Jason Cerrato 00:00
Sarah, thank you so much, Sarah. It’s wonderful to be here with HCI again, and it’s always wonderful to be partnering with Deloitte and with you, Sue, I truly enjoy the conversation. Then I think you and I have partnered in the past. I think the last session we did was in November, and the world doesn’t wait for anyone. It sure moves fast. So I’ve been waiting to talk about the 2025 report for a while now, and we sure have a lot to dig into. Just a week or so ago here at Eightfold, we were celebrating our annual customer event conference we call Cultivate in California, and we’re about to do it again in London, and what we’re going to discuss today was a hot topic, as we were talking about agents and talking about agentic capability and introducing the concept of digital twins. So this is a hot topic that is on the mind of a lot of people. And I remember Sue when you and I first met, this was something I mentioned to you that was on my mind, thinking around AI is not just a tool, but as a teammate. And flash forward, and it didn’t take that long, here we are. So I’m excited to have you join me today, and I’m ready to jump in, but I want to give you a chance to kind of introduce yourself to the folks that may not have been here before, and just say, welcome again, and thank you for joining me. How are you doing, Sue?
Sue Cantrell 01:26
Thank you, Jason. It’s always a pleasure. Really appreciate you inviting us and having an opportunity to talk together about this topic. I think the world, as you had mentioned, is moving so fast, and it’s having pretty big implications on us already when it comes to AI and and we’re going to take kind of a deep dive into one of our trends in this session, but I’m going to give you a little bit of an overview of a few of our trends, and we’re going to be having other sessions spotlighting other of them, so stay tuned for that through HCI. So this year’s report, we call it “Turning tensions into triumphs: Helping leaders transform uncertainty into opportunity”. There is no doubt that we are in an uncertain environment, technologically, socioeconomically, we have a lot going on. Just think about what has changed since January of this year. We didn’t know this when we actually wrote the report, but it has only increased and elevated, and it’s making our world increasingly difficult to navigate. So if you move to the next slide, we’re building on one of the core themes from last year’s report around what we call human performance. And it’s this equation here that human outcomes, things like greater well being, skills and employability. You know, all of the value that’s created for workers as human human beings, times business outcomes equals what we call human performance. And this is more than a plus sign. It truly is a multiplication sign, because, based on our research, they reinforce one another. If you create better human outcomes, you’re more likely to create better business outcomes, and vice versa. So this theme continues throughout this report. You’ll see it today when we talk about AI and its impact on the workforce. But one of the things we did in this year’s report is we really focused on evolving our thinking about these really complex choices and tensions that leaders have to make in a really uncertain environment. So things like, how do you plan when the only thing you know about the future is that it’s unknown? What are the tensions between things like augmentation versus automation, outcomes versus outputs, personalization versus standardization, stability versus agility. And where do we find how to navigate those tensions? Because it’s really not an either or choice. It really is an and so a lot of this report is framed around these tensions. You can see to the right that it is the largest longitudinal research project done in our space, in talent organization, human capital ever done. This year, we had over 13,000 survey respondents. We always include leaders, managers, workers, cross industries across 93 countries, and if you go to the next slide, I’ll give you a very, very quick tour of our trends, and I encourage you to check them out, and then we’re going to spotlight one of them you could move the next slide.
Jason Cerrato 04:51
So just one thing real quick, is the key word on that slide that jumps out to me is tension. Yes, we, I was talking with a bunch of talent leaders at a recent focus group, and whether you’re in a business that’s transforming for growth, or you’re or you’re in a business that’s transforming for efficiency and doing more with less and reduction, the common theme amongst these leaders was it’s never been more difficult to be doing this work. And it’s the pace of change. It’s the uncertainty, uh, assuming today, we’ve probably attracted a large HR audience, we often get caught up in analysis paralysis and this quest for perfection, yeah, and, and even if perfection was a thing that existed today, the pace of change won’t allow it to last for too long. So it’s this dynamic, moving world that I think is causing that word tension to jump out at me on that last slide.
