Sooner or later, every AI rollout runs into an org chart question: Who owns this? The CIO controls the technology. A chief AI officer, where there is one, sets the strategy. HR, which is responsible for the people being asked to change how they work, is often the last to hear about the decisions.
This episode of Keep Moving Forward is adapted from the SmartBrief webinar “Is HR the Real Chief AI Officer?”, moderated by Mackenzie Putici. I joined three people leaders: Laura McGann, chief people officer at Prosci; Harry Osle, chief human resources officer at The Hackett Group; and Matt Poepsel, vice president and godfather of talent optimization at The Predictive Index. Their answer to the title question was no, but not because another function deserves the crown. Matt called the question of who owns AI “a very dangerous question to ask.” AI cuts across every function at once, so a chief AI officer only succeeds when HR, IT and the business work as one system.
AI Doesn’t Care About Your Org Chart
Matt’s case against a single owner starts with complexity: Once a function gets complicated enough, it gets its own department. And fear soon follows. “When I start to fear, I start to carve out my fiefdom,” he said. “AI has none of that. It has no fear, and it’s not overwhelmed by complexity.”
AI provides a way to counter the risks created by those silos by adding context layers so AI can see information across the whole enterprise. “If we go back and try to erect them in our human systems, it’s going exactly the wrong way,” Matt said.
Laura feels the shift in her own calendar, now full of conversations among HR, IT and engineering, legal and risk, and the business leaders who own the use cases. “AI opens up this very different operating model where you have got to break the silos,” she said.
Our research shows what it costs when the walls stay up. Nearly a quarter of the HR leaders we surveyed don’t know how their organization is adding AI engineering capacity. Inside the same companies, HR and AI leaders were 47 points apart in their confidence about sourcing AI-capable talent.
Everybody Has a Role. Nobody Gets the Crown.
Harry’s answer starts by taking HR out of the lead role. “I don’t see HR as the leader of the AI strategy,” he said. “The CIO chooses the technology. The chief AI officer will choose a strategy that needs to be deployed, but HR plays a critical role in how the workforce leverages and uses that AI.”
HR’s piece, in his words, is to be “the architect of an AI-enabled workforce.”
Laura agreed there’s no single owner and put senior leaders on the hook for the change. “They need to own this. This is a leader-led change,” she said. HR’s job, in her view, is to coach those leaders, push back on them, and bring workforce data on readiness to the table.
In that model, the chief AI officer sets the direction and still depends on IT for the tools, on business leaders for the use cases, and on HR for a workforce that can turn the investment into value.
HR, Heal Thyself
Matt turns the same scrutiny back on HR. “It’s disingenuous for us to ask everyone in the building to reevaluate and reconsider their work if we’re not willing to do it ourselves,” he said.
Laura called it “an uncomfortable truth”: HR needs to get more technical. “Maybe we’re not coding software, but we are coding and re-engineering our workflows,” she said.
Harry’s method works for HR’s own jobs as well as anyone else’s. “You have to stop looking at jobs and start looking at processes,” he said. Then question every work step: “Have we been doing them for all the wrong reasons?”
I’d draw the line a little differently than Laura. HR doesn’t need to learn code, but it does need enough technical fluency in how AI capacity gets built and staffed to earn, or demand, a seat in the staffing model decision, the same way it already has one for comp and benefits.
The business, technical, and people systems in an organization are all tightly integrated. A chief AI officer can lead the way. Success depends on how well everyone can work together to get to the destination.
Transcript
Matt Poepsel:
Everybody has a relationship with HR. You know, the nature of that relationship we could talk about.
But the fact is, being able to plug in and have a very switched-on HR team that is seeing themselves as people capability, and they’re plugged in from the entry-level individual contributor all the way up to the CEO and everything in between, that is a very powerful backbone of an organization if we choose to look at it that way. So I’m very excited about it, but I think that this function of who’s gonna own this AI thing, I think that is a very dangerous question to ask.
Gemma Versace:
Hey everyone, and welcome to Keep Moving Forward, the podcast from X-Team for tech professionals who are passionate about growth, leadership, and innovation. I’m your host, Gemma Versace, Chief Customer Officer at X-Team.
This episode is adapted from a SmartBrief webinar I joined alongside three people leaders, moderated by Mackenzie Putici. The question on the table was how big of a role HR should play in shaping AI strategy. Our own research at X-Team makes that question urgent.
Nearly a quarter of HR leaders we surveyed have no visibility into how their organization is adding AI engineering capacity. And when we asked about sourcing AI-capable talent, HR and AI leaders inside the same companies were 47 points apart in confidence. Those are two functions looking at the same workforce and seeing very different things.
Joining me in the conversation were Laura McGann, Chief People Officer at Prosci, Harry Osle, CHRO at The Hackett Group, and Matt Poepsel, Vice President and Godfather of Talent Optimization at The Predictive Index.
We talk about why HR needs a seat at the table before the technology decisions get made… why you can’t build an AI-ready workforce without knowing what your people can do today… and how to decide what to automate, what to augment, and what to redesign entirely. We also get into why AI adoption has no clean beginning, middle, or end, and what HR has to do differently to play a more consequential role.
Let’s get started.
Mackenzie Putici:
To set the stage, I wanna look at how HR’s mandate is changing as AI moves beyond individual productivity tools and begins changing how work itself gets done.
