Episode 229: The Role of People Analytics in Measuring AI Adoption and Business Impact (Interview with Erik Schultink)
Rolling out AI tools is easy. Making them deliver real value for people and performance? That’s the challenge.
In this episode of the Digital HR Leaders podcast, host David Green is joined by Erik Schultink, CTO and Co-Founder of Worklytics, to explore how organisations can move beyond the hype and start measuring the real impact of AI in the workplace.
Join them as they uncover:
Why most companies aren't seeing the tangible value of their AI investment
What leading organisations are doing differently to embed AI into the flow of work
How people analytics teams can track meaningful signals of successful AI adoption
The unintended consequences of AI on workload, productivity, and burnout
The role of ONA (Organisational Network Analysis) in improving AI integration
Who should own AI adoption
Practical steps for HR leaders looking to shape a responsible, people-first AI strategy
Worklytics helps leaders understand how work actually happens with data-driven insights into collaboration, productivity, and AI adoption. By analysing real work patterns - from meetings to tool usage - they empower teams to work smarter, not harder.
And here’s something special: Worklytics is offering Digital HR Leaders listeners a complimentary AI adoption assessment to understand how your teams are really using AI - and where untapped potential lies. But don’t wait - spots are limited.
Learn more at worklytics.co/ai
[0:00:00] David Green: A recent study by BCG found that 74% of companies report they have yet to demonstrate tangible value from their use of AI. And Deloitte's recently published Global Human Capital Trends report found that while the promise of AI is that it will improve productivity and wellbeing by reducing our workload, in reality 71% of companies say AI has actually increased their workload and decreased their productivity, and 61% say it will increase burnout. I'm David Green and today on the Digital HR Leaders podcast, I'm joined by Erik Schultink, CTO and Co-Founder of Worklytics, and together we will be exploring one of the most pressing challenges facing organisations today, how to measure the real impact of AI, drive meaningful adoption, and ensure that the employee experience is enhanced.
I'm particularly excited for this conversation, as today Erik and I will explore why simply rolling out AI tools rarely leads to the outcomes organisations are hoping for. We'll also discuss what leading companies are doing differently when it comes to integrating AI into the flow of work, and the signals people analytics teams should be tracking to ensure AI adoption is supporting their workforce. We also discuss where responsibility for measuring AI adoption should sit within an organisation, and why HR and people analytics leaders are well placed to help shape AI strategies that are not only effective but also aligned with culture, fairness and long-term business goals. So, whether you're just beginning your AI journey or looking to deepen the impact of existing tools, this episode offers practical insights to help you lead with clarity and intention. With that, let's get the conversation started.
I'm curious, as someone that's now specialised and working in the HR technology and people analytics field, what's top of mind for you right now when it comes to AI and its impact on the world of work?
[0:02:05] Erik Schultink: Yeah, so I think we've seen it be hugely transformational. And I would say that one of the most fascinating things is kind of the paradigm shift from when I studied it 10, 20 years ago to now, is the fact that it has become, in the current generation, so people-centric that we're really personifying AI systems as if they were kind of human coworkers and collaborators. And I think that really opens the opportunity for HR in thinking about how to integrate these into our organisations. And they sort of naturally fit into the paradigm of organisational network analysis as additional nodes and coworkers throughout the organisation.
[0:02:44] David Green: And I don't know what you're seeing at Worklytics, but some of the research that we did at Insight222 last year, so we do an annual exploration of what's happening in the people analytics field, we had 348 companies participate last year, the survey ran from May to July '24, and one of the questions that we asked was, "How long has the AI journey in HR been in your organisation?" And I was quite surprised when we got back that 62% of companies said they were in the first year of their journey, a further 18% said they hadn't started, and it seemed to be the hypothesis seemed to be that the launch of ChatGPT around 18 months earlier had almost acted as a catalyst. Because as you said, we've been talking about AI for a long time and AI has been happening in other business functions maybe, but it would seem from that, if we just take that survey, that most HR functions hadn't really started going with AI. Is that something you're seeing at Worklytics as well?
