Episode 263: The Hidden Cost of Fragmented HR and Finance Data (with Kenneth Matos)
Have we made the manager’s role more complex without making it easier to make good decisions?
Over the past decade, expectations on managers have grown significantly. They’re expected to make decisions that are fair, data-informed, and financially responsible - often in real time and under increasing scrutiny. Yet in many organisations, the systems designed to support those decisions haven’t evolved at the same pace.
So, in this episode of the Digital HR Leaders podcast, host David Green speaks with Kenneth Matos, Director of Market Insights at HiBob, to explore what it takes to design better decision environments for modern organisations.
Drawing on new global research involving 4,700 people managers, Ken shares why the time spent stitching together data and the lack of a unified HR–Finance view are undermining decision quality - and what leaders must do to enable managers to balance people fairness with financial discipline.
Tune in to learn more about:
Why decision friction is emerging as a hidden barrier to organisational agility
How fragmented data undermines fairness, consistency, and trust in people decisions
What changes when HR and Finance operate from a shared context
Why defensibility is becoming critical in an era of pay transparency and scrutiny
How AI can reduce decision friction when implemented with the right guardrails
Why designing better decision environments is becoming a core leadership priority
This episode is sponsored by Hibob.
HiBob brings HR, Payroll, and Finance together into a single platform that employees actually use. With AI throughout, you move faster, work smarter, and empower your people to power your business.
Sapient Insights recognizes HiBob’s AI vision, citing the Bob AI Companion for making everyday work faster and easier. Fosway Group also names HiBob a 2025 9-Grid™ Core Leader, recognizing the strongest AI vision among Core Leaders.
HiBob. All-in-one HCM for HR, Payroll, and Finance.
Learn all about HiBob’s modern HR platform here
Resources:
Better Together: Budget-Smart People-Fair How Managers Decide with Data Report
This episode of the Digital HR Leaders podcast is brought to you by HiBob.
[0:00:00] David Green: If you're an HR leader, you'll know that the expectations on managers and people leaders have changed dramatically over the years. They're being asked to make decisions that are fair, data-informed, and financially sound, often in real time and under increasing scrutiny. But have we really redesigned the systems around them to make those decisions easier or even possible? I was surprised to learn, in new search from HiBob, that in a global study of 4,700 people managers, 60% said that they spend three or more hours pulling data together from different systems before they can even make a people decision. Only 2% report having a unified view across HR and finance, despite the fact that their decisions are being challenged more often and more frequently. It raises some important questions around decision quality, right?
To explore this in more detail, I'm delighted to welcome the lead of this research, Kenneth Matos, Director of Market Insights at HiBob, to the show. In addition to examining the findings of the Better Together research, Ken and I will be discussing what managers are really dealing with on the ground, why HR finance alignment remains so difficult to achieve, and what organisations need to start doing now to design better decision-making environments for the future. There's a lot to unpack. So, with that, let's get the conversation started.
Ken, welcome to the Digital HR Leaders podcast. Before we get deep into our conversation, can you walk us through the journey that brought you to HiBob and maybe explain what your role as Director of the Insights Lab actually means and how it helps HiBob's customers?
[0:01:57] Kenneth Matos: Absolutely. Thanks for having me, David. So, my journey is a little bit of a bouncy-round one. So, I originally thought I would be a counselling psychologist, and then fell in love with organisational psychology. So, I'm trained as an org psych. I focus on, how do we bring people together in order to actually accomplish goals? And that, for me, is whether it's their individual goals or their collective goals, the really key thing that I'm focused on. And then I applied that in the US Department of Defense, focused on research on harassment and discrimination in the armed forces, employer support for the reserves, and whether or not people enjoy the travel systems. So, it was a real, very mundane and very juicy kind of research places. I spent a couple of years focused on remote and flexible work before COVID, really defining, how do you create work processes that span space and time in those ways, that we find a Zoom or recording or a bunch of emails, creates change from just being in front of people?
[0:03:04] David Green: That proved quite timely, then, doing that.
[0:03:07] Kenneth Matos: Yes. Yeah, a lot of the things I've found myself studying have become major public issues very shortly thereafter. I wish I could say it was because of me, but. And then, I spent about six years focused on leading a consulting division within Culture Amp, really helping HR people have better surveys that get them better information to actually make better decisions. Now, I'm at HiBob, which is a bigger research goal. I lead our insights lab. And so, we focus on how can we help customers and the HR community at large have a better understanding of what's going on in the world across all of HR, not just the engagement piece, but performance, growth, the financial aspects of HR. Basically, if you all sort of flowing into how you plan your people, we kind of want to have a finger in it.