I’d like to ask you all, why is it important to bring HR and talent leaders into the conversation and decision making early? And Harry, why don’t you open this up for us.
Harry Osle:
Well, let me submit first, Mackenzie, that I strongly believe that AI is not just a technology initiative, it is a work design and workforce initiative.
As you change work, you’re obviously impacting the workforce, so AI changes will naturally change jobs, skills, roles, and organizational structures. Technology, as I see it, tells us what AI can do. HR helps in driving the organization’s ability to adopt the technology, use it, scale it, govern it, and realize value from the AI as much as possible.
HR’s role to me is really, as I work with hundreds of our clients and as a CHRO of our company, you know, in leveraging AI itself, HR needs to help design the workforce strategy before the technology decisions are made. To me, you can’t bring in HR as a consequence of AI. You have to bring HR in upfront to help design kind of the future of work and increase the capability of that workforce. And by capability, I mean skill sets of that workforce so they can leverage that AI as much as possible.
Mackenzie Putici:
Thank you, Harry.
Laura McGann:
I think, Harry, you said the big word, access is not adoption. Right? So access to this technology is not adoption.
And I think we all know that AI creates real value only when people adopt and use it well. So I think that is the key to this. Everything you said earlier, I agree with, right? It’s changing all of our workforce strategy. The other piece of this, I think this is a leader-led change, and our senior leaders are really gonna need to sponsor this change, understand what it means for their specific functions, and really help with that adoption path.
So getting folks from, yeah, I have this new cool tool, all the way through to, wow, this is really changing the work that I do, the work I do with this technology. And as we all know, the work that technology is gonna do for us, you know, completely by itself, which has huge implications for how we show up in the work as humans.
So this leader-led change, I think, is probably one of the biggest pieces of this. And HR, right, is not just a downstream comms and training function in leader-led change. We’ve really gotta be upfront in being that leadership partner to turn this AI strategy and our company strategy using AI into workforce adoption and real business value.
Gemma Versace:
As you guys have just heard from both Harry and Laura, you know, they’ve kind of framed their answers from inside HR, and I can add a little bit of an outside view that we are seeing across the market and clients that we serve.
And specifically in our Out of Sync report recently released by X-Team, you know, we recorded that nearly a quarter of HR leaders responded that they simply did not have insight into their organization’s AI engineering capacity or AI strategy. So, you know, to confirm, that’s roughly one in four HR leaders who are not being bought into the decision making early enough.
Not only not getting bought in early enough, but, you know, not being included in the decision at all. And you can’t build, I think, Laura just said this, you know, you can’t really build or contribute to building a credible workforce strategy around a decision that you’re not a part of. And this is something that HR leaders should be, you know, consistently advocating for, to be in the room when decisions are being made relating to AI and the impact that it’s having on how their organization is changing, how work is getting done.
You know, but there are good news stories out there, organizations who are giving HR a central seat in the strategic direction itself, and not just, again, as Laura said, you know, the change management rollout post the decision being made. And a recent example that I can give was a conversation that I had with the CTO and the CHRO at Avalara, where they really spoke about building the workforce plan together from day one and are seeing great success as a result.
And I think that that’s something that, you know, many HR and AI leaders that are listening today, we should be able to, you know, take that as a key learning that, you know, you don’t want any daylight between your CHRO and CTO when you are building out the talent strategy for AI.
Okay.
Matt Poepsel:
Yeah, Gemma, I’m equally frightened and encouraged by your statistics. I think that the thing that we’re leaving one in four HR leaders outta the conversation, that seems pretty ill-advised, but I’m encouraged that when it does happen, you’re finding a lot of success and a lot of moving the organizations forward.
I think whether we use terms like digital transformation or AI transformation, they sound very technical, but in reality it comes down to human transformation, and a more people-centric workforce is absolutely going to be the future of competitive advantage, regardless of how much technology we bring in.
So for that reason, I see that HR professionals are absolutely central to that long-term success. The reality is that technologists, engineers, even business people are not classically trained in people science. And our HR leaders have absolutely made this their vocation, and they’ve gone to school for these things and they studied these things in a way that, frankly, functional leaders just either haven’t had the opportunity or haven’t necessarily thought to do.
So, one last point is that HR sits really at this curious intersection between the employer interest and the employee interests, and I think that’s exactly where we need to sit for a transformation of this magnitude when it gets down to it. Because anything that’s one-sided or tilts too far one way or the other is going to cause really long-term ramifications.
So yes, cheers to HR. Let’s make sure that they’re part of the conversation early and that we equip them, empower them, and enable them to guide us through this transformation that’s going to be so profound. I think we really are only just beginning to understand it.
Mackenzie Putici:
Thank you, Matt. I have to completely agree, I’m excited about this transformation. Obviously there’s a lot of challenges here, but really interesting to see the shift in where HR comes in and finding that sort of crossover between the money and the outcomes and the talent and the people.
And I do want to dig into that a little bit more around sort of the talent. And I know there’s been some recent figures where people say, okay, we know what our talent is and we know how to address them and use them, and others who maybe aren’t getting the results or know how to get their AI talent. So Gemma, I want you to lead this one. What does an organization need to understand right now about its workforce to build a credible AI talent strategy, and why do so many companies maybe still lack this visibility?