[0:03:45] Erik Schultink: Yeah, absolutely. I think it's taken a long time for people to understand how it's going to integrate into the organisation. And we've had kind of this, if you're familiar with this sort of shadow IT term, a lot of individuals in companies getting their own ChatGPT subscription and copying and pasting stuff back and forth and working with AI that way. And only I think this year really, even just Q1, we've had a number of customers who have only rolled out proper enterprise-level AI solutions on that timeframe. And so, that's where they've really tried to bring that stuff in-house under direct sort of IT control, where they have the observability and they have the sort of understanding and the safety and have defined the controls around how this is going to be used. And so, as we've done those initial integrations, it's now going to become a question of how do you maximise the effectiveness and gain out of those systems, now that you're beginning to integrate those into the enterprise.
[0:04:45] David Green: Yeah, and there seems to be a general assumption out there that once you give people access to AI tools, productivity will naturally improve. But it's not always that simple, and that's not always what happens, is it?
[0:04:59] Erik Schultink: Sure, yeah. I think in the early days when we've seen this, the lift has been extremely uneven, both across roles, but also across teams. Anecdotally, we've seen in some of the initial exploration data that we have that adoption sort of starts, and when people say they're using AI in high usage, they mean sort of once a week or a couple times a month, type of things, that they're going in and using ChatGPT. And so, I think there's an opportunity for a much larger uplift. One anecdote we've seen is how important the manager is in that transformation. In teams where the manager is a higher active user of AI, we've seen an uplift of up to 5X in how much their direct team uses AI as well. And so, we've seen the implementation of AI and the adoption of AI being very uneven across companies based on those sorts of dynamics. So, it's certainly early days, but that's giving us some clues on how organisations need to start thinking about the rollout of AI and how they can encourage adoption to gain productivity that way.
[0:06:06] David Green: How does work really get done in your organisation? Worklytics helps companies measure collaboration, productivity, and AI adoption, using real data, not guesswork. With insights into meetings, tool usage, and work patterns, Worklytics helps enterprises optimise team performance to get more done, faster. They're currently offering podcast listeners a complimentary AI adoption assessment, gain insight into your organisation's AI usage, and unlock its full potential. Limited spots are available. Learn more at worklytics.co/AI.
I know you work with some particularly advanced companies, a lot of technology companies that have Worklytics, so when you look at some of those organisations that are making good progress with AI, what are they doing differently, especially when it comes to maybe embedding AI into the flow of work?
[0:07:22] Erik Schultink: Yeah, so I think it's very early days. So, I would say even the most advanced companies have just started to release in the last three to six months more officially sanctioned toolboxes for using AI, and frameworks for, "Okay, I want to build a Slack bot", or, "I want to start using AI in this tool". Before, there was even from just a vendor legal agreement perspective, having any kind of AI component in that triggered a huge additional legal red flag, where we don't know how to handle that, what should the data be, all those compliance and controls. So, I think now, even the most advanced companies have now gotten through that, they have their legal team to have an understanding of, "Okay, what do we need to do this? What requirements do we need to see from these vendors to start using these AI systems in our company?"
So, I think that is where we've gotten. I think even the most advanced companies just now have those frameworks. We're starting to have really officially sanctioned, enterprise-wide level AI use, and are just now entering the experiment phase with, how are we going to use this and how to best leverage this. So, they're starting to open this up to their teams and say, "Hey, go ahead, figure out how this goes into your workflow". And so, I think the moment now we're seeing amongst the leaders in this field is just, "Okay, now we have a measurement problem. How do we see where this adoption is happening, where people are unlocking the value, and how do we maximise and accelerate that?" So, that's where really we see Worklytics' role, is trying to measure that, measure how people are adopting these systems now that they have it under official channels, where it can be monitored and is something that's happening in corporate systems, as opposed to off on people's own devices or so forth.
