organizational change, not the speed of technology. And so we are actually humans are now the bigger barrier, whether you look at it at the individual level, the team level or the organization level, in terms of really reinventing or transforming or moving toward that future of work that I think we’ve been talking about for years now. And yeah, I’ll share a couple of statistics that I think start to bring this to life and maybe show exactly where we are in the journey. This is all research from our state of generative AI in the enterprise report. It was a study that we ran all throughout 2024
getting quarterly refreshes of the data. This is an invite only survey. So we’ve sort of hand picked the people that we want to participate in this based on their role, their title, their expertise, but we did get about 3000 responses. So it’s statistically significant in terms of pulsing where the market’s at. The first stat here, 40% of organizations are investing in a high level of effort in re-skilling workers due to Gen AI. I think that speaks to the disruption that we’re really still in the early stages of that are going to force organizations, force companies, to really emphasize the development of new skills, and those skills may look very different than the skills we’ve prioritized in the past. Correct when we think about the role that AI can play, and that’s a moving target, right? As AI matures and expands and advances capabilities. And speaking of that, the second stat here, 26% of organizations are already exploring autonomous agents. So I’m sure many of you on the call have heard about agentic AI? It’s sort of the next evolution of generative AI, which is interesting because generative AI has only really been around in the mainstream for about two years, two and a half years when chat GPT launched toward the end of 2022 generative AI and technic technical circles have been around for much longer than that. But I like to say, between October 2022 and early 2023 Gen AI went from, you know, technology conversation to a boardroom conversation to a dinner table conversation, in terms of how quickly it became part of our mainstream dialog. And I think we’re about to see the same thing happen with agentic AI. So that’s the new terminology that everyone’s talking about. It is about autonomous agents that can perform tasks with little to no human intervention, or where we can design very specifically that where that human intervention should take place, which we often call, you know, human in the loop. And there’s lots of different flavors of that human in the loop, but this is all about boosting productivity, and it’s going to drive the redistribution of human resources, financial resources facilities, you know, and you name it, right? This is really where a lot of that disruption is going to happen. And then the third stat, 67% of organizations have started to integrate their most advanced Gen AI initiative or effort into core business processes. And what we found in our research is that this is actually the way to get the highest return, or maximize the benefits from generative AI in particular, is to embed it deeply into functions and business processes to drive transformational results. And then the last piece of this, which I think completes the story a little bit, is that 78% of organizations do not today consider their talent to be highly prepared to adopt Gen AI tools, and that’s where the dependency on adoption to create or realize value from Ai investments is going To be a big focus for organizations in the next 12 to 18 months, which will require a lot of this rapid and unprecedented training and education to increase the fluency and proficiency of the workforce. So there’s a lot going on. There’s a lot of moving pieces to this. That’s just some of the data that we’ve pulled out of the research to kind of get the conversation going today.
Jason Cerrato 7:20
And I think when you look at that research and those stats, it’s telling the story of how this is comprehensive. It is not kind of single streamed, single threaded, all of the change, all of the needs, all of the transformation is happening in a variety of places, and the organization of tomorrow looks very different than the organization of today. So it’s not just enhancing or optimizing. It really is rethinking and transforming in part because this concept of agentic AI not only adds automation, it adds decision making in a way that impacts how we look at productivity, how we look at org design, how we look at work design, I love how you talk about this kind of redistribution of resources. We’ve been talking to leaders who are talking about maximizing artificial resources and amplifying human intelligence, right? And trying to figure out what that looks like in the workforce of tomorrow. But then, to your last stat, there what that requires in terms of training and preparedness for how, how those resources are going to work together, and then how those, how that’s going to happen in a way to deliver to customers, to drive innovation, to help with collaboration, because also the way we learn, I’m assuming, is going to be very different, yeah, and I think we’ll get into that a little bit more in our conversation, but I completely agree.
Greg Vert 8:49
And from our perspective, we are in the early stages of the largest work redesign since the Industrial Revolution, you know, 100 plus years ago, and it’s being fueled primarily by AI. But that’s not the only component. It’s not the only part of the equation. And we know, based on what we’ve seen already, and just kind of general direction of travel, that this revolution is going to be much faster paced than what we saw in the industrial revolution. So we have to be really proactive and have a bias toward action in order to navigate the transformation transition that we’re like I said really at the very beginning of one question I want to ask as we kind of spend the next half hour together.
