Decoding the C-Suite: Part 2 - The People

0:17Hi there and welcome to Decoding the C-Suite, a five-part miniseries from Recruiting Future about how leadership really thinks about AI. The organisations genuinely driving AI change in talent all have one thing in common, which is alignment with the C-suite. Winning that alignment means understanding how each person around the leadership table actually thinks. And that's exactly what this series is for. I'm talking to five senior leaders at Smart Recruiters, seven months on from their acquisition by SAP, each seeing AI through the lens of their own function. We're starting with the lens closest to home for most of the audience, the people.

1:00The headlines about AI and the workforce make grim reading at the moment, dominated by surveillance, layoffs and disappearing roles. Lahua's Stonebreaker leads the people function at Smart Recruiters, and her approach starts from a very different place, naming the fear rather than managing around it. We talk about how to build psychological safety when your people are even scared to admit that they use AI. Why she believes the org chart as we know it is gone and her vision of an employee life cycle that finally connects everything an organisation learns about its people. This is Decoding the C-Suite, episode two, The People.

1:42So let's talk about AI and people. So the headlines at the moment, when we kind of read about AI, it's companies using it for surveillance, measuring keystrokes, laying people off, roles disappearing. There's a lot of scary stuff out there. What's the emotional reality of that like within organizations and how does that shape the way you think about the workforce? It is true. I mean, I wake up every day listening to the news and I hear sort of doom and gloom all over the world. And then when you apply that to the place where you should feel safe or you should feel trusted to be an expert in what you're doing.

2:19And so one other added measure of a meta headline to track keystrokes or Oracle doing three three hundred or thirty percent or whatever the three number is in terms of layoffs. I mean, those are scary things. And so I can't generally speak to how all organizations are thinking about it. But I think from a practicality standpoint, if you're wanting to sort of have a surveillance and a monitoring system, are you just doing it for compliance? And what's the goal? What's the purpose? And so for the organization and what I believe that we're doing that's a little bit differently is really taking this moment to say we have to stay curious.

2:55You have to sort of adopt an even faster learning mindset. We just, it's almost like the playbook hasn't even been fully written and you don't even know what page you're going to turn to. So if you don't stay on top of it, all of a sudden you become even farther behind. And so being able to allow for people to almost experiment with safeguards, helping them understand that the work is not meant to necessarily be to replace what they're doing. But how do you use AI to not only take away sort of that low value add, they talk about administrative responsibilities or creating more efficiency. But actually, how do you use AI to be more sort of people-centric?

3:31Because now you're focusing on those sort of scare skills, judgment, complexity, conversations with people, helping them understand sort of the moment. And I think helping people kind of come along on the journey is probably the most important aspect that we can take rather than having something happening to them. How do you give them that psychological called safety to do that? Because there's some really interesting sort of data out there about people using AI at home in their kind of personal life and then not using it at work because they're afraid to or their company won't let them. I mean, how do you make people feel safe?

4:10So, Smart Recruiters, a couple of years ago, we actually went and we did a lot of discovery around sort of our first organizational introduction into AI. And we, the IT team and the InfoSec team, they really kind of surfaced something that they thought would be beneficial to all roles. So role agnostic across the organization. And that tool is Glean. It actually sits on top of ChatGPT. But what it does is it allows for you to aggregate data sources from all over the organization that every unique person has access to. So in my role in HR, obviously, lots of sensitive confidential access and information.

4:43I don't want that to be kind of pulled into a search or used in a prompt. And so we did a little bit of an organizational-wide experiment by creating a corporate goal around it. What would happen if we created a bit more efficiency? So we did, we used that sort of as a starting point. And what we started to see is all of these interesting use cases and ambassadors that kind of surfaced across the business that said, well, let's try and apply it in a different way. And how do we then collaborate a little bit faster? How do we get to the information or how do we kind of solve complex problems in a different way?

5:15And that itself, I think, started from top down. And that, I think, started the infrastructure of psychological safety and allowing for people to then want to carry that forward. And so now, you know, speaking from sort of the people or the HR team that I lead, we're using that same sort of concept to say, if you could rewrite your job, what would be the work that you find either uninteresting or cumbersome or frustrating because there's bottlenecks or you're waiting on something else. And we're starting to kind of crowdsource and do some experiments of our own. And when I think about sort of the blockers of AI within sort of the HR world that I live in, a lot of what we do is we have to react to the business.