[0:03:59] David Green: Okay, very good. And I think that leads on quite nicely to what we're going to be talking about today. So, Ken, obviously, you spend a lot of time talking to HR leaders in your various roles, whether it's back as a practitioner at the Department of Defense, and obviously during your time at Culture Amp, and now at HiBob. When you talk to them, what's one of the top frustrations that you hear?
[0:04:21] Kenneth Matos: So, I think the thing that's really popping right now is trying to create certainty at speed and scale. So, there's so many things going on right now that HR is being asked to resolve. So, like, yes, IT is putting in AI products, but HR is supposed to deal with the adoption of it. And how do we get people online and comfortable with that? And I think that is really difficult for people who are told, "Show me ROI metrics, show me data that proves this is correct". "Well, it takes a year to get that data and we've never asked for it before, so how do I give you that certainty without the investment in tools or processes that allow us to get that data?" And then, there's sort of this chicken-and-egg circle where it's just like, "Get me data to prove the investment for the thing that gets the data". And so, I think a lot of HR people are just really frustrated right now at trying to prove things that have always been intuitive without actually making the transition to an evidence-based approach.
[0:05:29] David Green: And I think, I don't know if you share this, over the last decade or so, we've seen HR move from its traditional role as a support function, very much focused on compliance and things like that, to being much more of a strategic partner to the CEO and the leadership team. I think COVID probably sped that up a little bit, particularly as it related to engagement experience and the impact on everyone working from home for a while and then coming back into the office, which obviously the work that you did probably helped to inform a little bit. And now, as you said, if you're going to be a strategic function, you have to be able to prove the value of what you do. And then, with AI, obviously the whole big AI transformation at the moment, which we're only in the early stages of at the moment, is obviously going to continue to speed up over the coming years, I suspect, this means that this is really front of mind, I guess, for CHROs, but also for their counterparts, I guess, in the leadership team, particularly maybe the CFO as well.
[0:06:33] Kenneth Matos: Yeah. I think one of the things we really struggle with is, so much of organisational life is defined by who controls what. And this data belongs to these people and they have these conversations to pursue these goals. But actually, it's all the shared stuff. Like, we all use the same money, we're all trying to advance the same organisation. And so, I think a lot of people, HR and others, are really struggling as AI comes in and says, "All these walls are actually artificial. They're built so that we can process what's in front of us". And you don't need to have them in place if the AI system is working, because it stitches the stuff together and compresses it to the insight that you're looking for, as opposed to like, here's 15 spreadsheets, you memorise all of them and make sense of them, which is, I think the other challenge that HR really struggles with. It's not just, "Can I get you proof?" I can get you proof. It's just not in your decision window. And I think that's really where the pain point is now as HR has become more strategic. They just can't keep up with the speed of what's happening without those tool transformations.
AI adds a whole other level to it, both making it easier to go faster, but also making the demand to go faster happen faster than people have learned the skills to use that tool effectively. And so, I think that pain point is kind of universal across the organisation. And HR feels it, I think, a little bit more intensely, because they're also held responsible for getting everybody else to be in that place.
[0:08:14] David Green: And I think we all know that people data on its own can be quite insightful. It can help you understand certain key things within the business. But its real power comes when you combine it with other data, other business, say finance data, ops data, sales data, customer data, etc. And you've just led a study, also research at HiBob, Ken, haven't you, to look at this. I think it's called Better Together, which I definitely recommend for listeners to dig into. Do you want to share a little bit about the study before we dig into some of the findings?
[0:09:52] Kenneth Matos: Absolutely. So, we wanted to understand what was going on for managers when they are trying to make decisions that pull in both people and financial considerations. So, that's things ranging from salaries and bonuses to performance ratings that affect salaries and bonuses, to who gets access to paid learning and development experiences, wherever you're trying to make a decision about fairness for the people, but also, "There's only so much money to go around. How do I manage that?" And so, we surveyed 4,700 people managers around the globe, so Europe, Australia, New Zealand, North America, and other places, and really tried to understand what is going on for these managers. And so, we found a couple of really interesting things about the pain points of trying to connect data across these different areas.
The thing that struck me first was, I think, three-plus hours on average for people to be stitching data together, just finding it somewhere in some system someplace, transferring it to an Excel spreadsheet, getting it from another place, putting it in that same Excel spreadsheet, to be able to even understand what's happening so that you can make the decision. So, it's not three hours to make the decision, it's three hours just to get informed enough to go about making the decision. And that's just the beginning of this painful experience. Because on the back end, we also found that about 50% of these managers said that half of their relevant decisions, things that were unifying people in finance, were formally challenged or questioned in the last year. And so, they spend all this time putting the data together to have a good answer. And then everybody's like, "I don't really trust that. Prove it to me". And so, you're losing time on the front end, you're losing time on the back end, you're going back and having to redo decisions that were supposed to be fair to begin with. So, if you're doing them over again, is the new decision not fair?