Gemma Versace:
Yeah, absolutely, Mackenzie. And I think, you know, to start to answer the question more, the second part of that question around lacking, to expand on the report that I referenced in my earlier answer, you know, we surveyed 324 technology, HR, and business leaders for the Out of Sync report, and the clearest finding was a 47% confidence gap between HR leaders and AI leaders relating to their organization’s ability to source AI-capable talent.
And so let me kind of break down the stats for you. That’s 31% of HR leaders surveyed reported confidence in their organization’s ability to source AI-capable talent. And that’s in comparison to 78% of data and AI leaders within the same organizations. And, you know, to state the obvious, that’s not a small variance. That’s two functions really inside the same company operating at almost opposite assessments, you know, of the same capability.
That’s not only a confidence gap. A quarter of HR respondents also told us that they don’t know how their organization is looking to add AI engineering capacity in the first place, you know, whether or not they augment, whether or not they contract, or if they build the skills internally. And I think that this points to more of a structural visibility failure and not a knowledge or effort gap.
Matt Poepsel:
Yeah, I would build on that by just saying that it’s really key to understand that there’s just not enough talent out there to fill all the AI roles that we’ve got. We’ve got to be able to grow it from internally. And not everyone’s at the same place. They don’t necessarily have the same appetite when it comes to their interests, their readiness for AI. So being able to understand that is really key. But I know, Harry, I think you’ve got even more insight on this than I do.
Harry Osle:
I would say, yeah, Matt, I agree with you. You really can’t build an AI-ready workforce if you don’t know what the capabilities of your workforce are today. First, I’m a big believer, as you know. At the center of The Hackett Group is all this rich data that we have, that we benchmark companies with across the different dynamics of the, you know, of the enterprise functions.
And I’m a big believer in leveraging real data to understand what people do, what they’re capable of, and how ready they are to work differently. What we’re seeing is that most companies know their jobs and head count, but they don’t really understand the workforce’s underlying skills and capabilities.
I think you start there. You’ve got to understand, as you go through, as you mentioned earlier, digital transformation and AI transformation, as Laura mentioned also, I think you really have to understand what capabilities your workforce has today. What are the skill sets that they’re gonna need for tomorrow? You have to build that into your structure. The biggest workforce AI problem isn’t a lack of technology. It’s a lack of visibility into the capabilities of your people and how they’re gonna drive the most with the technology.
That’s the most impactful decision you’re gonna make to help, or the Human Resources group is gonna be able to make, to drive that technology forward. To drive a sustainable change in how you do work, you have to understand the capabilities of your workforce and what you need in the future.
Matt Poepsel:
I would just complement that too by saying we know that in order to embrace change and to be flexible, workers need to have a certain level of safety, psychological safety. Also trust, which is true, but I think one thing that we overlook is hopefulness.
I think that a lot of times we are so excited about these AI transformations and what they’re gonna produce on the bottom line, but we lose sight of the fact that people are asking the question, well, what does my future look like? What gives me hope? I understand what’s in it for you. What’s in it for me? And if the local manager for that person who we’re asking to take on these new AI tools can’t spark hope in the people who are being asked to go through this change, that’s a major miss.
So something’s getting lost as the communication and the mandates roll from the top all the way down. And it’s no wonder that people are digging in their heels a little bit more than certainly we would like them to.
Laura McGann:
Yeah. This is exactly the space Prosci plays in and I play in, in my internal role as well, but I break it down to really simple things, right? Leaders need two things. A work map and a people map, right? The technical side of the change and the people side of the change.
So what you all have just shared kind of fits perfectly into those two sides, right? That work map is that task-level view, the activities, decisions, handoffs, accountabilities, where AI is really gonna come in and automate or augment or where it’s actually gonna create risk, right? And that’s very technical. I think we can all get our hands around those things. And then also look at the opportunities that AI gives us on the technical side to actually re-engineer workflows and maybe do some things differently than we have in the past.
The people map, which you’ve both talked about a lot already, which is really like that people map around readiness, AI literacy, confidence in, you know, tech, trust. I mean, the bigger things that we need to get at. Do people trust technology? Are managers ready? What’s driving resistance, or what’s driving some of that concern around change?
And you mentioned the big ones, right? It’s job security, professional identity, our autonomy, fairness, data privacy. I mean, everyone’s got that huge list of things that people are very scared about. And I think even just to bring it home and personal, I think for folks listening to this and probably all of us on the call, professional identity has changed.
So I think, Harry, you said, you know, we’ve studied this, and Matt, you know, the science that we all bring to our roles, that science is accessible to a lot of folks now, right? And so how do we work even ourselves with AI, right, to bring the best thinking through this transition of our workforce? This is a really unique opportunity, but I think HR coming together with AI and with our leaders to bring all of those things together is where there’s really a good opportunity on both the technical side and the people side.
Mackenzie Putici:
Laura, absolutely. I think you’ve all put down some interesting examples here of the fact that, hey, work is changing, and with AI coming in to the workforce, we need to think about our identities and how we see ourselves in the work. Harry, you had mentioned that, you know, there are people with a lot of skills in the workforce, but maybe they’ve gone overlooked over the years, or as we start to have to look at what work needs to be done and who needs to do it in new ways, there’s some opportunity there.