Jason Cerrato 9:33
So the last time we met and we were having this kind of discussion, the focus was a lot on skills. I think these days we may not be talking about skills as much, but skills is very much a part of this exercise in this conversation, because the way we work, the tools we’re using, are changing, and as a result. Skills are a component of this discussion. Still, am I correct in thinking about that that way, Greg?
Greg Vert 10:06
100%. Yeah. And a lot of our clients are at different stages of maturity in their movement toward a skills based way of thinking about and managing talent, human talent, and I still think skills give us the best data to understand the capabilities of our human workforce. And right now, we’re starting to actually see the concept of skills getting applied to AI and to digital workers as well. It gives us a framework to define the capabilities of our AI agents. So skills are still a very useful framework and data sets that can help you to architect the right workforce, both human and machine, and it helps you on both the supply and demand side, right? So you can define the skills and the composition of your workforce, again, both human and machine, but you can also define the needs of the business through skills. So it gives us a lot of versatility. And you know, one of the things that I believe is going to carry a premium over the next few years is organizational agility. Agility will become more important than expertise in a post knowledge economy, and that agility is something that a lot of organizations are struggling with, especially large organizations that have been around for hundreds of years, or, you know, a really long time. It’s hard for them to move and become more agile. There’s a lot to unravel to get there. But again, skills give you the ability to improve that organizational agility and the movement of talent, again, both human and AI, around your organization. So I think it’s a big part of it. We are seeing, though, that there is another ingredient to the puzzle, another piece to the puzzle, and that’s what we’re calling work intelligence. So when we only look at the skills side of things, you know, we miss out on the fundamental building blocks of the work itself. And so there’s this whole emerging category of solutions that help us to deconstruct the work that has to get done, and help us to map that work to skills and then that skill, the skills to the right human and or machine, or in most cases, combination, to deliver that type of work. So that’s kind of where we are is figuring out how to map work to skills and skills to the right resources in order to move forward in a strategic and purposeful way, versus, I think, what we’ve seen, and this is no different with AI, is when We let the technology lead the transformation, versus strategy, the results are diminished.
Jason Cerrato 12:46
100% agree, and I think that’s how the conversation has evolved and shifted a little bit. And you know, I loved what you said around the kind of organizations that have been doing this for some time and their history not being as valuable as they’re building for a future that looks very different. I’ve seen a couple different approaches with some of the organizations that we’ve partnered with.
Jason Cerrato 13:18
I had one sitting on hundreds of pieces of data that they don’t consider as valuable to them anymore, and they need to help surface the unknown and prepare and plan for the unexpected. We had another organization that said, you know, they were a 100 plus year old organization that’s trying to refuse to act their age, right? And they’re trying to think innovative and think progressive and and act and act young and fresh. And then I had a third and I think you referenced this in the beginning, around in the past, when they tried this, the technology wasn’t quite there yet, and they’ve talked about how the technology has caught up. And the word that they keep using is now that technology unlocks a lot of capability, and it unlocks a lot of visibility to develop strategy and to deal with uncertainty in real time and on demand and create agility. But part of that is, and I think you also mentioned this, the team, the actual team members, the humans, need to catch up, right? And you need to deal with change management and culture and how this benefits them as well, in kind of the Win Win, of how things like talent intelligence empower the organization, but also empower the individual experience. So can you talk a little bit about what this means for where organizations start and empower the workforce?