So, for example, if you've done your performance review and said, "Okay, this is what people have earned in their ratings. Oh, now you give me the compensation threshold. So, now this can't stand. I have to rejig what I'm rating people as so that it fits into the budget". But now they're all going, "Wait a second, is this fair? You already judged me". And so, these are the kinds of pain points that managers experience on the day-to-day when the two processes are kept separate and not brought together.
[0:11:43] David Green: And of course, one question I'll dig a little bit deeper in that, but we all know the frustration of having to bring multiple data sets together into one, and then trying to get to the analysis and the insight and the recommendation from it. And obviously, if that's taking an average of, for 60% of the people that participated in your study, at least three hours or more each time, then that's quite staggering, really. And then if they're doing that work, and then it's being questioned, it suggests a lack of trust in the data, perhaps. Again, you may have looked at this in the study, but I'm sure you've looked at it elsewhere otherwise, is this a technical capability thing or a human capability thing from an HR, a professional perspective, or perhaps a bit of both?
[0:12:32] Kenneth Matos: I would generally assume that things are a mix of both, but I think there's a couple of key areas that we could focus on. First, I think it is, technology is built for the buyer. So, if I'm building an HR system, it's for the HR person. If I'm building a finance system, it's for the finance person. It's going to be the person who signs the cheque or makes the decision. It's not necessarily built for all of the people throughout the organisation. And I think having that people-first approach is a very different design strategy. And so, everything's optimised for the thing that you bought it for, and then somebody has to optimise it by hand for the actual thing that you're doing at that level. And so, I think a big chunk of it is just we haven't set them up with tools that make it easy to juggle multiple priorities for the business. I think that's where we get a lot of the conflict in organisations, where everybody's really trying to do a good job, but their jobs are defined as in being in opposition to one another, as opposed to some sort of collaborative, final experience that's not optimising for either one's perfect outcomes. So, I think we have that space.
But then, we also have people who are like, "Okay, are you training me because you're going to get a system that's going to link it? Are you training me how to do the stitching? Do I even know what politics I now need to play to get these people who don't trust the system?" One of the things I was also really saddened by was about 62% of people said that if they couldn't get to the data, they would just make an educated guess, rather than miss the deadline. And I think the focus on speed, to the detriment of accuracy, both spawns more things to get you through each step, but then the steps don't connect. And so, you end up having to do all the work by hand anyway. And so, I think that's what we're really experiencing, is we're solving fragments of a big problem, rather than solving the problem.
[0:14:34] David Green: Today's HR teams juggle all their processes across disconnected systems. HiBob brings HR, payroll and finance into a single platform employees actually use. Bob already has AI throughout, so you can move faster, work smarter, and empower your people to power your business. That's why Sapient Insights recognises HiBob for its AI vision, highlighting Bob AI companion for helping teams move faster, from drafting job descriptions and communications to getting answers fast. And Fosway Group names HiBob a 2025 9-Grid Core Leader, recognising the highest AI vision among core leaders. HiBob, all-in-one HCM for HR, payroll and finance. Learn more at hibob.com.
One of the other findings in the Better Together report that stood out was that only 2% of people managers that participated in the study have a unified HR finance view. And I think that's what we're really talking about here. How do we get a unified view across HR and finance, despite them being challenged more frequently on their people decisions? Because obviously, if you've got that single source of truth, how it's almost always phrased, why do you think that alignment remains so difficult in practice? I think you alluded to it a little bit there, HR have got an HR system they're using, finance have got a finance system they're using, ops have got a system they're using, sales, etc, so suddenly you've got to bring it together to get that one view. What are some of the things that you're seeing maybe in the study, but also from customers; and how, of those organisations that do have that unified view, how are they solving that problem?
[0:16:35] Kenneth Matos: So, I think the technology pieces is that chicken-and-egg problem of like, do we invest to get the answer, or do we wait until we have an answer that we can't get until we invest? I think that's one big piece. I think the other piece is the optimisation of everybody's jobs for the specific thing that they do. There's a real need to have a bigger conversation about like, why does HR do what HR does? Why does finance do what it does? And how do those intertwine so that the final decision has considered all those different pieces? And I think also, one of the downsides of our really technological society is it's just so much more that you could pull in. If you are a manager making a decision, there's 15 systems that somebody could be like, "Did you look at the HR? Did you look at finance? Did you look at the L&D? Did you look at this, did you look at this?" I don't have time. I also have an IC job likely on top of this, and I have a team that's probably gotten bigger as various researchers are saying manager spans of control are getting larger.