Now that it's really within corporate systems, we're talking about something that is within Worklytics' domain of being able to measure that and develop a data strategy so your team has the data to understand the adoption and start to gain insight into how you're going to leverage that and use that to multiply productivity. But yeah, we're very much, I think, in the early days of that where the most advanced companies just have the framework to roll it out and are struggling with the, "Okay, how do we push adoption? How do we maximise and leverage this to multiply our efficacy?
[0:09:48] David Green: Let's stick with that measurement point there, because I know that you've published at Worklytics something on the Worklytics website, if you go to worklytics.co/blog, for those of you listening in and want to find out more. And you've actually presented a framework for measuring the impact of AI on your organisation, which has three steps: adoption, which you mentioned; proficiency; and leverage. I think it's fascinating, and having spoken to a number of peers in the industry, I think again this is something that's really resonating. For those listening and maybe who haven't seen the article, can you maybe walk through that? And then maybe as an addition to that, given that we have a lot of people analytics professionals listening, what's the role of people analytics teams in this as well?
[0:10:35] Erik Schultink: Yeah, I mean the basic framework you're describing is just how we're trying to help people formulate about how they think of their AI journey from simply adopting the tool. So, we start to be able to use ChatGPT, we gain proficiency in that, and that starts to be something that people are able to be skilful with and develop prompt engineering, and so forth, and thinking about just as if you had to write an effective Google search before, "How do I effectively frame something in a way that these systems are able to give me the most effective answer back?" So, that's kind of the gains in proficiency there. And the leverage is then, how do we actually integrate this into our workflows to multiply the efficacy of our team? So, now that we've become skilful in using this, what are the highest value problems that we have, where it's going to provide the biggest wins? And so, yeah, that's kind of the framework is where we're thinking about it at this point, to try and help people understand where they fit on their journey.
To the second part of your question, "What's the role of HR?" I think the role of HR is certainly that this is a people-centric problem, and that's been the big paradigm shift, in sort of anthropomorphising these systems as agents and that we interact with through chat and talking to kind of as if they were human beings. It's been very natural to start thinking about this and integrating these into your organisations as if they're co-workers, so these are kind of agentic co-workers in your organisation. And so, I think HR has a natural role to think about, "What's the most effective way to integrate these into the organisation? How do we understand that collaboration? How do we use these to multiply product productivity?" So, you have the organisational network analysis and organisational design angles to that and thinking about how we're going to leverage this to maximise our efficacy.
[0:12:25] David Green: And I suppose that leads to the next question which is probably around employee experience. So, firstly, again, there's two pretty two elements to this. If AI is being pushed without people in mind, you talked about it very much, the paradigm shift here is that it's people-centric, could we end up creating workplaces that if we don't have that, put that people centricity at the core, could we end up creating workplaces that are actually worse to work in than maybe some of the workplaces today?
[0:12:54] Erik Schultink: Well, sure. I mean, I think there's those opportunities and pitfalls with any disruptive change. I think the opportunity for HR to lead is to frame the questions in that way. I mean, if you have IT leading, I think it's obviously more about how software's replacing people; as opposed to if HR is leading, it's more about obviously how are we integrating these coworkers to multiply people's productivity? The state of these AI agents is, these are removing a lot of tasks that, to be honest, aren't necessarily taking real intelligence, sort of summarising and so forth. And the power and what these things are good at right now is a lot of the repetitive work. And I think it can be very empowering to people if I can offload some of those sorts of things to let me focus on the stuff that requires true intelligence and is going to help me have the most effective work life.
So, I think that's the opportunity for HR, is to frame these questions in that way like, "How are we going to make people more effective, have them have a more meaningful work by focusing on the work that matters, and helping them to automate their workflows where the stuff is repetitive and tedious and repeatable?" because those are what these AI systems are going to be most effective at, where this is something that's been done 100 times before, let's let the AI learn from that and do it again. And you can move on to the more novel problems, the more interesting problems and focus on that. And in extent, I mean, this is going to be people sort of managing teams of agents, to a certain degree. And so, there's a lot of sort of HR mindset and how that should be done.