Greg Vert 14:40
Yeah, and I’m actually going to switch us to our next slide here, because I think it’s a helpful framework to have this conversation. We’re sort of seeing five key things that are needed to help manage the transition and navigate the workforce transformation that we just described. Three of them are a little bit more urgent than the other two. So. I’m going to start with the ones in the blue boxes. But this all starts with driving adoption and engagement. People have to be open to and look for ways to use AI in their day to day work. And so driving that adoption and getting people to use tools and to change their behaviors and to work in different ways is a key part of all of all this, and without the adoption, the rest of this stuff sort of doesn’t matter. But what we’re finding is that in order to enable that adoption, you have to create a culture or an environment where experimentation is encouraged, innovation is encouraged. A lot of organizations have been on that journey for a while as well, trying to become more nimble and innovative. This is going to be a forcing function to get there, because if AI becomes a competitive advantage, and we can’t harness that competitive advantage unless we drive adoption and engagement, and we can’t drive adoption and engagement unless we have the right culture, you can see where you know the dominoes start to fall, sure, and we know that trust is a huge part of this. And when we talk about trust, it’s not only trust in the technology, because AI works very differently, differently than any previous generation of technology. And we as humans have this weird mindset and relationship with technology, where we think of it very binary. This either works or it doesn’t work, and that’s where terms like break fixes and defects come in, come into a vernacular. AI is not like that. I mean, yes, there could be legitimate, a legitimate defect in an AI model that needs to be fixed, but most of it is more about capability development, the same way we would think about developing a person on our team, and we have a role in that humans are accountable for the outcomes of AI, and so building up that trust and building up that accountability is really important, but it’s also trust in leadership, and leadership’s incentives and motivations for investing In AI, not as a replacement of human workers, but to augment human workers and to really unlock human potential, which I think, Jason, you started to hit on there, and then the third piece of this, and I’ll pause, because I want to get your thoughts, Jason, before we go to the green boxes. But the third piece of this is all about the fluency and skills, and we think about this from a persona driven perspective, there is a level of fluency that virtually every worker on the planet, regardless of what job you have, what role you have, will need to be responsible and effective user of AI and to be able to collaborate effectively with with machines. And then there are skills beyond that, right? So you think about someone that’s building and delivering AI solutions. They, you know, obviously, will need to have more technical skills. They’ll need to know the under, under workings of AI. They’ll need to understand the relationship between AI and data and be able to build out different use cases as we progress. And then you think about people, leaders, they’re going to need a different set of skills, and executives who are driving the strategy for the organization are going to need a different set of skills, and so there’s lots of skill building needed in that persona driven approach, I think is the right way to go at it, starting with the fluency for all the information that everybody needs to thrive be successful in an AI powered world. But Jason, I’m curious to bounce those, those three ideas, up against what, what you’re seeing in the market.
Jason Cerrato 18:24
Yeah, and I think this also continues, this kind of looking at the conversation from two sides, right? There’s the use of AI as a tool, there’s the incorporation of AI as a teammate, there’s the adoption of AI as part of getting the work done, and then there’s amplifying the human work that remains beyond the work the AI is doing, right? So all of this becomes multifaceted for organizations to address from a learning and development perspective, for employees to understand from an employability and a sustainability perspective. And I think you know, as you mentioned, this is going to be the future of careers and kind of employability, with developing your skills in a much more agile way.
Jason Cerrato 19:17
Answer your fingertips, and you have automation with agentic teammates as part of your org. Or how do you design orgs where now, traditionally, they’ve been designed for how decisions were made or how information needed to be controlled, and those two elements are handled very differently in an agentic world, right? We’ve already seen a lot of research along those lines. But then also if, let’s take HR, for example, since it’s near and dear to our heart, if the HR function is servicing and answering questions to the organization, but now think of the organization as a customer. They’re much more informed on their own. Of AI and asking questions and getting answers at their fingertips. By the time they come to HR, the level of question that they’re going to ask is much further along, and the level of expertise that that HR person or leader or associates are going to have to have is at a much higher level. So you’re amplifying the need for that human interaction, because now it’s beyond just a transaction right now. Now it’s critical. Now it’s critical thinking. Now it’s strategy now. Now it’s at another level. So there’s the technical component of this, there’s the amplification of the human component of this, and even if it’s emotional connection, we didn’t mention that yet, right? So all of this has kind of a multi-faceted approach, as leaders and organizations are designing for how we’re going to work in the future.