So, we're creating a scenario where we're forcing people to make satisfying real-time decisions, even if they're wrong, "Well, I'll fix it. If I'm going to get challenged either way, why waste the time?" And so, I think we're kind of caught in a loop with that dynamic, where we're consistently saying, "Well, it's not going to be different. So, doing the things that would make it different is not actually a good strategic choice". And so, I think that kind of thinking makes it really hard to pull a finance person, an HR person into the room and have them be like, "We only have so much money to do these things. But if we don't do them, here's the downstream effects that's going to mean you don't have enough money for anything". And I think that sort of moving back and forth between right now and tomorrow is also another big challenge that organisations face. And so, I think there's a real value in being able to create shared models that pull in the HR content, the finance content, and say, "If we do this, odds are this will happen. If we don't do it, odds are this will happen". What's the net effect? I think that is the big transformation that we need and that potentially, AI can give us by making it easier to get to the net effect of the decision, not each individual fragment of it, and then overwhelm us with trying to put it all together in our heads.
[0:19:16] David Green: So, again, two things really around those companies where it is working. What difference do you see in organisations where HR and finance are operating for the same context? That's one thing. So, I guess technology is part of that, but there's also that partnership. Have you seen where HR and finance are fairly locked in, they understand where each other's coming from, which I think is very important as well, and each understands how they can support the other and together they can be even better than they can be on their own, if you see what I mean? So, again, I don't know, what are some of the things you're seeing from organisations that have that single source drift, but also partner effectively? And it could be with finance, it could be with other business functions as well, I guess.
[0:20:02] Kenneth Matos: Yeah. Better process flow, to begin with. And just being like, "What order do we make these decisions in, and which of these decisions are shared versus I make mine and you make yours?" Because I think, like, when I chat with HR people, one of the questions that comes up for them is like, "Well, now that our spaces are sort of converging, to do HR work, I need to know finance data, to do finance work, I need to know HR data. Which one of us is making the decision?" And I'm like, "You make it together?" And when they have better collaboration, when they have that foundational shared reality, they stop talking about what is true and they start talking about what are we going to do with it. And so, they're thinking about like, "How do we get there? What is the plan? Where are you going to come to me and say this is going to backfire for these reasons, so that I can just build it in to start?" And so, I think you just move very far past the 'what is' reality to 'what do we want to do about this' reality. And I think that's the real difference between an organisation that's sort of unified, both socially and technologically, and one that's divided. It's simpler to be able to get all the tools that you need. It's much harder to develop the collaboration that says, "We are going to now get rid of our silos and make this decision collectively and deal with the trade-offs that that creates, rather than push it up the ladder to, say, the CEO to solve between CFO and CHRO".
[0:21:39] David Green: Yeah, it's very interesting. And what you're saying resonates with me, Ken, from some of the research that we've done at Insight222. So, again, for listeners, we do an annual research study on companies, targeting the Heads of People Analytics. And what we've identified over the last three or four years is the characteristics of what we call leading companies in people analytics. So, these are companies that are delivering significant business value on a consistent basis. And one of those characteristics is they measure the impact of what they're doing, and they have a good partnership with their finance team. So, from simple things like having the same definitions, for example, what an FTE is, from using the same language that finance within that particular organisation uses as well, from getting finance to kick the tyres on some of the HR models before they then go and take those to other people in the business. Because I think you talked about that trust thing there. Rather than worrying about whether each other believes that their data is right, they're actually talking about how they can use it, which is obviously where we really want to get to with this.
But one of the worst things that can happen for you from an HR or people analytics perspective is if finance start questioning your data in front of the rest of the leadership team. So, again, a good tip that I've heard from a lot of effective Heads of People Analytics and senior HR leaders, is build that trust by getting finance to check your models; let them kick the tyres on it, as the phrase is; and then, get them comfortable with it, so they're not going to then raise their hand in the room and say, "We've got a different number for that".
[0:23:21] Kenneth Matos: 100%. I remember my PhD dissertation, and everyone was like, "Why are you getting the most rigorous stats guy on your committee?" I'm like, "Because no one's going to question him". He's going to tell me what good looks like, and when I deliver that, no one's going to question it. And same thing with finance. Getting them to sign off on it means everyone else is going to feel confident that those things are real. But I also think it gives you the opportunity to position what you're doing in terms of the money. So often, the narrative of HR activity is, it's for the good of the employee's feelings. And there's an aspect of that, that's true. But HR wasn't built as a function for that purpose. It was built as a function to help manage a variety of things that make the business more successful. And if success is often measured in money, you need to be able to have that linkage be natural and assumed, not constantly being proved over and over again. And so, as we talk about in a lot of our presentations at conferences and other places, on ROI, you begin with a use case that translates to some sort of financial outcome. And you build it from there, rather than saying, "Let's just go and do a thing. We know it's going to work, but we have no way of talking about how it's going to hit the bottom line".