So what I wanted to dig into next is this transformation. And so when AI can perform maybe parts of a job, but rarely the entire job, how should organizations decide what to automate or what to augment and what work needs to be redesigned altogether? Matt, I’ll send this to you for the lead.
Matt Poepsel:
Yeah. I think a lot of times we think about the worker, the entry-level worker, perhaps somebody who says AI’s gobbling up a lot of the job. But the truth of the matter is, for certain types of cognitive processing, the current AI is more effective than even the CEO at processing certain types of information at large volumes. Not certainly the judgment, the experience, all these things, but no one’s impervious to this. And I think we lose sight of that sometimes.
But while not all the work needs to be redesigned, I recommend that all of it get reconsidered. I think that there’s just such a fundamental shift in what’s possible now. It would be a miss if we were to say, oh, this job is impervious, or this job is not gonna be affected by AI. Are you sure? I wouldn’t be so sure. Certainly not today and certainly not tomorrow. So I feel like that’s an important part of it.
And in fact, a lot of the research now, because the nature of AI’s capabilities to contribute is really spiking, it’s always been known that, in my view at least, the most important and dangerous form of work is team-based work. We’re now seeing digital teammates joining teams, and to understand the psychology of that, the interpersonal nature of working with things that increasingly look to be human in some ways, but they’re not really. And how does that work? You know, this is the next frontier, and it’s something we’re gonna have to figure out, you know, if we’re going to unlock the levels of not only just performance and productivity, but also work experience.
It’s gonna take some time. There’s gonna be some pain. Some people are not gonna be as comfortable as others at first, but ultimately that’s where we need to get when we’re getting to real transformation with a capital T, not the lowercase t that we’ve kind of satisfied ourselves with over the last, you know, 12 to 18 months, I’d say.
Harry Osle:
I think, Matt, you did a beautiful job of stating that everything should be reconsidered, or consider everything, every process, every job. You have to at least consider it. And first, I would say you have to stop looking at jobs and start looking at processes. The work itself. You can look at a job, but you have to understand and decompose that into the processes, tasks, steps, work steps that make up these jobs. That’s first and foremost.
And then you have to ask yourself, do these tasks, work steps, are they needed? Do we even need these work steps? Have we been doing ’em for all the wrong reasons? You have to decompose these jobs into individual processes, tasks, and work steps. This is re-engineering 101.
Ask, what can AI do better in these specific work steps? We have a tool, AI Explorer, that does that specifically, not only internally, but for our clients as well. At the work step level, it looks at it, considers it, and says, okay, here are six or five different ways of driving AI in this specific work step. That’s the level of detail you have to get to in order to maximize the impact of that process.
The future isn’t really, you know, human versus AI. It’s really redesigning the work so humans and AI are doing what they do well to drive the best impact for that specific process.
To me, AI is indirect labor. You have to utilize or think about AI as indirect labor and think of your talent as direct labor. The combination of both those are gonna be able to drive the work in the future. How you maximize the skills of those individuals is very, very key. And I know, Gemma, you have a perspective on that as well, and I’d love to hear that.
Gemma Versace:
Yeah, absolutely. And, you know, again, to reference the research that was conducted by X-Team, our research found that the most mature organizations are using staff augmentation as part of their talent strategy, and not just because it makes ’em faster. Speed wasn’t referenced in the data at all.
The difference really shows up in what accumulates afterwards. And we did that across four measures. So whether the organization captures strong value from the work, whether it tracks outcomes, whether the training is structured, and whether the governance is actually embedded into the workflow as well.
And so embedded, long-term partner teams scored the highest on every single one of those four measures. So 85% for strong value capture, 77% for tracking outcomes, 66% structured training, and 47% embedded governance, versus internal-only teams scoring 42, 47, 35, and 30% respectively on those same four measures.
And so for us, short-term contractors showed to be the weakest across the board, especially on the governance, at just 13%. So the answer to your question is, you know, automate the parts of the job that are genuinely mechanical and don’t need to compound. Augment, ideally through an embedded model, the parts where you need the capability to build up over time, you know, outcomes tracked, training structured, governance being embedded, and redesign the job itself when the AI-handled slice, you know, is big enough that the remaining human work no longer resembles the original role. I mean, I think that’s the ultimate goal.
And, you know, one last point is this is not a build versus buy decision. It’s, I think it’s more about building capacity and the staffing model that an organization or leader chooses. It really determines whether that capacity compounds over time or evaporates at the end of an engagement. But I’d love to hear what you think too, Laura, on this point.
Laura McGann:
You said the magic word, Gemma, I think, capacity and capability, right? So, you know, AI is not the strategy, it is an enabler of your business strategy. So I think for a lot of us, when we first came into the AI conversation, it was, what’s your AI strategy? It was kind of, it’s the wrong question.
So what is your business strategy? How are you then using AI to enable that? And that kind of creates a different real question. That’s not what can AI do. It’s how do we intentionally redesign work, recapture capacity so that people and AI can create more value together, right?