Greg Vert 20:52
And what you’re starting to get up there are what we call enduring human capabilities. These are the things that AI is really not a threat to replace or replicate in the near future, and it is things like critical thinking, curiosity, empathy, creativity, and those are going to be the human capabilities that will carry a premium in an AI driven world. And to your example with HR, you can see that play out in action right where you can imagine certain HR scenarios require a level of human empathy that AI can’t replicate, and the right service delivery model or the right experience design is for a worker to get or a manager to get connected directly with that HR expert. Right off the bat, yeah, because of the nature of the situation, there are going to be others, other situations, though, that are much more transactional, I need to understand a policy. I need to execute a transaction process, whatever it might be. And in that situation, we actually would expect that AI would be able to do most of that work, but you still have to have that expert behind the scenes that is not only monitoring AI and ensuring that we’re getting the right results, the right outcomes, but also driving continuous improvement and being there as a backstop for unprecedented situations, exception handling, stuff that you know AI can’t address because it’s never been it’s never been addressed before, and that’s where, you know, those enduring human capabilities come back and become really important. Because what that expert needs to be able to do in that situation is an advanced and highly nuanced level of problem solving that you know only the top experts in their field will be able to do. So you can, you can see where the work shift in HR is going to move away from service delivery, process execution, operational focus that it has today, and we’ll talk more about that in a minute, to one where you’re scaling expertise through AI and you’re putting a premium on those enduring human capabilities like empathy and an advanced problem solving through critical thinking.
Jason Cerrato 23:05
I think you know that brought us to the right side of your chart in designing for this human machine collaboration. Or, as it mentioned, one of the talking about the my artificial intelligence, or this kind of work redesign for Where are you automating transactions and driving, you know, efficiency, and where are you inserting those human interactions where it’s either a higher level of critical thinking or those emotional moments that require compassion or empathy or some type of connection?
Greg Vert 23:41
That’s right. And I go back to the word intentional, because I think right now, a lot of organizations are looking to take advantage of AI, but they don’t have a good strategy that balances what AI can do and do really well. But also, how do you reorganize humans as AI takes on some of the traditional work we’ve done to get the benefit of not only the technology, but the capacity that’s created that can be reinvested to unlock human potential. And that’s where this human and machine collaboration concept comes, comes to play. And you really have to look at this almost on a roll by roll basis, or function by function basis, because the right combination of humans and machines will look different. It’ll look different even within the same function, but at different organizations and different industries. So there’s not going to be, you know, a one size fits all design for how to get this right. It’s going to be something that you’ll have to architect, you know, specifically for your organization and across different workforce segments. But we like to think of it in a little bit of a framework where every role, every role in your organization, is at least going to be assisted by AI in the future, even if the human is primarily responsible for doing the work, they will get some level of AI assistance. And. Benefits in that most roles, especially your knowledge workers, are going to be heavily augmented meaning work is going to pass back and forth between humans and AI, allowing us to move faster, to do more with less. So it’s a little bit about efficiency, but it’s a lot about velocity, our ability to work faster and get things done faster, with better outcomes, better better outputs. There is going to be some work that we call kind of AI powered. I think the HR Service Desk, or HR contact center, is a great example of this, where AI should largely take the heavy lifting away from humans, because we can train it to answer those questions, but humans will still need to play an important role behind the scenes in monitoring, monitoring AI, driving continuous improvement and being there to handle those anomalies, unprecedented situations, with the right expertise. And there is a level beyond all of that, which is pure autonomous AI, where there may be some areas that we can completely take our hands off the wheel, because AI actually can do it better than we can. And I think a lot of data rich processes with a lot of like data, large data analysis will fall into that category over time, but there won’t be a lot. It’ll, it’ll, it’ll take some time to get there, but just being thoughtful about that progression in designing your workforce, human and machine in an intentional way, is half the battle. And really thinking about not just the deployment of AI from a technology perspective, but also the impact that it has on your workforce, your organization, and being able to navigate those changes in parallel. So it doesn’t become a situation where you’ve deployed a bunch of AI and now you’re sitting around looking at, you know, what I call skills debt or talent debt, right? Which are people that you know have a set of skills that are no longer needed at the same volume within your organization, and now you find yourself with a problem to address, right?