Once you've done that enough times, you have that confidence. People will say, like, "Okay, I see where you're going and where you're coming from".
[0:24:47] David Green: People probably see when I'm at a conference and I talk about, "Okay, let's look at the partnership between HR and finance", and it is just simple things sometimes like, check the models, get the language, as you say, identify a real business challenge and work together to solve it, so that both parties see the benefit of working together. I don't know, if you're looking at what drives effectiveness in sales, for example, you're going to need some finance data, you're going to need some sales data, you're going to need some people data to really study it properly. If you can identify what drives value in sales, either from a team perspective or from an individual perspective, the business is going to be quite happy about that. The individual salespeople and the sales leaders are going to be happy about that because they want to be more successful and frankly earn more commission. And finance is going to be happy about that, because it potentially brings more revenue into the organisation as well.
So, it's kind of a win-win-win in that respect. And every company is selling something. So, it does seem a good opportunity, I think, to work. And I don't know, again, as part of the research, Ken, or outside of that, is there any particular examples of companies that you can give, even if you can't name the company and you say, "A finance company did this", is there any examples that you've got of HR and finance working really well together, either through having that kind of technical link, but also from that strong partnership as well?
[0:26:13] Kenneth Matos: So, I'm searching my memory now. I think some of the feedback we've gotten is actually, what I can talk about is where it goes wrong and what kinds of things would be different, because that's what we got in the data. So, we also asked people like what problems you had when you didn't have the right data. And so, they described a variety of things, from hiring two people for the same role to promoting a weaker candidate based on flawed data, that then became revealed, and they're like, "What do we do now? The person's been promoted and they're not doing a good job. And the high-quality person's now looking for a new job". And so, I think one of the challenges for a lot of this is we're trying to stop a lot of bleed. And organisations aren't good at recognising bleed. They're very focused on wins. And if you can solve problems that are like, "These resources are now available for reinvesting in more wins", that doesn't click quite as well for them.
So, I think a big chunk of this is really about helping your organisation wrap its mind around all these problems that just don't have to happen. They're there because you're not working together and you're not setting up your performance cycle and your compensation cycle in alignment, each person sort of going off and doing it on their own.
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One of the things I think you looked at in the study as well was around how people are viewing AI to potentially support this process. I'd love to hear what you found around AI, because obviously, we can't do any podcast episode now without mentioning AI. And also, if you've, again, got any example of any AI tools that you've seen. It might be something you're doing at HiBob, for example, or any examples of companies are already using these AI tools effectively.
[0:29:26] Kenneth Matos: Yeah, so I think one of the things that really popped for us in this study was that 87% of managers would welcome AI to help them with data analysis and action suggestions. I think we're really struggling with scaling information and insight and scaling response to that insight. And so, managers were very open, and this reflects research we did previously that showed that managers were more bullish on AI than individual contributors, because I think managers see a huge chunk of their job, it's not really their job. It's administrative work that got pulled out of other departments and made, like, how much can we stuff into the manager's job before they break?
AI, I think, steps into the arena and says, "The big challenge is the sheer amount of stuff. If we make that stuff quick and easy to process, how does that help you?" And so, managers, I think, are very eager for that, as long as they still retain override permissions. So, we also asked what would be required for them to trust it. And the ability to override what the AI decides to do is really important. And because, I think, we know that AI is powerful, but it's not perfect. And so, just like we with our colleagues will say, "You know what, you don't necessarily know everything", neither does AI. And so, being able to get something that can analyse the data quickly, give some suggestions and set you up to have a thoughtful decision about, "What do I want to do?" rather than spend all your time on, "What do I even need to know to decide?" I think is where people are excited to have AI support them.
So, I would lean into organisations, like HiBob, that are looking at ways to stitch the data together before you get to the decision question. So, by pulling all the data into one platform that actually talks to each other, because I think a lot of times people talk about a platform being all in one, but that really is only good for procurement. It's not actually good for any of the users because the pieces don't talk to each other. And I think AI could step into that gap and people are excited about that possibility. Because it's not work that anybody is sitting -- we've already done the job of cutting out the people who might have been excited by that work, and now it's stuck with people who are not. And so, I think that's where AI gets to be a real positive game-changer for a lot of people in making those activities more manageable.