And you have choices around what to do with that capacity, right? You can choose to capture it and maybe do some cost reduction. I know that’s, you know, part of the conversation that’s real. You can also capture it and redeploy it towards innovation. From an employee experience perspective, I also think you can recapture it for wellness and for, you know, doing things that we haven’t had time to do over the past, you know, post-COVID era, when everyone’s been so frantic and overwhelmed because absolutely everything that we’re doing and how we work is changing.
So this capacity question, I think, really is a big part of this. And these are really leadership decisions, right? It’s not just a technical configuration and process change, all of that is really important. But I think as you’re thinking about AI as an enabler of your strategy, you’re looking at some of those core processes that really drive value and enable your strategy. There’s lots of opportunities to repurpose capacity towards higher-value work, which is a huge opportunity and really exciting, actually. Okay.
Mackenzie Putici:
I love that. Bringing it back to the human, right? We’re all still in the workforce here, and I think a lot of times it’s efficiency, it’s ROI, it’s numbers, it’s faster, it’s better, but reminding ourselves that, hey, there are other things we can track, that we can put that energy towards now that we’ve freed up some time potentially with AI implementation.
So I wanna look at adoption and kind of get into that a little bit more. There’s often a large gap between the executive enthusiasm for AI and then how employees and managers experience it. Laura, I know you work with so many companies to manage change, so what has to be true exactly in an organization for AI adoption to produce these meaningful changes in how people work?
Laura McGann:
Yeah, and I think first we have to recognize that AI adoption, AI integration is not the same type of change as we have ever faced before. It does not have a clear beginning, middle, and end, and it is not done. So, as you know, folks who have been doing change work, a lot of us on this call, and there has always been some really clear methodologies of, you know, start, middle, and end, and that is just not the case with AI.
So we’re in this perpetual loop of, you know, understanding what is happening and figuring out how to very quickly move through, you know, awareness of, we use the ADKAR model at Prosci, so awareness of what is changing, why we need to change, what is the risk of not changing. Desire, so, you know, decision and desire to participate in the change. Knowledge of, do I actually know how to change?
So with AI, that could be, you know, the latest LLM, it could be whatever, you know, wrapper you’re using around the LLMs. I mean, there’s so much just knowledge that people need to get their arms around, literally the hands on keyboards, before you even get to ability in the ADKAR model. That’s the second A. And then the reinforcement of, like, are we actually changing the way that work happens?
And that’s much less in the linear cycle and much more of an everyday, real-time kind of journey for all of us. Just when we think we’ve learned something and have the knowledge to, you know, work within whatever model you’re using, all of a sudden another one came out, right? And you are having to re-engage in your knowledge journey, in your ability journey, and then something, you know, you name the day in the news, we sometimes have to go all the way back to desire. Do I even wanna do this, actually, because the outcomes that we’re getting from AI, I don’t trust them, or it’s hallucinating, or I’m very concerned about the bias that’s being presented here.
And I think that’s the most challenging thing that is very different about AI integration, and that takes a lot more than, like you said, executive enthusiasm, a lot more. I mean, sponsorship is really important. You know, that’s something that Prosci research tells us. You know, the number one driver for change success is sponsorship.
So it’s still important to have that leader understanding, you know, what the change is, and for leaders to have hands on keyboards, but that is not going to be, you know, the silver bullet for sure, because it’s a much more nuanced change, a perpetual change than we’ve ever experienced before.
Matt Poepsel:
Love that metaphor of a journey. And not only is it not a straight-line journey for a lot of employees, it’s not a journey they even asked to go on, and it was foisted upon them, and they’re reacting to it, and in some cases not well. And while enthusiasm certainly does characterize most of the executive attitude I hear about what’s possible now, that’s not a term I often hear from the floor for certain types of people.
And the fact is that people’s readiness or willingness, it varies based in large part on their disposition, on their behavioral preferences and traits and these things. A lot of times we fail to recognize that we hired people to do certain roles that are about regulatory or compliance or reducing risk or high levels of quality control. And then we say, here’s this totally unproven thing that’s moving way too fast. Isn’t this great? And they’re saying, no, this isn’t great at all. This is the opposite of great. And we’re wondering, why are they resisting? Why aren’t they bought in? You’re like, that’s not who they are. And we’re making it worse when we don’t get their needs met.
So managers find themselves having these conversations with people to say, look, we’ve gotta get adoption up. We gotta go faster, with a part of the workforce that’s saying, that’s not how I’m wired. They’re turning to HR, and our research has found four outta five managers are saying that they want frameworks and guidance and coaching tools that are tailored to that person. They’re not getting them, so that’s exacerbating it a little bit.
And even more at a foundational level, what we know is people need time. They need time with the tools. And then I talk to so many that are saying, my company doesn’t give me free time. I have to do all the work I did before. I’m supposed to figure this out nights and weekends. Yeah, that’s not gonna happen. So are we committed enough to this investment that we’re gonna give people time on the job to learn, come up to speed, to experiment?
What about the level of safety? Historically, and especially in this current job climate, people don’t feel very safe right now, so don’t be surprised when they don’t want to just rush right out and try new things. And then finally, when you think about skills and education, if we don’t have the ability to go outside to develop the skills, do we have enough internal L&D investment to be able to bring people toward those skills?
All of this really comes down to, we’re really managing a very large-scale behavior change. And as we all know, behavior change for humans is not altogether easy. Let’s not make it worse really by, you know, stumbling out of the blocks the way that we have. There’s still a chance to get it back together. Those are things that we have to recognize, I think.