[0:14:43] David Green: And I guess, as you said, we're kind of at the start of this, so it's difficult to predict what's going to happen in the next year, two years. Going beyond that is probably just madness to even try. But what you're painting there is a very different world of work to the one we have today; and again, listening to you, that if we are able to remove some of the more repetitive process-driven tasks from jobs effectively, from people's roles, then obviously the promise is that they can focus on stuff that's more value-adding, more interesting, I guess, from an individual level. But again, this is where probably you can see a big role for HR, around things like job design. And I don't know what you're seeing ready for maybe the companies that are maybe further ahead than others on this. Is this something that they're already actively thinking about and doing?
[0:15:38] Erik Schultink: Yeah, I don't think it's evolved to that point quite yet. I do think that's kind of the opportunity, is to think then, "Okay, instead of sort of focusing as much on carrying out the process, we need to think more about designing the process, designing the workflows, in which these agents are playing the role of nodes and carrying out". And so, that's the opportunity, is to have people do more focus on that, of designing what the workflow should be, as opposed to simply implementing the workflow. So, I think yeah, HR definitely has a role to think about that, because this again is modelled as agents, in effect, become nodes in your organisational graph, and we need to measure how those agents are being used and being collaborated with so that we can understand how to best integrate those and leverage them.
[0:16:27] David Green: And I guess that's maybe the unique opportunity for HR. It's helping the organisation to incorporate these new technologies to be more efficient, to drive employee experience, but to drive productivity and ultimately outcomes for the business, but at the same time, re-imagining how we deliver some of the HR programmes that we deliver as well, and looking at different roles within HR as well, and thinking how they're going to change during this. And maybe that is a unique opportunity in many respects for HR leaders.
[0:17:03] Erik Schultink: Yeah, absolutely. I mean, there's certainly a section of the AI being applied to solve HR problems directly. And then, AI is going to have a big role in trying to help HR gain productivity on its core functions that it already does. And then, yeah, the broader implications for the business I think is something that HR is uniquely positioned to help the organisation understand, because it is so people-centric. And how we're going to be integrating these systems and some of the challenges that that's going to create are very much within the realm of HR.
[0:17:39] David Green: I want to take a short break from this episode to introduce the Insight222 People Analytics Programme, designed for senior leaders to connect, grow, and lead in the evolving world of people analytics. The programme brings together top HR professionals with extensive experience from global companies, offering a unique platform to expand your influence, gain invaluable industry insight and tackle real-world business challenges. As a member, you'll gain access to over 40 in-person and virtual events a year, advisory sessions with seasoned practitioners, as well as insights, ideas and learning to stay up-to-date with best practices and new thinking. Every connection made brings new possibilities to elevate your impact and drive meaningful change. To learn more, head over to insight222.com/programme and join our group of global leaders.
So, we talked about the opportunity for HR. Now, many HR professionals, they obviously don't have your background in AI and computer science, and many HR professionals that I come across maybe aren't that technically minded. So, first question is, what advice would you give to HR professionals that kind of want to learn more about generative AI, agentic AI, and AI in how it can benefit the organisation?
[0:19:18] Erik Schultink: Sure, yeah. I mean, I think obviously it's been covered a lot in popular media and so forth, how these bots and how the LLMs and that kind of shift in the technology, which was, I mean coming from the traditional AI experience, quite unexpected. I think there was not a broad expectation that these would be as effective as they were. So, I think that's fascinating. And certainly, there's a lot of literature out there for people to read and gain an understanding of how these tools are working. And so, I think from our perspective, again, is to start to understand how these fit into the traditional ways of thinking about how HR is thinking about work. And so, we see this as fitting pretty well under the organisational network analysis framework, where you have, okay, agents, we interact with them and the current paradigm of LLMs is that these are kind of chat-based or communication-based systems, and they interface with the humans through that sort of a medium. And so, they naturally fit into organisational network analysis as if they are nodes in that collaboration graph. And so, I think that's the opportunity for people to learn and apply.
That kind of gives you the starting point for thinking about how these might integrate into your organisation and say, "Hey, can these play the role of drafting some communications?" and I can send out a couple snippets and it has a bunch of context from my organisation that it can then turn that into something that is polished enough to be externally facing, or so forth. So, I think those are some of the opportunities and some of the frameworks that HR can use to kind of get an entry point into thinking about AI and LLMs and how that's going to change and integrate into your organisation.