Sue Cantrell 05:48
Oh, true, and it’s so challenging because, you know, the temptation is to kind of slow roll our decision making in an uncertain environment, but actually not making a decision can be a decision in and of itself, right with implications 100% so I’m going to give you a very brief tour, and then we’re going to dive into this, this one trend on how AI is impacting work in the workforce. We organized it into three buckets, work, workforce, organ, culture, just to make it easy. The first trend is really spotlighting that tension around stability versus agility. Bottom line leaders, they need dynamic agile organizations. Workers, though, just by human nature, need some stable ground to stand on. So how do you achieve that balance? So we coined the new term stability so that you can balance both. When work gets in the way of work is all about how, how, how busy we all are. If you ask any person how they’re doing at work, eight out of 10 people will say they’re busy and you know, a lot of that busy work is not necessarily value added work. So how do we identify that, cut down on that, and create some extra capacity in our work, so that we have better well being, so that we have the ability to think thankfully, so that we have the capacity to adjust in a dynamic environment. It’s more important than ever. We’re going to dive deep today into the question about, do we need to update our fundamental employee value proposition for an AI powered world? Closing the experience gap is all around the idea that our entry level talent, our early career talent. They’re having a hard time getting experience today, in part because AI is taking on many of those entry level, kind of foundational roles, right? Yet at the same time, employers are demanding more and more experience from their entry level talent than ever before. So that’s the experience gap. How do we bridge it and when, when AI is taking on so many of those kind of entry level tasks. Where’s the training ground for our workers, which could be a big issue for the talent pipeline in the future. The next trend is all about the question about, How do I get value out of all of our work and workforce tech? What is the new value case? Because the tech today is very different from what it used to be, even five years ago. Jason in particular, I’m sure you’re experiencing that at eightfold. It’s not just an efficient play anymore, although that is part of it. And then the three under organ culture. How do we use new advances in tech to understand what motivates people to behave the way they do at an individual level, which can drive all kinds of human and business outcomes. What do we do about performance management in an age where we’ve tried over and over and over again to reinvent it? Why is it still not working? Why do people still not like it? What can we do instead, we talk about engineering human performance and not relying just on a single HR process to improve human outcomes, and then the last one, we tackle the role of the manager. What is the role of the manager in this new world? There’s been a lot of press around. Should we go bossless? You know? What should we do with this role? That’s kind of a catch up. So that’s a very brief overview of the trends. I’ll let you say a few words, and then we’ll go in. Then we’ll go into a deep dive on the AI trend.
Jason Cerrato 09:26
And it’s just, you know how fast this is moving. I remember last year we talked a lot around the traditional fence posts that we use to organize work were pulling apart and they were decoupling, yep. But now, with this concept of stability and reading the report, now we’re looking for new anchors to try to build around and create some level of comfort while also driving agility. So as you know, the more traditional frameworks were pulling apart, we’re looking for some new way to have agile frameworks to at least have something to tether to. But then also, you know, we talked about this skills gap and this experiential piece that started to disappear as roles get automated or as organizations fly. And how do people learn? You know, there’s been a lot of discussion around the disruption in the L and D function, and how, as much as AI enabled L&D from a personalization perspective, what AI is doing to roles and career paths is also changing the nature of learning on the job. So now we have to come up with new ways to roll out learning in organizations, and it’s not so much of a push as it’s been in the past. It’s now a pull to keep up with the pace of how work is evolving. So I just think this is where we start to get into more collaboration and strategic collaboration, and new ways to look at mentoring, and this is where I know we’re going to get into this, but some of the implications and dangers come into this as AI can protect can potentially expedite the expert and distract the novice. And how do you allow for creating opportunities for collaboration when in some cases, you’re asking that expert to slow down and work with others, and that novice to understand when they need help and when the AI may not be giving them the correct answer that they should be questioning because they’re not an expert and they don’t know what to question. So this is the world we’re living in, and all of this is turning on a dime.
Sue Cantrell 11:40
100% and all of that needs to be baked into our fundamental value proposition for our workers, honestly, the stability piece, the how you learn piece in this age of AI. So that’s why we spotlighted this trend. The trends kind of intersect with one another, because when you think about it the people side of the equation. When we think about AI, it’s traditionally been about reskilling, AI, fluency, change management, role, disruption or job loss. But who’s paying attention to how it’s fundamentally changing the very experience of people at work and what they need, and what they’re going to be attracted to, and what it’s going to take to retain them in this age of AI, which is what this trend is all about.
Jason Cerrato 12:27
Yep, I know we’ve talked a lot in the past around this recruiting, retention, redeployment, redesign concept, one that I hear people using quite a lot now, is rebalancing right as we as we distribute work, and not just amongst people, but amongst technology, with automation and with agentic capability. How do we figure out what is done by technology and what is done by people, and what is the rebalance of work and of time and of capability? And what does that look like? And then what does that mean for how we design, you know, the org, and how we delineate work and how we make decisions and organizations. It’s a fascinating time to be in this space, but it’s moving so fast. It’s why that tension word jumps out so with that, one of the things we like to do when you and I get together and have these conversations is we like to keep it a little interactive and get a pulse for the audience. So we build in little little sections along the way where we like to ask poll questions, or like to ask some type of interactive step to see where you are on this journey. And right now we want to do our first one of these, and if we can get some help from the HCI team, what we’re going to try to do is ask a question of the audience and see if you can use their tools here to help with a little bit of a word cloud. And what we’re asking for is, what’s the first word that comes to mind when you think about working with AI, and even kind of expanding that question, when we’re talking about thinking of it not just as a tool, but as a teammate.