5:58You can have foresight, if you will, in terms of thinking about workforce planning or flight risks and all of the rest of it. But But the reality of the day comes, the business changes. All of a sudden we have to respond in a moment and then we're running out of time. And so being able to kind of carve out blocks to be able to do some of that more creative thinking. And so right now I've got a leader on my team that's actually going to reserve a full eight hour day over the next few weeks. We've kind of come up with a couple of hypotheses of things that she could do. So for instance, we're looking at moving into SAP through our acquisition.

6:36and they have a very different way that they look at sort of compensation, job architecture, title levels, what have you. So rather than having to go and look up all of these independent pieces of information, as we think about bridging from one world to the next, what if we could create agents that could go and do that for us? So again, there's consistency, there's scale, there's reliability. And what would that then do? What capacity does that give us back to do something different and unique around the business? So we're testing it. We're going to see the effectiveness. We're going to see if we've got the right tools.

7:07And then we're going to actually go and start not only crowdsourcing around HR, but around the business and see how we can apply some impact elsewhere. And you said that comes down from a leadership level. What does that look like practically? Are you assessing the leaders in how they're using AI and how they're thinking about things? So not so much in terms of looking at goals and having specific outcomes. I think, you know, if I go back to sort of the traditional ways of measuring SaaS and you're looking at consumption or widget-based, what have you. We're not necessarily looking at it at that scale.

7:39But actually a few weeks ago, the executive leadership team got together for a two-day off-site. And a lot of what we do is we talk about what are the biggest problems, how do we kind of collaborate in solution, and what's our strategy going forward. But we actually spent 50 % of our time, so one full day where we just did AI design like a masterclass. We actually applied a lot of these principles to ourselves. So, again, talking about top down, we can't actually expect any sort of excitement or desire to change and to lean into the moment if we're not sort of practicing that and embodying those qualities.

8:14And so, right now, we're taking a lot of the things that we did at that session, and we're going to carry it forward in terms of some initiatives that we're going to see across the entire business. So, we're really excited about where we're at. And so you're going through this integration at the moment, and I'm presuming a lot of the work is around how teams are structured and designed and those kind of things. We're at a point where AI is actually changing the way the work gets done. So how do you factor in? What does that do to the way you think about organizational design? No, it's such a good question.

8:47And it's hard because I go from, we kind of were the ones that were the decision makers. And now we're in a bigger organization where we have influence and we have credibility. But at the end of the day, we're part of a larger organization that may have a very specific way of doing things that we're trying to learn. And so in my personal point of view, I think the org chart is actually gone. I think when you think about org design, you really need to think about what are the outcomes? What's the work that you need to actually produce? And then you're not necessarily confining people to a specific job title or here's your day-to-day responsibilities because those could ebb and flow and they could change.

9:23And so we just haven't necessarily gotten to that inflection point. But I am excited about SAP and really having access to sort of this autonomous, large ecosystem where data can come in from everywhere and really be able to kind of help you inform and accelerate a lot of those decisions. And if organizations are designed like that, what's the impact on people, particularly people who have sort of grown up in a structured environment where org charts are the norm, they know their responsibilities, or has work just changed anyway and is that an outdated way of describing it? I think the work has changed.

9:57I think it's going to be a continuum on where people are at in that journey. And particularly if you think about the industry sector that they're coming from. Right. But in particular, I do think there's an opportunity for you to invite people into that conversation. So, again, these are not necessarily like decisions that get made. They get pushed down and all of a sudden people are just saying my job is different. Like, again, inviting them in to say, if you could design something different, if you could remove something that you are basically blocked on today, what would that look like? And I think that that's where we're starting to have more of that conversation across the business.

10:31I want to talk a little bit more about skills because you mentioned earlier in the conversation about the type of skills that are sort of bubbling to the surface that people need in this AI world. Tell us a little bit more about that. Yeah. I mean, soft skills are always going to be important. And I mentioned a few of them in terms of being agile, having sort of that learning mindset, being open minded to things that you just don't even understand. And the desire and curiosity to go out and kind of seek to learn more and to practice. I think those soft skills will always be there. But the other one's just around AI.