[0:21:15] David Green: And of course, again, organisational network analysis, you mentioned that there, Erik. It's something that to a greater or lesser degree, HR and people analytics teams principally have been using now for a number of years. Do you see that the advent or the acceleration in the adoption of AI is going to act as a driver to help ONA grow and increase its use in companies?
[0:21:46] Erik Schultink: Yeah, I think that is, well, I just think it naturally fits to how these systems are currently being deployed in the early days. So, generally speaking, sort of Slack bots or chat bots more generally is one of the ways we're seeing organisations start to roll out these systems within their organisations, giving someone a Slack bot for HR that they can ask HR questions to, and the AI can take a first stab at answering that, and then it can escalate to a human if it's not one of these sort of repeated questions that fits well, that the AI is able to sort of chain answer with some more or less pre-canned variant. So, I think that's where organisational network analysis fits really well into thinking and analysing how these systems are beginning to be integrated and adding value into the organisation.
I think you have to start with the measurement as sort of organisational network analysis, we can understand, "Hey, which teams are interacting with these AI systems the most? What's the frequency of that interaction?" You can apply a lot of the methodologies that you get from organisational network analysis and thinking about how these nodes are adding value to the graph around them. You can apply those same sorts of measurement techniques and analysis techniques to thinking about how these AIs are being integrated, if that's happening in Slack bots, or some other sort of thing that you're communicating with. So that's, I think, the real opportunity we've seen, is a lot of this data and communication channels is happening in the same way as it was before, but it just happens that now some of the agents in the graph, some of the nodes in the graph are these AI systems.
[0:23:30] David Green: And I think that's one of the things that you do at Worklytics, maybe not uniquely, but certainly I think the emphasis that you've placed on the connection between organisational network analysis and employee experience, and then the impact that has on business outcomes, that's something you've been kind of pioneering, as it were, at Worklytics for a number of years now, haven't you, coming into the pandemic, coming out of the pandemic, looking at return to office? And I guess AI is kind of the next natural stage of evolution in that.
[0:23:58] Erik Schultink: Yeah, well, we've very much seen that AI is going to be the next sort of shift in how people work. And so, we saw very much in the transformation we had with the pandemic and RTO, and so forth, a huge interest in using organisational network analysis to understand how that was changing how people work. So, looking pre-pandemic, once you moved to remote, then remote to once you return to office, how that changed your networks, how you collaborated, the strength of those networks, how siloed they were, all those sorts of things were helping people to understand that. And then, we've been able to show that those correlate with a lot of the outcomes that HR traditionally has wanted to track, whether that be retention, performance scores, engagement surveys, so forth. A lot of the things that you're able to identify possibly by looking at the organisational networks that you can get from Slack and email and calendars, and so forth, you're able to show that that stuff correlates.
So, I think that will be the fascinating next thing to understand, is when you look at that for communication graphs with these AI agencies, how does that correlate with these outcome variables as well for people that HR will care about, engagement, retention, so forth. Already early days, we've seen that the adoption of AI systems with some early things on onboarding, like as people are getting into the company, that new hires are much bigger adopters of AI than the people who have been in the company a long time, that it's helping people interface. And when you're starting to come with a fresh mind on how I can work most effectively in an organisation, these are some of the people that are most willing to adopt and start integrating AI into their workflow because probably they don't have as much of a history of how I get work done in this company. And so, we've seen organisational network analysis being applied in conjunction with the traditional HR data sets that we like to look at, to really gain some insight in how people are working, how AI adoption is happening and how that's helping drive the various measures of success we want to look at in the organisation.
[0:26:21] David Green: And anything particularly you're seeing that people analytics teams are doing around the AI topic? Obviously, we've mentioned organisational network analysis, but maybe more from a, I don't know, a governance topic that's helping their organisations advance maybe quicker than others?