Sue Cantrell 14:13
Oh, helpful. Okay, easy. Oh, that surprises me. Complex.
Jason Cerrato 14:32
I want to give some folks some time. They may not have known they needed to be close to their keyboard to enter in some words here, but I do think you know the capability of the technology has advanced so quickly. If you remember, last year, there was a lot of conversation around thinking of AI as an intern on the team right, and you were adding an intern that could help you get some work done, but always you needed to check the work and kind of monitor the work and make sure that it was done correctly. So you always had to kind of check it and evaluate it. Now we’ve evolved to the point where we’re talking about everyone having their own kind of Chief of Staff, right? So I’m no longer an intern. It’s now been promoted based off of some of the advancements of this technology. So let’s see what’s, uh, what’s happened here. As the words are coming in,
Sue Cantrell 15:35
I see a mix of, you know, sometimes I see words like, confusion, complicated, hesitant, cautious, and others, you know, helpful, easy, efficient. So we’re seeing both ends of the spectrum here, untrustworthy, that’s a big one. Because, you know, I’ve often thought, are we going to move from a world of cybersecurity to disinformation security, and we’re going to need to make sure that the information that’s produced is trustworthy?
Jason Cerrato 16:17
Okay, so if the HCI team can put us back into the presentation, we will move forward. But I think from the word cloud that we saw there, you see a little bit of a mixed bag of responses, but you also see why we’re hearing so much around this concept of work design, right, trying to figure out what is the right use cases and governance for where to use the tool, where to use humans. You know, I was talking with a leader who said, you know, they really want to determine the best places to leverage artificial resources to maximize human intelligence, right? And try to figure out this, this blend of the two. And I think, you know, sometimes not every use case is the best for the technology. The same way, not every use case may be best for the person. Part of the opportunity here is to remove some of the toil from work and the things that people either just don’t want to do or aren’t very good at, if it’s monotonous or repetitive or, you know, very, very difficult, painstaking work. So let’s move on, but we appreciate your participation, and stay close to the keyboard. We’ll have a couple other polls as we go along. So let’s talk about this new era.
Sue Cantrell 17:39
Yeah, we posed a question, meaning it’s not definitive. And I’d love everybody’s reaction to this. You know, when you think about the narrative where we’ve been with AI, there was maybe eight years ago or so, a lot of attention on automation. That’s still there, right? This is a build. And then everybody was talking about augmentation, assisting and extending people’s capabilities, rather than replacing them, and then it built to kind of human and machine collaboration, right? Friending a team, friending AI, putting AI on a team, lately called the Super teams. And obviously that’s still still very present, but we’re wondering if we’re starting to enter into a new era that we’ve tentatively called “Convergence”, not that AI is going to literally converge with people, but that we’re moving so close to one another that AI is becoming so interwoven with our daily work. So some people in the field liken it to the metaphor of centaurs and cyborgs, right? So a centaur is half horse, half human, and we, you know, the human part gives the AI, the work to AI, which is the horse part, and then we pass it back to the human are we in a world that increasingly looks more like a cyborg, where they’re kind of indistinguishable from one another is so clearly woven into the work we do and the outputs that we produce. And we’ve listed here on the right a number of signals we’re seeing that could point to convergence. I mean, obviously technology is becoming more human with more human like interfaces. You know, we can code with natural language today. We have gesture and natural language interfaces. You know, technology is taking on more human capabilities. It can detect emotions, it can infer motivations. We’ve got the rise of humanoid robotics that increasingly resemble and mimic humans. And with agentic AI, we have AI agents that can act on our behalf. You know, we wrote a piece three years ago called Digital doppelgangers, and the idea was, can we replicate ourselves in a digital version? And sure enough, here we are in this world today where we’re beginning to see this so one of my favorite examples is with an automotive organization, and they had a man named Doug, who was retiring, who had very specialized knowledge. And instead of having him train everybody, they with his permission, listened, had AI listen into his phone calls, look at his work products, mine his keystrokes, and they created digital Doug, which was basically a digital doppelganger, an AI version of Doug that everybody could query and ask questions to when he left, and digital Doug could train other people on an ongoing basis. So there’s all kinds of scene signs that we’re seeing that we’re entering an era of convergence.
Jason Cerrato 20:36
I love reading some of these points and thinking back, like you were saying how you wrote that article several years ago. I remember, as part of digital transformation, we talked about how every company, regardless of industry, was becoming a tech company, and now, with these tools and this technology, every person is becoming a tech enabled worker, right? Yeah, yeah. So we’re all tech companies and we’re all tech talent.