Harry Osle:
I think, Matt, you said it, you and Laura said it perfectly. When, you know, leaders see the potential of AI, right? It’s out there. We see all the numbers. We see the effects of it solving a math problem that’s never been solved before. So you see the impact it could have. You know, leaders see the potential of AI, but employees experience the change.
And Laura, back to your point, I see some organizations kind of seeing this new, beautiful, shiny object and platform, an enabler called AI, and they lose sight of some of the things that work really well in driving change, and that’s called change management. They kind of lose sight of that.
Those basic things that we have to do to drive change within our organization still hold true today, even though this new technology is out there, especially as we move from, you know, AI’s innovation, you know, kind of phase through the hype phase, and then through the trough of disillusionment and ultimately through the slope of enlightenment, as you know, kind of that curve goes and more adoption happens.
Employees need to understand what’s changing, why it’s changing, what’s in it for me as an individual, and then, most importantly, how’s the organization investing in me to help me drive better performance? Not just, Matt, to your point, here’s a new shiny object, let’s, you know, use it and tell me how it goes.
It’s really about the investment back into the employees, and that’s really where managers come in. Managers are really that kind of missing link that helps drive that. I think AI adoption isn’t really about the tech. It isn’t a technology problem. It’s a behavior, leadership, and work design problem that can ultimately have a tremendous impact on business value if we drive the change management and investment in our workforce as they’re seeking today.
Gemma Versace:
I think if I can expand on something all of you have mentioned as well is about investment, not only time investment, but also financial investment. 19% of the organizations in our research reported having a standardized approach to AI value capture tied to finance or operating metrics. But, you know, concerningly, 23% reported to have no formal attribution at all.
So, you know, that’s two in 10 organizations that literally cannot say what changed because of their AI investment. And, you know, I believe the connection that matters most for this specific question specifically is that organizations that can prove AI ROI are also more confident in their ability to source AI talent going forward, because measurement doesn’t just track what’s being built. It also helps build the organizational conviction to keep investing. And it’s that conviction that helps to close the gap between the executive enthusiasm, as we say, executive enthusiasm versus how employees are actually experiencing the change as well.
So, you know, as executives, we can stay enthusiastic about AI pilots forever if no one’s forcing the specific number or outcome to surface, whereas employees, I think, are only feeling something is different once an organization can point to or pinpoint a specific attributable result and change and process around it that gets them excited, that they can, you know, kind of touch and see it and feel it.
And I think that’s the difference between, you know, a pilot staying a pilot and one that becomes a program, and really, it’s whether anyone can point to a number and say what has changed. And, you know, if I can go back to the Avalara team example again, you know, they described exactly this design versus kind of pressure problem.
So the CHRO, Ee Lyn Khoo, said telling people to kind of just use AI more produced completely inconsistent results across the team. You know, everyone was guessing differently at what looks good. So before anything else, they defined what success with AI looked like for each specific role, which I think is really important there.
On the flip side, their CTO, Danny Fields, also paired that with a structured no-blame root cause process. And that was, you know, for reviewing and fixing any AI failures instead of treating each of them as a one-off. And I really think that that pairing, being a defined standard plus that repeatable process for fixing what breaks, if it breaks, is really what design looks like instead of that pressure. So that’s my example to hone my point.
Mackenzie Putici:
Thank you for that, Gemma. No, and I think, oh, you’ve all given us so much to think about so far. Obviously there’s a lot on the table here. This is a really interesting and really valuable moment to take advantage of what’s changing. And so, you know, bring it back to the title of this session, Is HR the Real Chief AI Officer?
I want to ask your guys’ opinion. If HR wants to play a more consequential role in shaping how the organization uses AI, what does the function itself need to do differently? And ultimately, which parts of AI strategy realistically do belong inside HR, which maybe belong to other functions such as engineering or IT or legal, and which maybe need a dedicated chief AI officer? So a lot to ask, but Harry, I’m gonna throw this your way.
Harry Osle:
I submit that to you, that the question isn’t really about how many people AI will replace. It’s really about how capable each employee can become with AI. And to me, that all comes right back to HR’s responsibility, Mackenzie. It’s really about understanding the workforce’s AI skills and capabilities currently and what it needs in the future, both for HR and for all the functions across the organization.
I don’t see HR as the leader of the AI strategy. You know, the CIO chooses the technology. The chief AI officer will choose a strategy that needs to be deployed, but HR plays a critical role in how the workforce leverages and uses that AI. Are they capable of driving the maximum value out of that investment?
HR’s perspective has to be about the business value. The business value comes back down to the workforce. Is the workforce ready? Is the workforce capable? The future CHRO, or HR, isn’t necessarily about being the leader, and it isn’t just a steward of the workforce. They’re the architect of an AI-enabled workforce. That, to me, is the difference.
Gemma Versace:
Yeah. And if I can build on that, Harry, you know, my 2 cents here is that HR doesn’t need to become technical, I think, but it does need to have enough fluency in how capacity is actually getting built and staffed.
And so, you know, right now, again, to kind of reference the numbers that we’ve talked about throughout today, you know, the 47% confidence gap between HR and AI leaders, as well as a quarter of HR leaders surveyed in our report who don’t know how their own org is adding AI capacity, you know, that highlights a significant share of HR leaders who are currently not even in the room for critical conversations on how their organization is choosing to or wants to use AI.