Jason Cerrato 27:18
You kind of want to manage that all along the way, as you’re delivering AI, yeah, so I was just at a talent management in person to go and the people in the state, and I said, How many of you in the room, in your organizations, have gone through some type of a skills project or initiative, and almost everybody raised their hand. And then I said, either before or after, how many of you in the room have gone through some type of job, architecture review or initiative? And almost everybody raised their hand. Then I started raising some of the points that you just discussed about how this is changing so fast, and how with work design and org design and this new way of operating with kind of this redistribution of work and this human machine collaboration, this org model is going to look very different. I said, How many of you did that job architecture work with some of that in mind, and there were very few hands remaining for the organizations that you’re partnering with. Do you see people starting to think about that? Is that why they’re reaching out to you? Is that still a new frontier for the folks in the audience that may be hearing this today, maybe for the first time. Is this still early days? Or kind of, what’s your read on this?
Greg Vert 28:27
Yeah, and you’re really kind of getting at the last box on here the work redesign. I don’t think we’ve reached a state yet where the cumulative effect that AI has on any particular part of your organization has required a full scale redesign of work, the way work gets done, and the makeup, composition of the workforce. But it’s coming soon, and I do think some of the early movers are trying to get ahead of this, recognizing that this is going to be the problem for HR to solve. It’s the two things on the right. This is going to be the challenge for HR. It’s architecting the right human and machine workforce and making sure that collaboration exists in a way that helps you achieve your goals, and then redesigning the work that humans do along the way so that our role within the organization continues to make sense and that we’re adding more value over time as AI takes things off of our plate, and I think that work redesign is a little bit more on the frontier, but but organizations are starting to tackle it, and we’re seeing, you know, certain workforce segments like software development or engineering, being impacted first in the driving force behind that is is, you know, that’s been an area of talent scarcity for a long time, and now organizations can figure out how to combine that human and machine workforce in a way where it offsets the talent gaps that they may have had and that have been. Impacting their ability to progress against their digital ambitions. And so work redesign in that space makes a lot of sense. It makes sense to prioritize that, and the ways that engineers, developers partner with AI to code, test, deliver solutions is going to change pretty significantly over the next 12 to 18 months.
Jason Cerrato 30:21
So, Greg, the last time we met, one of the things that you shared that really caught my attention was you had shared some kind of early thinking on kind of the HR org of the future in an AI enabled world. As we convene to chat today, what are your thoughts on the future of where, where HR can go, and how we can kind of think, manage and measure differently as an HR function?
Greg Vert 30:52
Yeah, I’ll start here, which is going back to the vision for the future, strategic ambition for HR, because I really look at this as an opportunity for HR to reinvent itself. You kind of talked about that earlier, that this should be transformational. It should be about reimagining the way things get done, not about layering AI onto the way things have always been done, just to create some incremental benefits. And that’s really what this first graph is really intending to show, which is if your strategy right now is business as usual, or do nothing maintain the status quo. Your value proposition to the business and to the workforce is actually going to erode over time, because the expectations of the workforce are going to change and elevate, and the needs of the business are going to change and elevate, and so doing nothing is really not an option if you’re an HR leader right now. But the other thing that I see a lot of is this short term focus on driving up cost. And don’t get me wrong, there’s a lot of opportunity to create efficiencies and capacity within HR by adopting AI and applying AI in the right areas. But our point of view is that if that’s all you focus on, you’re eventually going to, you know, squeeze as much as you can out of HR, and your value proposition will plateau and then possibly even erode over time after you’ve reached that plateau. The way that we advocate thinking about it is really more with a lens of value creation. This is where you’re driving those efficiencies out. So we’re not ignoring that, because that is a big opportunity, but you’re actually being strategic about where you reinvest the capacity that you create in building new capabilities to help architect the human and machine workforce of the future and to help lead that work, massive work, redesign that, as we said, is, you know, the biggest we’ve seen since the industrial revolution. So I think that’s the first piece of this. The second piece of it is, well, HR is going to have a different shape then in the future, right? Because today, as we talked about before, about half of the human power in the HR function is spent on process execution, service delivery, and that’s the stuff that AI can do really well. That’s the stuff we can shift but very little time is spent on delivering workforce insights and solutions. And when I say insights and solutions, I mean things like an onboarding assistant that can guide you through a personalized journey when joining a new organization, to help you get up to speed, build your network and become productive in your role without the need for a lot of HR manual support. It’s also about things like embedded analytics and decision intelligence. So anytime you’re making a talent related decision, you’ve got the best data surface to you to help improve the outcome from that decision, make you, help you make the best choices and with AI and kind of following that progression that we talked about earlier. With some work being AI assisted, some work being fully augmented, some work being AI powered, we can actually invert this triangle and change the shape of HR, where most of our human power is being dedicated toward the more valuable workforce and insights and solutions that the business and the workforce need now more than ever. And you know, the last thing to kind of call out here, and I’d love to get your thoughts, is that, you know, we talk a lot about AI. AI is, you know, in the spotlight right now. But it’s not the only thing that has to happen to make this shift work? Right? Its AI plus other technology will come to bear. It is re Skilling and redeploying humans, and it’s investing in those enduring human capabilities that we talk a lot about, and it’s going to lead us down the path of creating much more dynamic work and organization structures the operating model of the past will will go away, and I think we’ll start to see elements of HR becoming more embedded in the business, or what we call boundary list HR, some elements of it, you know, being brought together on enterprise platforms, along with other enabling areas like it and finance, so that it becomes a shared platform. But. And so the shape and structure will look very different, and that’s all work that will happen over the next, I would guess, two to three years for most HR teams.