Sue Cantrell 21:03
I love that. I love that we’re all tech talent. That’s absolutely right. That’s one of the blurs. So I thought we’d spend just a couple minutes on agentic, because that is a new thing in AI basically, the way I think of it is, you know, roles will kind of go into kind of three categories, what we call AI assisted, which is when tasks, you know, remain primarily human owned with support from Ai, ai augmented, that basically of tasks evenly split between humans and AI to deliver work outcomes. And then AI powered with tasks that are primarily AI owned with humans managing the AI outputs to monitor their performance and drive continuous improvement. If you want to go to the next slide, I’ll just quickly point to a couple other points about agentic. Agentic is really what we think of as the unlock in particular for HR, and so you’ll see how it’s different compared to generative AI on the left and then AI agents on the right. So not only can they just automate tasks, they can actually get involved and execute tasks right. AI can create and execute multi step plans to achieve outcomes. Right? Agentic is it dynamic. It can be ident dynamic, and it can, you know, have new, new information that gives to the human, for the human to make a decision based on potential different suggestions that AI gives them. One of them is that agentic AI has memory, which generative AI does not. And then I’ll, if you go to the next slide, I’ll just quickly cover the agentic part. This has all kinds of interesting implications on talent and the ability to almost create this idea of always on adaptability. So you know, with agentic AI, we can, we can be continuous in terms of our change always on agentic AI can help us surface like unused capacity as an example, can help us be data driven and predictive. Jason, you and I talk a lot about skills and how we can more fluidly. I love your word of rebalancing, how, how we can rebalance who does the work based on skills and based on AI. And of course, it can be agent augmented. So a little bit about agent tech and Jason, I’d love to hear how you’re thinking about this at eightfold.
Jason Cerrato 23:45
So one of the things you had on this, on your slide two slides ago, was the kind of scale across the different versions of AI. And there was a metric around time. And when we talked about this at our Cultivate event, our co-founder and co-CEO got up on stage and talked about how with the agentic movement, where he referred to it as entering in the age of time, and what we’re driving is not necessarily entire just efficiency. We’re giving back time, and time is the only resource that is not, you know, infinite. So when you think about that, this is something where there is only so many hours in the day, and with the ability for this agentic capability to work with greater autonomy, learn as it goes, make decisions, but also work simultaneously and not be single threaded like a human is. It creates greater scale at a greater rate. So there’s a tremendous opportunity to give back time, both to the individual and to the organization when used appropriately, but also it’s just this capability is, I think you had another word on there, the unlock. We’ve had people that have been using this and are well on their journey, and that’s a word that I hear them using quite frequently, that the technology has caught up with the vision and the wishes of where they wanted to go and what they wanted to do, and now it’s creating all these unlocks. And what I think is happening is that in the past, there were some things that humans wanted to do or could dream of, and the technology wasn’t always there. And now the technology has moved so fast, the technology is almost quickly surpassing what we thought of, and the humans need to catch up. And we’re going through this tension of, how can we understand the right strategies, the right applications, the right governance fast enough for how fast this is moving. Because, you know, the iterations and evolution of this capability and technology evolves exponentially, not just incrementally and it operates at a much different pace than it has in the past.
Sue Cantrell 26:23
I love that Jason, one of my favorite phrases here at Deloitte is you can only move at the speed of the organization, not necessarily just at the speed of the technology, which really gets to the heart of this trend we’re going to talk about. If you move to the next slide to realize the value and potential of AI, to do that, unlock that you just talked about, we’ve got to pay attention to how people are actually using it and experiencing it at work, how it’s impacting their very workforce experience, how the humans are interacting with machines because we’re that’s going to be critical, not only for human outcomes, going back to that human performance equation, but to realize the very potential of AI. So that’s what really this trend is about, is zeroing in on what is the impact on our workers. How do we design it and help our workers use it in a way that achieves that unlock both for them and for the business. So a couple stats here on the right from from our research, over 70% of managers and workers say they’re more likely to join and stay with an organization if it updates its employee value proposition to help them thrive in an AI driven world. And it’s different. It’s impacting every single element of what we think of as our traditional employee value proposition. But getting back to that earlier slide on convergence, the number one concern by workers. Interestingly enough, was not just job loss, but 54% of workers are concerned about those blurred distinctions between what is done by humans and done by tech or AI. I speak at a lot of conferences, and I usually will ask a question along these lines, and it always comes up as number one we’re fully aware of if, for example, if we create a work product with generative AI, who’s who’s accountable for it? How do we check it? How you know, what is it? Who gets credit for it? Am I rewarded for something that I use generative AI with? So there’s all kinds of interesting implications for this, this, this new AI powered world, where we’re working more closely with with AI than ever before.