And I think closing that gap isn’t about, as I mentioned, you know, HR learning code or anything. It’s about HR earning or demanding a seat in the staffing model decision for its organizations, you know, the same way that you referenced it earlier, Laura, the same way that it already has one for, you know, comp, benefits, and things like workforce planning and change management.
Matt Poepsel:
I also think it’s disingenuous for us to ask everyone in the building to reevaluate and reconsider their work if we’re not willing to do it ourselves. And in fact, a lot of the siloing that has taken place historically over centuries has really been a reflection of two things, in my opinion. One is around the complexity. So once a sales function gets so complex, we have to have a dedicated sales function to run that.
Another is fear. When I start to fear, I start to carve out my fiefdom, and this is mine and that’s yours, and you stay over there, and these types of things. AI has none of that. It has no fear, and it’s not overwhelmed by complexity.
And so you see many companies that are adding entire context layers where it can see all the information in the enterprise to break down those silos. If we go back and try to erect them in our human systems, it’s going exactly the wrong way. So I think we have to understand that the business, the technical, and the people systems in our organizations, they’re all tightly integrated.
So from an HR perspective, it’s, how do I represent the subject matter expertise of the people side and make sure that as we break this down, that that function or that capability is appropriately showing up in all the other places? The more we try to carve out and draw bold lines between what’s mine and what’s yours, it’s only going to break the entire system itself.
The last thing I’d say is that it’s always about relationships when you’re talking about people. And the C-suite only has relationships that extend so far down into the organization. But an HR team has relationships with everybody, ’cause everybody has questions about benefits and everybody needs to get paid. So everybody has a relationship with HR. You know, the nature of that relationship we could talk about.
But the fact is, being able to plug in and have a very switched-on HR team that is seeing themselves as people capability, and they’re plugged in from the entry-level individual contributor all the way up to the CEO and everything in between, that is a very powerful backbone of an organization if we choose to look at it that way. So I’m very excited about it, but I think that this function of who’s gonna own this AI thing, I think that is a very dangerous question to ask.
Laura McGann:
Yeah, I agree, Matt. I don’t think there’s a single owner, but I do think there’s an uncomfortable truth for HR, perhaps, or maybe for some of us in this, in that I do think HR needs to get more technical.
And I know we’re all going through our own journeys on this. Maybe you did not come into this profession to be the technical leader, and there’s some uncomfortable truth there, but I do think we have an opportunity to both be the architect, like we’ve been talking about, of this human-AI integrated workforce and business model.
We also need to be that visible model of what responsible AI integration looks like in our own technical discipline. And for a lot of us, that does mean moving much more into the tech space. And, you know, we might not have all the expertise, but can we stay at least current enough to push our own comfort level, getting our hands on keyboards, and maybe we’re not coding, you know, software, but we are coding and re-engineering our workflows?
And then based on that capacity getting freed up, there’s probably a huge opportunity there that all of us are probably curious about right now. Like, what do we do with that capacity? Is it more of that one-to-one coaching that is not technical? Is it actually creating more technically relevant, you know, employee experience programs?
I think there is a bright future for HR that probably, you know, we’re all maybe starting to, as we get some more capacity on our plates, to envision, wow, if I had more time, here are some really exciting things that I could create with the help of AI. So we’ve been on this journey, you know, to become more strategic over time, use more data, right, to drive better decision making. Now with AI, I think it’s just, the best is yet to come, perhaps.
And I know I’ve touched on it already today, but that real, like, change leadership fluency, I think we can go more and more into that uniquely behavioral human side of HR, that is our expertise, really, to help leaders own the AI-enabled change. They need to own this. This is a leader-led change, and I think we play a role there like we always have in enabling leaders to be a coach, for leaders to be that sounding board, to be that challenging perspective, and to bring some really interesting workforce data to the table around readiness and, you know, what it means to help folks in this age of perpetual change that is quite complex and quite overwhelming.
So I think we have a really critical role to play there, and we’ve gotta do it in partnership with our tech leaders, right, with our finance leaders, with folks who are concerned, and rightly so, on ROI of AI. I think we’ve probably got some different views on what ROI is and how we measure AI integration ROI. I think we need to stay in that conversation and have a very strong voice in that conversation to make sure that we have healthy organizations into the future.
Harry Osle:
I couldn’t agree with you more, Laura. I think that’s spot on. Absolutely spot on.
Mackenzie Putici:
I think you’ve highlighted that, you know, AI has permeated essentially every team, every part of the organization. So like it or not, we probably do need to be a little more technical, and some things that were maybe more soft skills or people focused now may have a technical element, which, you know, is just the nature of change that we’re looking at here. So whether it’s we’re offloading our tasks or augmenting our own human capacity, there is a requirement now.
So to kind of put this a little forward-thinking, to get your best suggestions for what’s to come in the next year, if we’re to look ahead, let’s say 12 months, what would you all say is the biggest change you think AI will have on HR and talent strategy? And what suggestions or what maybe pitfalls to avoid would you all give our audience here tuning in? And Matt, can I throw you in the hot seat?