11:03We talk about not wanting to create bias. And it's not meant to be the decision maker. It's meant to influence or to help you succinctly get to, like, what is a root cause or what are some potential solutions? and then let's have a conversation in terms of how we're going to execute against it. So, softer skills around, again, the complexity of work, the decisions that need to happen, the judgment piece, the human elements, I think, which is why I don't envision AI completely replacing our jobs, right? Because we need all of those across the world. So, to talk a little bit more about the integration, obviously, that's going to come with a set of challenges when you're integrating processes and people and cultures.

11:41What has that bubbled up in terms of the challenges you had to deal with or you're dealing with? So we talk about AI as if it's this thing, but at the end of the day, it's not owned by one sort of person or team, right? It is the culture, the culture of the organization. And so I think that there is intentionality around how you talk about it. So going back to your earlier comment around waking up every day and sort of hearing all these things about job loss or tracking, name the fear. Like we know that these are things that you probably have on your mind and we may not have all the answers. So name the fear, name what the purpose is, what outcomes are you trying to drive for?

12:18I mean, there may be organizations that want to drive efficiency. And then what do you do with all that extra work with fewer people? So those are some conversations that you have to have. But I think naming the fear, being transparent, speaking frequently. So over-communication is really important. For us, we actually just concluded our, we do an engagement survey two times a year. We announced our acquisition September 11th. We'll always remember that day. We then did our first engagement survey in October. Shockingly in headwinds of change when something like that happens, our engagement scores went up.

12:52And then in April, we just saw another increase in our engagement. And so globally, you start to see engagement go down. And for us, I think it has been because of those sort of intentional choices to allow for psychological safety for people to say, I don't like this or I feel uncomfortable with this decision or I'm scared. And how do you help me work through those things? And again, it's not to say that you can appease or come up with an answer that's going to help everybody, but being able to name it and sort of walk them through that moment together, I think is really important. And what does smart recruiters bring to SAP culturally?

13:28What's different? What's the ad there? So it is interesting because one of our, so actually our stated corporate goal is that we're going to be the innovative growth engine for success factors within SAP. And what that means for us, you know, when you think about startup organizations and sort of the very generic descriptors of, you know, bullish and disruptive and fast paced and what have you. I think all those things can still be true in a very large organization. So it's a new way of working. It's a new way of bringing focus. So one of the things that we've actually seen implemented within SuccessFactors is an OKR framework where you have very, very small level of corporate goals.

14:07Everybody knows what those are. Those then drive sort of your prioritization or your decision making. If they're not linked to that, then that decision makes it very clear that it's either no or not yet. Right. And so we've introduced that. And I think that has sort of propelled into a culture shift and a culture change. We do see a lot of shared sort of DNA across our values, which is great. It's a great sort of foundation place to start from. But I think being able to bring sort of that energy, that speed, I will say in larger organizations, you tend to see a lot lower risk appetite. And I'm not saying that we are just throwing risk out the window in all caution, but I think that there is a level of appetite for us to say, let's just try it.

14:50We can pull back quickly, but let's just not be afraid to just kind of hold ourselves tethered to old rules or old ways of working. Obviously, this is leading to a situation where you're able to offer full visibility on the employee lifecycle from when people are hired to when people leave with all the data across that and AI powering it all the way through. What does that mean for people professionals? What does that mean for talent strategy? What does it enable that isn't possible now? Yeah, this is something I'm the most excited about. I'm not necessarily the owner of this idea. It's nothing that's kind of come to fruition, but I do think that the possibility is the greatest with smart recruiters joining SAP.

15:31And it's this idea of almost having this employee blueprint. So you think about I submit an application. So here's some information about me and my experience and what I'm interested in. And you go through this journey of being interviewed and then you get hired. You get hired for a very specific job. We talked about maybe the org chart or the job itself may change. And so obviously take with a grain of salt in terms of what that evolves to. But you get hired to do something. right? Perform some outcomes. And then from there, your first sort of 60, 90 days, you're learning, you're trying to absorb as much information, understand the company's vernacular.