Jason Cerrato 28:50
And I think this is why we I often talk about the requirement to think, manage and measure differently in this world. But this is why, when people start on these journeys, it’s not just a technology implementation or a technology initiative. There is so much of this that requires culture and leadership, right? Because this is really operating in an entirely new way. And you it’s the employee value proposition. It’s the way you message, it’s the way you operate. It is not just the technology for technology sake. It really is a new way of work.
Sue Cantrell 29:30
Yeah, and without, without really making that new way of work happen, we’re not going to get value out of the tech. That’s the realm. Actually, the next slide really illustrates this. So AI is inherently neutral, right? And it can have really positive or negative impacts based on how workers use it. So on the left is kind of the common narrative. Of course, AI can make our work easier. We saw a lot of this in the word cloud, actually, on both sides, but on the other hand, a lot of our research points to the fact that AI is making work harder. I mean, when you think about it, AI is often times taking over some of those transactional tasks, some of the routine tasks. What’s left? It’s the harder work. It’s the more complex work. The common narrative is that AI empowers us with new tools. Yes, absolutely, that’s true. But also AI can, you know, decrease autonomy. I mean, the easy, obvious example is rideshare drivers, they’re told exactly where to drive, you know, and exactly what maps. And we’re seeing this within organizations as well. On the other hand, if used in the right way, we have lots of examples of how AI can empower workers and drive greater autonomy. So we have to really be careful of these potential silent impacts. I mentioned earlier, when I was going through the trends that AI can actually be what some people call “a silent skills killer” because it’s taking on those training grounds for entry level talent, right? And a lot of people are questioning what happens to our critical thinking when we can write things with AI. We need ways to practice that and continue to build that critical thinking. You know, I brought up the digital Doug example about AI blurring the distinctions between what is done by humans and tech. Well, who gets credit for the work? Right? We’ve, there’s been study after study that AI can drive increasing loneliness and negatively impact well being we have, you know, obviously wonderful new available data and insights at our fingertips. But on the other hand, we have the potential for privacy breaches, right? So, I just wanted us to be aware that it’s both sides of a coin, and we really have to pay attention to how it’s being used in our organizations to avoid some of the unintended impacts that AI could create on our worker workforce.
Jason Cerrato 32:09
So I think there’s two things here. One bullet around AI can become a silent skills killer and create an experience app. I think a basic example of that, I can tell you the phone number of the house I grew up in, the phone number that was my grandmother’s phone number, the phone number of my best friend. I can’t remember my own cell phone number all the time.
Sue Cantrell 32:32
I don’t, I have no idea what my husband’s is.
Jason Cerrato 32:34
I don’t have to, I don’t have to know it. I just push the contacts or I hit the button, right? So just the way the phone operates, the way the tool works. It’s not something that’s required to make it run and again. For my whole entire life, I can remember all of those phone numbers because it was required to get something done that I needed to get done in the way that work was done, the way that was done. Then the other thing is, we talk a lot around jobs that will disappear or go away as a result of AI, but often that is either short term or misplaced. And in many cases, you know, there’s the academics that’ll say, AI will be a net job creator, or there are several examples where people thought AI would lead to a reduction, and it actually led to an increase in hiring. And I think this is an example of the opportunity for hiring will be in roles that didn’t exist before. And we’re hearing a lot around governance, or hearing a lot around hybrid roles in the HR space, there’s been a lot of discussion in the news with moderna and IBM around this convergence of HR and IT right? That’s a whole potentially new function with a variety of new roles, right? That may not have existed before, with new job titles. So the opportunity for roles may be disappearing at the same time, roles are being created for the work that will be needed and the work that will remain. And this is kind of the transformation and transition we’re living through in real time.
Sue Cantrell 34:18
Yeah, I love that. Jason, you know, I’m actually more concerned, to your point, about, you know, changing roles or displacing roles. I’m with you. Things will change, but we’ll always have human work. I’m actually more concerned about AI, kind of fraying the connections that we have with one another, and almost being an echo chamber for ourselves. So when you think about generative AI, it wants to please you. It’s telling you what you want to hear. And you know, like, are we going to what is that going to do to our critical thinking? I almost think divergent thinking is going to be more important than ever before, because we’re going to get into these narrow, you know, echo chambers. I’m worried about loneliness. I’m worried about those human connections most at work, and I think as as people professionals, we really need to build that in more intentionally, both the human capabilities of critical thinking, divergent thinking, but also those human connections.
Jason Cerrato 35:23
And I see that as one of I mean, there’s a lot of reasons, but one of the reasons for a lot of the return to Office, yes, initiatives that are going on. Obviously, there’s a financial component underlying a lot of it too, but it couldn’t be happening at a more important time as well for strategic collaboration and for kind of the learning that happens off the side of your desk that’s informal, and all of these things that have happened historically, but now with the tools that we’re using, and the opportunity to kind of isolate and get work done independently become all the more important. Yeah.