Matt Poepsel:
Yeah. 12 months used to sound like a long time. It doesn’t sound that long anymore. Things are being compressed so quickly and things are advancing so fast. I would imagine that within 12 months we’re gonna look at digital coworkers very differently. The fact is now they seem to be personal agents at best.
People are kind of graduating from buckets of skills and plugins to actually naming coworkers and treating them one-on-one as people that help me do my job. You know, they’re not people, but they’re, you know, equivalent coworkers, digital coworkers, to help me do my job. That’s gonna evolve into a team context.
You’re going to see this hybrid example. I think HR needs to hold this north star of a seamless, frictionless cooperation between human and digital coworkers in a way that is high degrees of productivity, but also high levels of employee experience. And it’s gonna be weird, and it’s gonna be awkward, and it’s gonna take a big adjustment, but it’s absolutely gonna happen. And I think that there will be a time when we look back and say, wasn’t that weird?
But I think we can get there, but we have to make sure we listen to our HR leadership to say, how do we evolve to this increasingly digital technical reality where it mixes these coworkers, et cetera, in a way that is more human and not less, that it doesn’t cost us our humanity? It actually gives it back to us.
And Laura was hitting on something that I just absolutely hope, with all my heart, comes true. We have more time to spend time on relationships and hold space with one another and actually develop our employees, ’cause we have time to do that in a way we didn’t before. I think there is reason to be optimistic, but we’re not going to get there if we take an overly technical view of what’s about to happen.
Harry Osle:
Matt, I think you’re absolutely right. I think we’re going to see a shift from AI experimentation to workforce redesign, which means really AI will change jobs, it’ll change skills, organizational structures, and career paths. It’s doing that already, but at very small levels and very inconsistent levels within organizations. I think you’ll see that kind of, that slope increase dramatically over the next 12 months.
I think the winners won’t necessarily be the ones who deploy the most AI. I think the winners are gonna be the ones who figure out how to better upskill their workforce so humans and AI can work together differently. As I mentioned before, I see AI as indirect labor. That’s the point. You know, you have agents who are now being named and, you know, kind of as coworkers. Yes, that’s gonna exist. And this might be a little bit weird, but yes.
If you see AI as indirect labor and your talent does that direct labor, and you increase that capacity through the use of AI, and you can then leverage that and have the most impact by focusing on the skill set and capabilities that those individuals need and invest in them, you’re gonna see that, you know, kind of kick off very, very, very quickly.
The next wave of AI isn’t about giving people, you know, the better tools, because it’s not a tool, it’s an enabler, it’s a platform. It’s about fundamentally changing how the work gets designed, redesigned, and performed. And I think those are the organizations that are gonna have the most impact in the next 12 months.
Laura McGann:
Harry, I love that. I was gonna build off of that. It’s that fundamental redesign, and something that I’ve found exciting, but maybe unexpected, is the conversations, how much conversation is actually needed, and with who.
So on any given day, you know, I’m used to maybe just supporting leaders in a specific function. The amount of conversations that now need to happen between HR, IT and engineering, legal and risk, business leaders, right, who are owning the use cases, if you’ve got a chief AI officer or somebody kind of focused on leading that work.
So many folks have different perspectives and implications on opportunity, you know, the opportunity side as well as kind of the risk and what we need to be thinking about from a data governance or an IP and compliance, without having those unsiloed conversations, I’ll just call them, because we’re working in a lot of silos still. I think AI opens up this very different operating model where you have gotta break the silos. You have gotta have these cross-functional, robust, and sometimes tension-filled conversations, because people are having such insight into how we actually leverage AI to enable our business strategy.
That, again, maybe I’m just too excited about this. I get that there’s real concern and not everyone feels the same way, but when you start to put some of these pieces together and realize what’s possible, that’s where I think that the big T, right, in AI transformation can get realized, is when you bring a lot of people to the table in the same room, having the conversation.
Gemma Versace:
Nobody on this panel made the case for HR owning AI strategy outright. Matt went further and called the question of who owns it a dangerous one. The more useful question is whether HR is in the room early enough to shape how the work changes. Right now, too often, it isn’t.
The panel kept returning to the work itself. Harry wants organizations to stop looking at jobs and start looking at the tasks inside them. Matt argues every role deserves to be reconsidered, even the ones that seem safe. Laura frames adoption as a loop rather than a project, one where people can slide all the way back to “do I even want this” after a bad output or a rough news cycle. And Matt named something that rarely shows up in an AI rollout plan: hope. If a manager can’t show someone what their future looks like, people dig in their heels.
Looking ahead, Harry sees the shift from experimentation to full workforce redesign speeding up dramatically. He expects the winners to be the organizations that upskill best. Laura sees the biggest opportunity in the cross-functional conversations AI now demands between HR, IT, legal, and the business. Matt’s north star is a workplace where human and digital coworkers operate together in a way that gives people more time for each other. My own addition: the people who learn to get value from AI will be in high demand, so retention belongs in the strategy now.
The organizations that get this right will treat AI as a people change first, and build the partnership between HR and technology from day one.
Join us next time for more conversations with technology leaders who inspire us to grow, lead, and innovate. You can find us on Apple Podcasts, Spotify, or YouTube. If you enjoyed this episode, please share it with your network.
We’ll see you next time.