16:06But at the same time, how do we build sort of an onboarding plan with you in mind? So it's not necessarily like a rinse and repeat. There may be a playbook to certain elements, but there's very specific skills or experiences that that person's bringing in. So how do we use AI to surface? Maybe it's, I think it's really important for you to start interacting or building a relationship with these people or these pockets of teams. Maybe it's these systems that we think that are really important for you to get your work done. So from there, now you're ramped up. All of a sudden you're performing. You're getting those continuous conversations with your leader on how things are going.

16:40Maybe there's a continuous listening moment where we can actually take sort of annual or quarterly or whatever that performance cycle may be. AI can then leverage. You guys talked about this being a very tactical goal. How did you produce? Here's something more strategic or a learning and development plan that we have. And then, again, surfacing and tracking how that person performs against it. Mobility. So the possibilities, I think, are endless if we're able to kind of use sort of this open concept around data and how do we then leverage that to make the employee experience not only incredibly personal, but you maximize sort of the output of their experience as well.

17:18I just think that's so powerful because it's always a ridiculous situation that someone goes for a recruitment process and you find out all this information about them that then just kind of gets lost within their role description. And we don't really know what they do. So I think it's just it's just such a powerful, such a powerful thing for organizations, isn't it? It's kind of like you think about the employee lifecycle as sort of these like buckets, right? So you've got recruiting and you've got onboarding and you've got benefits and pay. And it's almost like the journey for them just stops at that moment rather than having sort of that continuous.

17:51It's not even a cycle. It's just it's it's all all connected all the time. And I think that there's that's where the power really lies. What are the implications of all of this, though? Because this is going to affect the way people think about talent, the way people hire, the way people develop people. What does that look like in terms of rethinking the way that the talent is thought about? This is where I think this is where people's fear comes in because we don't know. But how exciting of a moment to be able to kind of be the architect of that future. And so I think I don't have the answer.

18:26I don't know what that's going to look like. But I think that the possibility, as I described, in terms of just thinking about new ways of work or sort of breaking down the traditional constructs of what we think we've always been sort of expected to do, what sort of possibility is that for us? And the curiosity piece is what really has me the most excited. Curiosity is really interesting because you can develop curiosity, can't you, as an attribute, as a skill? Absolutely. And I think it's something that you actually have to have to see, not only the curiosity. So we're talking about just screening for talent or making sure that they're the right sort of fit for your organization, building in sort of that as sort of a litmus test.

19:07Right. And curiosity, the fluency, the desire to go in and take on some new skills and to apply something in a new way. It's just so kind of hard as well, because there is so much uncertainty and things change very quickly. And that must have an impact on the way that people think and behave as well. It does. And I mean, we have definitely seen, I would say, both an openness and then also a resistance. But I think those are things that you expect to have happen. They kind of want to see, well, what happens if? And those are some of the conversations I've actually had with the SAP colleagues that I'm working with.

19:41They're kind of, what happens if you do this? And then maybe if it's successful, then we'll follow. Why don't we just go and be that sort of take that first step? Yeah, it makes a lot of sense. And as a final question to sort of really pull this together, what does good leadership look like in an AI driven world? Good leadership. I think we talked a little bit about this earlier in terms of a lot of those sort of like the fundamentals that are really important. So psychological safety, naming the fear, helping somebody understand how they can be a part of the solution and addressing the fear. Good leaders have to lead by example.

20:21So it's not the do as I say, not as I do. I think the leaders also have to understand exactly what they're striving for. So I think starting with the outcomes in mind versus this is the expectation and also just challenging. So if there is, you know, this sort of desire to look at AI as only efficiency or only for compliance and surveillance challenge. Why? And what is the reason for that? Lahuah, thank you very much for talking to me. Nice to see you. Thanks, Matt. That was Lahuah Stonebreaker on fear, safety, and the end of the org chart. Next time, we follow the money. So if you've ever struggled to get AI investments signed off, the next episode is for you.

21:05Tom Didesidero is Smart Recruiter's CFO, and he explains exactly what he wants to see in a business case, why the old rules of measuring ROI no longer apply, and shares some numbers about the real economics of AI that genuinely surprised me. Subscribe or follow wherever you listen to your podcasts, and I'll see you in episode three.

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