Sue Cantrell 36:02
So then that leads us to think about like, Okay, how do we think about updating an EVP for an age of AI? I’m not going to drain this slide. There’s a lot here, but let me give you a couple of examples. On the left is kind of some of the main categories that most people think of when they think of an employee value proposition. One of the areas is, can we share in the rewards that AI creates with workers? So one way of doing this um is if, if AI really is successful in reducing some of that, that busy work, or the non value added work, can we make you know, a lot of people are talking about this in the world. Can we give workers four day work weeks because they produce the same amount that AI had produced? Is this a way of sharing in the rewards that AI creates? So there’s a Canadian law firm that has done this specifically because of AI to share in the word rewards that workers create. Another example of sharing in the rewards is Shutterstock, and they trained its Shutterstock AI image generator on its photographs from people, but only after securing their permission and offering the photographers royalties for the use of licensed images they use to train the AI. Another example is waste management. They piloted a program that lets drivers stray from AI optimized routes, but gives them financial incentives for following the routes that AI suggests, and then it’s sharing some of the productivity gains in the form of wages back to drivers. So that’s just one of many areas, and I will pause and see if you have anything to say before I go on to a couple other areas beyond rewards.
Jason Cerrato 37:57
So this is something that I think is a bigger conversation that I’ve been interested in bubbling up like I said, when you and I first started working together, I was talking about this AI teammate topic. But in the last few months, the thing that I’ve been grabbing onto that’s been keeping me up at night recently is kind of how AI as a teammate will redefine our concepts of performance and our concepts of proficiency, and what does that mean for evaluating talent and Talent Management in HR, right? And you know how if it’s if we’re cyborgs, and it’s human machine collaboration, are we being measured on results and output, or are we being measured on the human component alone, or the human machine collaboration? So all of this is up for, uh, examination and being redefined, and as a result of those discussions and how you want to set forth that strategy, a lot of these can potentially be changed. So we talked about giving people back time, or changing rewards and compensation, or doing a lot of things around the way teams are structured. So again, I think all of this is multi-layered and very complex, but as you start to address one area, it opens up opportunities to potentially improve or make considerations in other areas.
Sue Cantrell 39:33
I love that. Jason, I’ve often thought, Should we have joint human and machine performance reviews? To your point like, do we evaluate the joint work of humans and machines? Is that so intertwined or separately and then, and then, obviously, it has all kind of trickle, trickle effects. Another area is learning. So, you know, when we think of learning, we think of it as, especially in the L and D function is, you know, making sure that the right content for learning is just in time that’s relevant to the workforce and when, when? When we think about AI. Almost every organization I know has kind of an AI fluency program for their workers, but I wonder if we need to think about learning in a different way as well, which is that people need to learn how to learn from AI, and AI needs to learn how to from people, right? So one example of this is Repsol workers analyze AI generated production options, and they incorporate context, and they feed their analyzes back into the AI system to improve it. So they’re teaching AI, and then AI learns from workers, and then the workers learn from the AI. So there’s that. And I think instead of thinking about humans in the loop, maybe we need to think about humans on the loop, with AI agents consulting humans when they get stuck in executing a task, kind of like a junior employee might, with a senior counterpart, in a way, so all kinds of implications for learning. I think one of the areas that we can think about for our updating an EVP is hard skills. We have a lot of focus on reskilling, but do we really need to make it part of our EVP to also help workers cultivate those human capabilities, the ability to learn quickly, curiosity, compassion, emotional intelligence. You know those are going to be there’s a reason that Deloitte, we call them enduring human capabilities. There are those foundational skills. If you look at any World Economic Forum, state of jobs report, those are, those are the skills always at the top of the list, year over year, and they’re increasingly more so in an age of AI. And can we, can we actually really help develop and cultivate them as part of our EVP? Obviously, as part of our EVP, being able to just simply give leading edge tools to our workers is an attractive capability. And then I’ll say one more thing, which is, can we democratize access to AI to empower all workers? So one fun example was I interviewed a software company, and an executive told me that he found somebody who was kind of self automating their own work with AI, but under the covers. And he said, quote, here’s what we don’t want to happen. He said, We don’t want workers who self-automate to keep this to themselves. We want to reward their agility and curiosity and then reshape their roles accordingly. So, so, so, you know, I think the ability to let people play with AI, experiment with it, support them, and learning to use the tools well and responsibly is an important piece of the EVP.