[0:26:43] Erik Schultink: I think, yeah, I think from a governance perspective, it's just about being conscious and putting the momentum behind your legal and privacy teams to develop the frameworks to be able to use these tools, and understanding and developing your norms around what you're comfortable with, what you want to see in agreements, what you need to put in every vendor agreement to be able to start to unlock the AI power that some of those vendors are providing, where they're coupling that in with LMs. I think that's where we've seen companies start to coalesce is like, okay, "We've been able to think about this for a while, grapple with what the implications are and decide what our strategies are going to be around allowing or adopting AI, whether we pull a bunch of that stuff in internally, whether we're willing to trust some vendors, what our requirements are". So, I think being on that journey and making sure that you're putting some focus on that journey so it happens quickly, which will position you to be able to adopt AI.
So, certainly there's traditional compliance and privacy and legal aspects of that, that HR is going to have more competency to bring to the table. And that stuff is probably, to a certain degree, beyond what adopting just any other traditional IT tool might've had with it. So, I do think that that is a role that HR can play and start to put some momentum behind and say, "Hey, we need to be out in front of this thinking about what we want to see in these agreements, so that we're in a position to move quickly when a new startup pops up with some specific LLM implementation that's super-applicable to what we do. What are we going to need to be able to see to jump on that and leverage that to add value to our business as quickly as possible?"
[0:28:28] David Green: So, I think listening to you again, Erik, and again something that we're seeing in the work we're doing Insight222 here, the natural assumption sometimes would be that we're rolling out technology across the organisation, this should sit with IT. And I think what I'm hearing from you very much is this isn't something that's should necessarily sit with IT. Clearly they have a role in this. It's something that HR has an opportunity to lead on. There has to be some sort of partnership between HR and IT, and people analytics obviously being a big part of that. What's your advice for organisations that are maybe a little bit earlier in the step here? Is this a partnership across different functions within the organisation, presumably?
[0:29:13] Erik Schultink: Yeah. I think what we've traditionally seen and what HR really needs to push for is having the data. Even, you would think IT, but often, even IT isn't getting good data out of these systems on what the adoption is, what the usage is, to be able to model that in people and then to be able to model that in people-centric ways to understand, "Okay, which teams are using this, which teams are adopting this, how do we layer in all the rich data that HR has on top of the much lower-level data that IT has to really gain insights and leverage it for our organisation?" And to a certain extent, IT's interests aren't entirely aligned here with the business because these are huge investments, and showing data that, "Hey, this big part of the organisation isn't really using this", isn't necessarily what they want to surface. So, I do think people analytics has a role to kind of understand that and layer that stuff on and should be in the conversation. I think we've seen in some organisations, these become big C-suite questions on, "Where are we on AI? Where are we on that rollout? How do we compare with our competitors? This is going to transform our industry. Where are we relative to them?"
So, I think the big challenge for people analytics is to have the data. We're going to see benchmarks; being able to benchmark that data against other companies is going to be super-important. So, that's something that we're going to offer from Worklytics, to help people understand how they compare, where they fit. And so, that is where people analytics can really play a role in helping people understand, in terms of transforming our work, how we work, transforming our workforce to be able to leverage these tools, "Where are we in that journey? How do we compare? Are we falling behind or not?" Because if you aren't getting those questions from your C-suite yet, you will be, because otherwise they're going to fall behind.
[0:31:11] David Green: Yeah, and potentially very exciting for you and your colleagues at Worklytics, you potentially can help organisations as they compare themselves with each other, because when something new is happening, we don't really know what good is, do we? So, that's going to be a really helpful service to help them to understand that.
[0:31:30] Erik Schultink: Yeah, absolutely. The way adoption is defined, I think, is still an evolving question, and we're trying to take some initial steps on establishing a framework for what that should look like and help people understand how they should think about that. But yeah, absolutely, that'll be something that we're all discovering together, I think, in the next 6 to 12 months here.
[0:31:49] David Green: Exciting times. We've got two questions left, Erik. So, first of all, it might be a bit of a summary of some of the stuff that we've already talked about, what advice would you give to our listeners, particularly HR and people analytics leaders that are listening, who want to play a more active role in shaping AI strategy within their organisations?