Jason Cerrato 35:19
So as I was listening to you, and then seeing this diagram here, it makes perfect sense, and it’s a wonderful description of the shift in the type of activity and where time is going to be spent and going to be focused, but also beyond technology. Where is the work? Where’s the transformation? I think one of the things that I’m concerned and excited about is a lot of organizations right now are spending a lot of time focusing on intentional and strategic collaboration, because if you look at the world on the on the left, my left, the traditional pyramid, a lot of that process, process execution and service delivery and enablement was roles and positions in a career path where people learned on the job, that’s right, and as those positions become fewer and far between, and people are expected to have higher levels of capability and higher levels of strategic thinking, Where does that come from? Right? It’s going to have to be developed in a new way, and a lot of it is going to have to be developed with experts, collaborating and training with people that are just joining the organization or are new to the function, and we’re going to have to develop new ways of learning and development. I’ve seen this phrased a lot of different ways, where l d is being disrupted, where l d is kind of increasingly the new way to acquire talent internally, right? But we need to rethink it entirely, because a lot of the traditional ladders are no longer ladders, and the work that’s going to be required is going to have to be developed in a very different way.
Greg Vert 37:00
That’s 100% right. And just a quick few quick thoughts on that, and then maybe we can switch to some of the questions we got from the audience, entry level. The way we think about entry level careers, that’s going to change dramatically. The way that we think about building expertise and sourcing expertise, I think, is going to change to the point, AI gives us the ability to scale expertise. So we may not need as many experts in the future in certain areas that we have today, but you’re still going to need them. And so the question is, how do you build that, or borrow that or buy that when you need it? If there’s not as many as there, there has been in the past because of the change in the shape of the structure of HR, but I also think there’s a reason why none of these bars go to 0% and there will still be opportunities for people to gain those experiences throughout their career in HR or any other domain, but it just is going to look different, and We have to be really intentional about building that pipeline of experts for the future, and thinking differently about succession planning, because if you do this the wrong way, I think there are unintended consequences of you all of a sudden realizing that you no longer have the human capabilities, the human expertise that you need in order to run your business. And I think, you know, that’s the risk we take of moving too fast here and only thinking from a technology perspective. Yeah.
Jason Cerrato 38:27
So, Greg, we have a few questions that have come in. And while we have a few more minutes, if anyone has other questions, feel free to submit them, one of which is, and I think you alluded to this, just the amount of discussion and frequency of people talking about agentic AI in the market and in the industry and on sessions like this. And there was a question just around how is this different from chat GPT and other tools like Gemini and things that we’ve been working with over the last few years?