Jason Cerrato 42:53
Yeah, and I think, and I’m sorry I keep going back to comparing this to digital transformation, but I’ve been in the transformation space now for 15 years, and lived through digital transformation. Digital transformation. So I keep comparing it as part of digital transformation. One of the employee value props that came out of that was, remember, bring your own device. Yes, of course. Well, we’ve had several articles in the last year or so around senior leaders talking about, in the future will have people bringing their own agents. Yeah, right. So not only, not only will you join an organization and use the technology provided by the organization, but they’ll be hiring you for your portfolio of agents that you bring to the table with your own personal knowledge, especially as they become increasingly tuned to you.
Sue Cantrell 43:40
Yep, yep, yep, I love that. And then when you think about skills and skills powered organizations, is it the skills of you and the agents combined. So there’s a lot underneath this chart. I’m not going to drain it. I encourage you to check out the trend some of the other areas, you know, is, how can it be how can AI be used to create more meaningful work, to tap into people’s personal motivations, you know? How can it be used I had mentioned one of our trends was around creating more slack time, you know, that would be an important part of the EVP. And how can we use AI for greater agency and autonomy for workers, the opposite of that silent impact slide, where, where we can. We can, you know, potentially reinvent there’s very shape and structure of our organizations to empower workers close to the front lines. So we’re embarking on a new research project on how AI could impact work structures and decision making. So stay tuned for that.
Jason Cerrato 44:48
Deloitte was kind enough to invite me on one of the Future of Work podcasts with the European team, and I talked about how, you know when with the move from on prem to the cloud, we were talking about freeing up agility with data and removing it from gatekeepers and from owners to empower line leaders with the right data at the right time to make business decisions much quicker. And I think with this talent transformation, we’re talking about freeing up talent right to move, to move across the organization, to flow to work and to drive business. Agility through talent agility right to deliver faster, to pivot faster, to meet the needs of the customers in new ways as the world changes. So that’s just another way where as part of that, by pulling on all these levers and changing all of these different components, you can drive, potentially a variety of new employee value propositions and drive sustainability in careers in a new way that’s different from how we’ve done it in the past with career ladders. Right? Absolutely.
Sue Cantrell 46:01
I like to think of AI almost as enabling us to treat everyone like a hypo or a high potential for the first time, because it’s scalable so we can use AI as a coach. One of the examples I didn’t talk about was how Amazon specifically developed an AI coach for its early career talent. So we don’t need, necessarily, a personal like a human being coach. We can give, I mean, eightfold is doing this in spades. We can. We can move people to stretch assignments, you know, which is typically in the past, only reserved for high potentials, and today, we can move people and let them grow through experiencing new work. And then you had mentioned Jason everyone, not just as an AI intern, but what did you say that sheet of staff, of staff, right? So I get excited about the ability of AI to help us grow more than ever before.
Jason Cerrato 47:05
Okay, we have another quick poll. So if the folks have hung on with us, I’d love to ask a polling question, which are the following: what do you believe organizations should do to improve their ability to help workers with AI to create better human and business outcomes? So we’ll give you a chance to select, and I believe here, we’ve given you the option to select potentially more than one answer, because it says select all that apply to if the HCI team can help us here, see, we’ll see how these come through.
Sue Cantrell 47:36
Yeah, the first two are really about like, you know that the people function, the HR function, needing to get more involved in tech than ever before in different ways, and we saw that with a recent example of the ID and HR functions merging. Let’s see what’s coming up top, Jason, practice responsible use of workforce data and AI to create trust is huge, huge. Yeah, yeah, especially when we think about it, Jason, we’re going to be a lot of talk out there is about like we’re running out of data, and so we’re going to have to create synthetic data. And what does that mean for responsible use of data, and is it trustworthy? What’s next? Study how different types of workers work with AI to improve it. That’s the heart of this trend. Understand how workers are working with AI, and that is really as kind of stewards of the people discipline. We need to join forces with with tech, because, Jason, you, you said it earlier, it’s not just a tech implementation. And we, we bring human discipline to play.
Jason Cerrato 48:52
One of the things that came out of our Cultivate event, and that I think is involved with the stability concept, is in this method of working is to learn and iterate right to learn and iterate and again, to try to avoid that analysis paralysis and quest for perfection, and you can take minimize calculated risks, but in the right areas, and learn and and iterate and improve, and learn and iterate and improve, but it’s, it’s not a strategy to kind of stand still.
Sue Cantrell 49:31
Absolutely right.
Jason Cerrato 49:34
Okay, thank you so much. I think practicing trust is where you have to start. So no doubt that that was a leading answer there. So let’s see some other examples of how AI is revolutionizing work. I think this came out of the report as well Sue, yeah.