[0:32:09] Erik Schultink: Sure. Well, I think you need to have the data to be able to measure. So, first and foremost, you need to develop what your data strategy is going to be, how you're going to get the data pulled together to build your data warehouse, or whatever system you're going to use, to be able to start to position yourself to answer these questions, because these questions are going to become, "Where do we sit on AI adoption? How are we rolling it out? What do the skills of our workforce look like in utilising these tools? Do we have the right skills? Are people then using this? Where are our areas of opportunities? Where are our maximum points of leverage?" So, I think we've gotten some early insights on some of those answers, in terms of new hires and managers being key early adopters and key points of which to drive adoption in the organisation. So, I think it's being prepared to understand and analyse that within your organisation, see whether those sorts of trends are holding, and position yourself to be able to act, and so forth, and advise your organisation more broadly in, "Okay, AI is going to be our theme of 2025. What's that going to look like? How are we going to be tactical about doing that? Is that training and specific communications with managers, being able to provide them with data that shows how important they are to this transformation, and how we can drive that?"
[0:33:32] David Green: And it's interesting, you mentioned around data strategy there. Now, you and me will know this because we've been working with people analytics teams for a long time, it's one of the banes of people analytics leaders' lives, that getting movement on the data strategy within HR sometimes can be very difficult. Are you seeing as well that the interest in AI is almost acting as a bit of a Trojan horse to kind of speed that process up and bring it back up the agenda again with the HR leadership too?
[0:34:05] Erik Schultink: I think certainly it adds a sense of urgency, right? And I think there is a broad awareness, at high levels within companies and organisations, about how important and how transformative AI is going to be. And I think it's up to people analytics to speak up and really say, "Hey, we can help understand that in the business and we can help answer those questions, help you understand where do we sit on that journey? Where does our industry sit more broadly? How is that going to change work?" Because those are sort of bigger questions that I think you need to make your organisation aware of that people analytics can help answer.
[0:34:44] David Green: Yeah, important questions for the future of any organisation. So, Erik, every series we do at the Digital HR Leaders podcast, we have a question of the series, so we'll be asking all the guests on this series, and I know it's something that you and the team at Worklytics have helped many organisations on. So, the question is, how can HR help the organisation understand and improve team effectiveness?
[0:35:09] Erik Schultink: Yeah, I think the big opportunity in 2025 is simply the adoption of AI. And I think, with the data that you can get from these AI systems, combined with the data that HR has about organisational structures, team structures, managers, tenure, all the outcome variables around engagement, performance, and so forth, being able to understand the transformation, you know, does AI adoption lead to higher engagement, more productivity; where it does, where it doesn't; what interventions do we need to make to ensure the best outcomes for the business there? So, I think that's the big opportunity of HR and people analytics to take a lead and to add value to the business, is helping to understand that transformation, because it is going to be people-centric in terms of how the implementation is done, and also that people are kind of the paradigm on how we're going to understand these agentic AI systems as those become co-workers in our workplace, that we're interacting with on a daily basis.
[0:36:13] David Green: Brilliant. Well, that seems a great place to leave it, Erik. It's been fascinating to listen to you. I've learned a lot in the last 45 minutes or so. Before we part ways, can you share with listeners how they can follow you and learn more about Worklytics and everything that you're doing for organisations and HR and people analytics leaders around the world?
[0:36:33] Erik Schultink: Yeah, absolutely. So, the best place to get our single consolidated view on the AI stuff is to go to worklytics.co/ai. So, we're going to group all of our AI-related thinking and offerings there. So, you'll see what our views are on AI, our frameworks or how to think about the AI transformation of your workforce, and what sort of data we can make available to better understand that. So, we see measurement as the absolute key thing and are developing offerings to be able to help people better understand.
[0:37:08] David Green: Sounds like a treasure trove for people to go and unlock there. So, Erik, thank you very much.
[0:37:14] Erik Schultink: Thank you.