[0:32:05] David Green: Yeah, and listeners, I'd definitely recommend tuning into an episode we did a while back, with Nicole Lamoreaux, who's a CHRO at IBM. She gave an example of how they have a quarterly promotion cycle at IBM and HR business partners were having to stitch data together from different systems, organise themselves, meetings with people managers in their consulting division, make recommendations around who got promoted in each quarterly cycle. And they brought AI in to actually do all the data stitching together and to make the appointments, so that the people manager and the HR business partner could have a strategic decision around who got promoted and who didn't. And they halved the time that that process took from ten weeks to five weeks, etc, and so, multiple thousands of hours over a year.
But I think the reason why that worked for them and what I'm hearing from you, Ken, around how you at HiBob, you do have a platform now where you can put finance and HR data together, you can't use the AI if the data's not good enough. You need to have those data foundations and that data in place, so that AI can work its magic and save managers time, save users time, and then help the managers do what they're good at, which is making decisions by surfacing that, those insights up, having done all the sort of stitching together work on that as well, which I think is important, isn't it?
[0:33:34] Kenneth Matos: I tend to tell people who are like, "What should I do to sort of kickstart this process for me?" I'm like, there's a bunch of things, but four things you could focus on that I think are really successful are, first, your data quality and integration. So, is the data actually good? Do you have processes that ensure that what the AI is going to consume is correct? If not, you need to start there, because there's nothing more important than actually feeding it the correct stuff in the first place. Second step is integration. Can it get to all of the places that it needs to get to? Do you have the right APIs? Do your products talk to each other well? Are you using the same naming language? I think that's going to be another big place because, as I said earlier, so much is built for a specific buyer. Now that the buyer is the org, not a fragment of it, the language needs to be put together, otherwise the AI thinks these are two different things. So, I think that's another important piece.
Once you've got that, then you need to lean into clear use cases with a theory of change and a success metric. So, what is the purpose for this? How does it affect the business's bottom line? If I can't explain that, it's a bad place to start. And a specific definition of success. It doesn't have to be ROI. There's so many people that I'm just like, "Why did you do this thing?" "Well, everyone was complaining". "Are they still complaining?" "No, they all seem happy". "Great, congratulations, it works. The thing you were setting out to do actually happened". So, being really clear about what you were trying to achieve, I think, sets you up for the right metrics.
Third, I say governance that encourages prudent experimentation. So, what you want is for people to have a set of guidelines and rules about using these tools that are giving them clarity on what they can do, what they can't do. If it's firm, say that that's firm. If it's something that's like, if you can figure out a safe way to do this, we'd love to talk to you about it. And that, again, gives people the ability to experiment. Because right now, especially with HR data, there's just this, what can I actually do to learn how to do this? You almost have to push HR people out of HR, learn the skills someplace in low stakes, then come back and be like, "Okay, now I can do it right". And that's just very inefficient. So, really thinking about those governance procedures comes next.
If you have those three things, then it starts becoming easier to do the fourth, which is just your cultural positioning, which is saying, "This change is a shared opportunity". I think businesses shot themselves in the foot by appealing to shareholders so openly with labour reduction opportunities, that now all anyone can talk about is, "AI is going to take my job". And that, I think, has to be fixed for a lot of organisations before they can actually get anybody to do anything. And that's why you need to have done the due diligence up front of, what is the data quality? Can we integrate it? What's our governance? And how are we using it, and what's it going to make different?" so that you can rebuild all that trust that I think we foolishly burned by saying, "We're going to make the businesses more successful by getting rid of you", and then turning around and saying, "Oops, actually, we still need you. Could you please do all these things?" Those four things set you up to actually drive a constructive AI change. And then HR and finance is just one manifestation of it. HR can also connect to research and development.
The big ROI example I always use in presentations is this idea of, could we make our research and development product to market time faster with appropriate training? That is HR, finance, and product all talking to each other to solve the business problem of getting the product out the door faster. How much money would that make us? That's a finance question. Do the people have the right skills? That's an HR and product question. And so, it really is moving away from thinking about just your own space. Last thing I'll flag is being ready for the politics that creates, because people are doing their jobs, they want to believe they're doing a good job. Coming in and saying, "Hi, actually, you're 50% below what you could be if you did this", is not an exciting way to get a partner. It really needs to be something that starts with their pain, and then expands outward to the organisation's pain in order to create real change.
[0:38:18] David Green: Do you think as organisations we need to, particularly with AI as well coming in and obviously the pressure on HR now to actually prove the value of the programmes that we're doing and its connection to the business, do you think we need to rethink how we architect decision-making?