Sue Cantrell 49:51
I mean, beyond you know, the big thing was, like rethinking all the elements of your employee value proposition. But there are a couple three, three key principles. I think I’ve said this a couple of times before, which is to really understand how workers are using it, and look at the silent impacts. Look at the unintended consequences. One of my favorite examples is Intel, and they employ a team of social scientists who study how AI and human workers interact, including the unintended impacts at a very basic level, and share the plan for AI with workers. They do this very well transparently. Spell out how AI will affect work and create value for workers. Emphasize the positive, and then we’ve touched on this as well. Forge a relationship between the HR and tech functions. It’s more important than ever before. It can look, it can look, you know, it can take different shapes. So at USAA, you know, it’s just a very strong collaboration. You know, my sense is that anytime you redesign work, the human discipline needs to be at the table, the tech discipline needs to be at the table, and then the people actually don’t do the work, the workers and the managers themselves, they know the work best. So 30% of executives said they believe that to create better human and better human and business outcomes, they need their CIO and their CHROs to join forces.
Jason Cerrato 51:19
And I think that story ties in well with this visual, because this talks about how these changes are impacting the HR function specifically, and how the traditional work that’s been done in HR is almost being turned upside down as some of the transactional, administrative work gets automated, and now What’s left is a lot of the strategic workforce planning, insights and solution type work, which is more of the forward thinking predictive design work, especially aligned with some of the moves we’ve heard of in the market around designing things going forward. And it’s freeing up the time to do that work because some of the traditional functions that reside within HR, the transactions of the administration are now being either automated or done by other tools, so you now have this strong collaboration focusing on kind of this, almost this chief of work role, yeah, But between between HR and IT.
Sue Cantrell 52:21
Yes, and it can’t be owned by either one of them. I mean, that’s the thing. I oftentimes ask, who owns work, and it needs to be a multidisciplinary, orchestrated approach. If you go to the next slide, this is kind of a model we’re playing with around like how AI would impact HR. And in the center, we have kind of the HR strategy, governance, portfolio management, and that’s really AI assisted. And then some of our traditional roles in HR we’re imagining are going to evolve. So the traditional HR business partner role might evolve into something like workforce solution architects, you know, using primarily ai, ai, augmented types of work. And then the next evolution of the people analytics team, it might evolve into something like an innovation and insights hub. I’ve been a big fan of organizations looking at creating work experimentation hubs, if you will, that go beyond pilots and test things like with AB testing. And that would be AI augmented. And then obviously, solution delivery is huge. The next evolution of HR ops and HR tech will feature a full transition, transition to Agile delivery. And there’s been a lot of talk about maybe combining that type of work with other operations and from other functions, and kind of being a cross functional kind of operations group, and then around, around the circle, the next evolution of HR services, you know, will include the final extraction of kind of the remaining administrative, operational work from centers of excellence and HR business partners. So that’s really AI powered. That’s where the agents will come into play. So it won’t be self service anymore, which a lot of people have griped about, but that’s where AI agents can really help.
Jason Cerrato 54:25
So just like the last round of transformation required HR to move towards centers of excellence and shared service, this round of transformation is requiring a new HR operating model as well, focusing a lot on governance, portfolio management and all these different workforce designs and solution design roles which didn’t exist before. So it is exciting and fascinating, but like I said, there’s a lot of factors at play, and for all of these conversations we’re having with HR leaders, it is a lot to take on, because to do this right, you have to kind of look at all of the pieces simultaneously in concert, as we’ve discussed here over our time together today. Okay, so with that, we do have one last poll question, what’s the most significant shift you’re seeing in HR due to AI. So we’ve talked about some of the research coming out of the Deloitte trends and some of the things we’ve seen in our travels. But as you’ve listened to us, and as you’ve experienced this experience this in your organization, what’s some of the most significant shifts you’re seeing?
Sue Cantrell 55:42
Hmm, yeah.
Jason Cerrato 55:47
I think that’s not surprising as this evolves and expands, the first things that were often addressed were the items that were self contained within HR. And now, as we get further into the journey we’re doing the work that touches other functions and other and other areas of the business. So that’s where a lot of organizations are now.
Sue Cantrell 56:13
And that’s only going to continue. It’s huge, and it’ll take many, many forms. Right integration doesn’t have to be done one way.
Jason Cerrato 56:23
Okay, perfect to keep us on time. Here, can the HCI team put us back? And I think with that, we have landed the plane on time. We do have some QR codes, if you’d like to engage with this information a little deeper, so you have the ability to get access to some of this, there also was the ability to download it as was shared in the beginning. I do want to thank everybody for joining us today. I also want to be sure to thank Sue for joining me once again, and for anyone who’s been with us before, I’m sure you will see us again, because it’s always such a pleasure to talk to you and learn from the thinking that Deloitte is sharing with the market and to often share a lot of the things we’re seeing with our eight fold customers here, with the work that we’re doing. So with that, again, thank you for everyone joining us.