Greg Vert 38:58
Yeah, yeah. Now, Gemini, ChatGPT, Grok, these are all examples of language models that have been trained with massive volumes of unstructured data, mostly from the internet and social media, that can now replicate natural language interactions between human and machines in a way that we we could not two years ago, and it gets better and better every day. It’s also generative in nature. It’s generative AI, obviously. So it can create things in a variety of different modalities that are derivative of the data that it’s been trained on, but not new. So it’s creating new things that have never existed before. That is really, really powerful. But when you combine that capability, think of that as like the front end capability with a genetic AI, which gives you enhanced reasoning, the ability to plan out and execute workflows, allowing you to go after more end to end automation integrated with your company’s data, your company. In these applications, it can execute processes in different systems, like your HCM or your ERP on behalf of a human or in place of a human, and it has, you know, memory capabilities so that it can learn and adapt based on interactions and real world experiences. What we’re starting to get at with agentic AI is much closer to and on the path toward artificial general intelligence, which is, you know, where we start to see AI match the human mind in almost every dimension. And so I think of agentic AI as a step toward that, and it gives us a much more powerful tool set when we think about deploying AI within our organizations.
Jason Cerrato 40:45
So maybe going back to your chart, with the three blue and the two green, in what ways should business leaders approach kind of redefining job roles to ensure AI complements human workers rather than replacing them. I think part of this is getting data in real time for how the work is changing and how the skills and skills are changing. But you know, a big part of this is adoption and engagement with those use cases for trust. I know Deloitte talks a lot about building that opportunity to play and learn in a trusting environment, to kind of figure out that mix between human and machine. But what are your thoughts there?
Greg Vert 41:31
Yeah, maybe I’ll talk about some things we haven’t covered yet that I think are really important. The first is making sure there’s alignment on the future vision and what outcomes you’re trying to create for the business, and I always make sure that you’re pairing business outcomes with human outcomes. And I think the best use cases, or the highest priority opportunities that any organization should pursue are opportunities where the enterprise benefits the workforce, benefits and society, or your customers benefit. If you can balance all three of those, that’s going to be a winning strategy for your AI program. If you find yourself in a situation where it’s hard to articulate the human benefits, the human outcomes, whether it’s to the workforce, your customers or society, then you may be going after something that may not have as big of an impact as something else you could consider. So I think just creating the right balance between business and human outcomes is a good place to start. And then everything else on here, you know, comes into play next. This is more the tactical side of it, of how do we actually get to that end goal? Of and I use the end goal lightly, because it’s going to be an ongoing moving target. Moving Target. But how do you get to that ultimate goal of humans and machines working in the most efficient, effective and responsible way, collaborative and responsible way possible?
Jason Cerrato 42:53
And I know we have only a minute or two left at eight fold, we use information like talent intelligence and skills insights to look at your existing workforce, your talent network, the audience that you’re considering, to not only identify these talent capabilities from a perspective, skills, and then their ability to learn and capability to learn other things very quickly, as we’re thinking about creating agility and moving people and redeploying people into other areas as work unveils itself with new tools. But we had a question that came in around you know, how can we address or how are you seeing organizations address this skills debt related to AI and this transformation?
Greg Vert 43:44
I mean, the best place to start is getting really good data at your disposal to help improve the decisions that you have to make. And that goes back to what we talked about at the beginning. Do you really understand the makeup of your workforce, your human workforce, and your AI workforce? Do you understand the skills needs of the business, which are going to change as strategy evolves, business strategy evolves, and then do you understand the and have an ability to deconstruct the work that people do in a way where you can start to map work to skills? And what you’ll see as you go through that is that certain skills in your organization that you may have a lot of today, you may not need as much of in the future, and so you’ve got a skill surplus. And so how do you rationalize and reconcile that? And in other areas, you may actually find that you have a skills deficit, where what you need is bigger than what you have, and then it becomes a conversation of, well, how do you address that skills deficit through a combination of humans and AI as you progress.
Jason Cerrato 44:52
So with that, I want to thank the audience for submitting some great questions. Greg, as always, I love partnering with you, so appreciate it. The thinking that you brought to the table and the discussion with you. Sure. I’ll be referring to this for some time going forward, and look forward to an opportunity to partner with you again. And with that, I think we’ve finished the plane on time, so I’d like to hand it back to the Argyle team.
Vicki Lynn (Argyle) 45:16
Thank you so much. Thank you, Greg, thank you, Jason. What a fabulous discussion. I want to thank everyone for joining us today for this session.