[0:38:35] Kenneth Matos: I think that's an interesting question, because my gut says no. We actually just need to do the things we know we should have been doing and just never do. So, I feel like the thing that AI does is it raises the floor of what we expect in a decision-making process. So, I mean, you go out and it's like you're supposed to do your due diligence, gather the data, but what constitutes due diligence versus wasting time diving too deep, moves with the technology. And so, what I think AI is doing is it's changing that floor and saying, "You can do 15 other things". Do I need those 15 things? Did I ever need those 15 things? I think the piece that's going to happen is we're going to feel like we need to reinvent it. Whereas I think the reality is we need to go back to what we always knew, from research, from practice, works, and stay there even if the technology says, "You can do 500 other things". I don't need those other 500 things. These are the five things that actually answer my question. I don't think we're very good at articulating that up front. And so, we end up just doing work.
So, that's where my head goes when you ask that question of like, I don't think AI has actually blown apart the human experience. I think it just reveals that more data can be processed in real time for the average person. And then we have to decide what data was actually useful. And that is the thing we don't like to do. We just like to throw everything at it and let someone else make the decision, which we already know is wrong.
[0:40:23] David Green: And I guess that's also the risk for us in HR. Traditionally, we provided multiple slide decks of HR data, people data. And senior executives don't have the time to wade through that. I suspect most of those things aren't even looked at, frankly. So, AI can help us do more, but that's even more important that we curate even better than we maybe have in the past and really try and understand, okay, what are the key metrics? What are the key insights that the business needs to be able to make decisions? And less is definitely more on this particular occasion. And again, that talks back to what you said earlier, Ken, about the kind of political acumen element of this, and then obviously the business acumen as well. The better we understand the business, the better we're skilled at influencing and understanding what the problem is, the more likely we are to give information that actually supports decision-making.
[0:41:22] Kenneth Matos: Yeah. I mean, in my career, the analytical skill that has been most valuable has not been my ability to run the numbers or present the deck. It has been the ability to say at the beginning of the process, "Why are we doing this? Because it doesn't actually address the goal that we put in front of us". And sometimes it does, and I'm wrong, but then I learn. Or they go, "Yeah, you're right. So, what should we be doing?" Like, okay, if this is what we're setting out to do, then this is what's actually useful. And I think a lot of HR people know that, but they don't necessarily feel entitled to call that out, because they likely have to do it through asking questions at early stages. And that just feels like it makes you look dumb, when in reality, it's what teaches you what you need to have to be able to realise, that's not actually what we're setting out to do here. And so, I think that's a real power experience that people need to have of like, the asking of the question is setting you up to have the insight, as opposed to showing you don't know what you're doing.
[0:42:28] David Green: It's that we can't just provide the what, we need to provide the 'so what' and the 'now what' a little bit, don't we? I mean, it sounds simple and it's obviously not simple, but that's effectively what we need to be thinking about when we're doing this. And obviously, the more able we are to combine data sets together, then the more likely that that's going to resonate with our audience, whether that's internal or not.
[0:42:54] Kenneth Matos: I think the other thing to keep in mind is so much of this is really, at its core, about time. There's not enough time to do the thinking. And some people think faster, some people think slower. The more stuff you make available, the longer it takes. But the decision window doesn't move. Like, "A competitor is going to do something. I need to make a decision tomorrow, but I need a 15-page report". I think we need to be kind enough to ourselves to realise that a lot of our problems are born from trying to just move so fast that our brains can't keep up with it. And that's where, again, I'm like, that is the value of AI, to allow us to do the thing we know we would have done, fast enough to actually participate in the decision-making process. Otherwise, we're just spitting out decks that nobody reads, but they know they need to have them for compliance reasons, which is just an unsatisfying reality for everybody.
[0:43:48] David Green: And, Ken, we've talked about some of the capabilities that organisations need and HR professionals need. Are there any other capabilities that we've not covered that you think that you'd like to highlight for listeners about how we get to better, more consistent decision-making?
[0:44:05] Kenneth Matos: I think the other thing is scaling it through people. A lot of stuff now wants to be top-down, not necessarily in an authoritarian way, but like the top understands and therefore everybody else will do what they're supposed to do. I actually, a long, long time ago, I was in a car with a bunch of MBAs while I was still in grad school. And they were like, "We don't understand what your degree is. What do you do?" I was like, "Well, you're the ones who know what everyone's supposed to do. I'm the one who knows how to get them to do it". And they were like, "Oh my God, I'm horrible at that. I know these 500 people, if they just did these things, we'd make all this money". I'm like, "But those 500 people aren't going to do those things, and here's why, and here's how you presented it that's going to turn them off". And really beginning to lean into, you can't just mandate this stuff, you have to actually take people through the journey with you. And I think a lot of organisations are trying to, again, move so fast that they end up leaving people behind and then they become anchors rather than accelerators.
So, I think the other thing organisations really need to do is think about, how do I bring the org with me, not just the five decision leaders so I can say I got the decision through and then adoption fails or transformation doesn't actually happen? This is why change management doesn't occur, because we get agreement at the top, but we don't bring everybody else along for the journey. And we need to invest in that, otherwise it all sort of drags to a halt.
[0:45:41] David Green: Yeah, very good. Ken, before we get to the question of the series, I'm sure a lot of listeners are like, "Okay, I'd really like to read this Better Together report". Now, we will put a link in the show notes, but obviously, presumably, they can find it on the HiBob website. Is that right?
[0:45:58] Kenneth Matos: Absolutely. You can go to hibob.com and look at our reports and resources, and you'll be able to find not just this report but a whole slew of insights and guidance on this topic and a bunch of other ones. So, I would suggest going there. You can also find me at drkenmatos at LinkedIn.
[0:46:17] David Green: Perfect. Okay, we get to the question of the series now, Ken. So, this is the question we're asking everyone, all the guests in this series, which HiBob is kindly sponsoring. And we've touched on this a little bit, but he's a bit more specific around ethics, I guess, and governance, perhaps. How can HR lead the responsible and ethical adoption of AI and not just manage it? So that's, how can we lead from an HR perspective?
[0:46:42] Kenneth Matos: So, I actually really hate the word 'adoption', because it keeps us focused on activity, and activities go in a thousand different directions. It's really about purpose that we need to be focusing on if we want to be responsible and ethical, because that's where responsibility and ethics live. And so, I actually feel like a lot of the adoption conversation is a distraction. What you really want to do is focus on process or innovation optimisation. So, what is it that we are making better with this tool? Is it the thing we currently already do? Or is it something we've just never been able to get to, because we just didn't have the resources or the tools to do that? When you look at it through that lens, you have a context, because ethics and responsibility are contextual. And so, once you have a context to AI, then you can actually start saying like, "Great, we could have this data, but here's all the ways in which it could go wrong. Or here's the spots where people are going to now just get lazy and let the machine make the decision, and here's the risks of that. Is that okay?" And I think there's research right now that people will tune out after about 750 cycles of an observation on an AI tool. So, it's like, "Great, what happens at 751?" If it breaks then, is that acceptable? But that can only really be discussed within a use case. And I think the AI universe just prompts us to make bad decisions for the organisation.
I'll leave with a really concrete example. So, there's a great case study in my field about cable installation, where they wanted as many installations as possible within a single day. And so, they incentivised the installers to just move fast, which resulted in them getting a lot of calls saying, "My cable doesn't work", because they just sort of shoved it all together and ran out the door. And so, they instituted a requirement that you can't have a functional complaint call within a certain amount of time after the installation to get the bonus. And so, they were still prompting them to move fast, but only as fast as actually resulted in good outcomes. And I think that's what we need to do with the AI conversation. As opposed to just pushing for adoption, we need to push for adoption within a context where we can see if it's creating problems or not.
[0:49:22] David Green: Ken, it's been an absolute pleasure speaking with you today and learning more about your work, and obviously some of the insights from the Better Together report. You've already mentioned how people can access going to the, I think you said the research and reports section on the HiBob website to find the Better Together. And you mentioned obviously your LinkedIn, drkenmatos, if they want to connect with you as well. How else can people learn more about HiBob and maybe a little bit more about, because I understand that you've now got a finance platform as well, so you're integrating HR and finance with some of your customers, I believe?
[0:49:59] Kenneth Matos: Absolutely. We're trying to live what we're talking about, which is bringing the information together in a way that is easy to manage and elevates the conversation. So, you'll be seeing us at a variety of conferences. I'll be at Transform this year, talking about HR and finance and how they can be better together.
[0:50:19] David Green: Great. Well, Ken, it's been an absolute pleasure. Thank you very much for your time today, and I'm sure we'll bump into each other at a conference in the not too distant future.
[0:50:27] Kenneth Matos: Absolutely. Looking forward to it.
[0:50:30] David Green: Thanks again to Ken for joining me today to discuss the Better Together study from HiBob. I certainly recommend that listeners access the report themselves. For those of you listening, I'm curious, what stood out for you the most from today's episode? I'd love to hear your thoughts. So, head over to LinkedIn, find my post about this episode, and let me know what resonated with you, maybe how your HR and finance teams partner or don't partner. I always read the comments and love learning about the different perspectives across the field. And if this conversation got you thinking, please subscribe to the podcast and share it with a colleague or friend who you feel might benefit from hearing it too. And for those who would like to stay in the loop of what we're working on at Insight222, follow us on LinkedIn, or head to insight222.com. If you want to receive the latest insights and developments, I also recommend subscribing to our bi-weekly newsletter at myHRfuture.com.
Right, that's us for today. Thanks for listening and we'll be back next week with another episode of the Digital HR Leaders podcast. Until then, take care and stay well.