Bonus Episode: How AI and Behavioural Science Are Transforming Employee Listening (with David Barrett & Katarina Coppé)
The future of HR isn’t about collecting more data - it’s about knowing exactly what to do with it.
In this special bonus episode of the Digital HR Leaders podcast, host David Green is joined by David Barrett (CEO) and Katarina Coppé (Chief Commercial Officer) from Welliba for a conversation about changing the way we listen to and understand employees.
Join them to learn more about:
Why traditional surveys are no longer enough to understand your workforce
How passive listening and AI can surface richer, faster, and more actionable insights
Practical ways to connect employee data to business outcomes at scale
Strategies to navigate data privacy and regulatory challenges without slowing innovation
Real-world examples from industries including retail, finance, healthcare, and manufacturing
If you want to understand your employees more deeply, act faster on insights, and deliver measurable impact, this episode, sponsored by Welliba is your playbook for the future of HR.
Welliba, winner of the 2024 HR Unleash Global Startup Award, is redefining people, culture and organisational insights.
Using the latest AI technologies combined with behavioural science, their EXcelerate solution, instantly analyses all available public data, delivering deep insights into people and organisations - without the need for surveys. Discover how you can elevate your talent strategy, transform your workforce, and stay ahead of your competitors.
Learn more at offer.welliba.com/insight222
[0:00:00] David Green: In HR, we are not short of workforce data, but what we are often short of is clarity. Clarity on how to connect that data, interpret it meaningfully, and use it to drive real human-centred transformation. I'm David Green, and today in a special bonus episode of the Digital HR Leaders podcast, I'm delighted to be joined by two fantastic guests on a mission to change that. Joining me today are David Barrett and Katarina Coppé, respectively CEO and Chief Commercial Officer at Welliba. Welliba is an employee experience platform that combines behavioural science and AI to deliver real-time personalised insights, empowering organisations with the data they need to support wellbeing, performance, and engagement at scale. In our conversation, we explore how HR's relationship with data is evolving, from legacy engagement metrics and surveys to integrated actionable insights. We talk about the role of AI in transforming HR research, why siloed systems continue to be a stumbling block, and how leaders can move from data collection to real impact.
So, if you're looking to make better use of your people data, connect insights across the employee lifecycle, and develop a more agile, human-centred HR function that understands their strengths and weaknesses in their competitive talent landscape, this episode is for you. With that, let's get the conversation started.
Katarina, David, welcome to the show. Could you start by sharing a little bit about your backgrounds and what led you to where you are today at Welliba? David, maybe we'll start with you.
[0:01:47] David Barrett: Yeah, thanks for having us today, David. So, I am a psychologist by background. When I headed off to college, my father was delighted because he thought I'd be able to give him a massage when I came back at Christmas. So, he was very disappointed with what psychologists actually do; some would say a lot less useful than physiotherapists. But I've been lucky enough to spend the last 25 years kind of on the intersection between how psychology, data science, digital services all kind of combine together to try and better understand people at work to generate insights, predictions, and connect talent to business outcomes. And I suppose it's been a very enjoyable journey and a fortunate point in time to have started work, in the late 1990s, when the internet was really becoming an obvious, massive innovation that allowed us to comprehend data and the world better in ways that wasn't previously possible. And it's as exciting today as it ever was, with the advent of all LLMs and generative AI and machine learning. And I'm sure there'll be something else in the future, but these couple of years are tremendously exciting, David.
[0:02:55] David Green: They certainly are, they certainly are, David. And we'll look forward to seeing how you mix that psychology and data science and services background into the conversation. And, Katarina, same question to you.
[0:03:05] Katarina Coppé: Yes, thank you very much. Also, 25 years' experience in the human capital industry, go way back with David as well, no psychologist, but applied economics and human resources. I've done most of my career selling and delivering solutions to help better predict behaviour at work and also help develop people at work. So, that's been my passion. And also, I've been a Global Learning and Development Manager in a global company. And so, when you ask what has led to you today and where you are now, well, two things. On the one hand, we know that people typically don't like change. But if we want to improve the practice of people and the practice of the organisations, we typically have to sell change. So, on the one hand, technical experts, like people analytics leaders or HR leaders, really need to understand how to sell that. That was also a passion project that led to a book that I recently published. But on the other hand, which is the topic of today, is really, okay, well how can we help the people practise then practically with what type of solutions that potentially might challenge the status quo in employee listening?
So, that's where we are here. We know it's a bit broken, the employee listening approach. So, very keen to share some new ideas on how potentially HR people and people analytics leaders could take a different approach to it.
[0:04:20] David Green: Well, what are your views, and then, David, maybe chip in afterwards, what are your views on some of the most significant changes that you've witnessed in the way that organisations approach HR, particularly around measuring and understanding people, and how behaviours drive actions, I guess?
[0:04:36] Katarina Coppé: Yeah, there's been lots of waves, right, and things come and go, we know in HR typically. But things that have stayed consistent and that we see actually are really important to look at how effective are we, is the role of data and decision-making, right, because data, as we all know, and especially from the people analytics, that is going to continuously stay important. But the way we've looked at understanding people and behaviour, especially in the organisation, has always relied on surveying. And now surveying, engagement, sentiment, workforce experience, all of those things, we've always measured it through surveys. That is something that we, I think, in the context of today, is probably something we need to challenge. Why? Because one of one out of three HR people or professionals says actually there's no real relevance anymore, or the programmes don't really lead to meaningful results anymore.
Not only that, it creates friction, it gets people, leaders and HR business partners working on action planning. We've all been there, right, completed a survey, had to wait for the town hall for the sharing of the results. And sometimes, we create those expectations on actions that we might not want to pursue, because they're not going to drive competitive advantages or they're actually not the right things to focus on. So, I think we're a bit at a turning point where, how do we listen; how do we understand people at work; and how do we drive it to connect it to business outcomes? I think those are the key things. We're at the crossing right now. I think we need to take a different approach there.
[0:06:12] David Green: And, David, you might want to chip in here as well, but are you saying that we shouldn't do employee-listening surveys, or you're saying that they're not enough on their own?
[0:06:21] David Barrett: Well, I think, David, if you think about human nature in general, people tend to like doing what they want to do. They do what they have to do, but they might not necessarily like it, they'll do it against their will; and they sometimes do what they should do. But if you can find a way of understanding people at work by being able to use passive and existing data and things that are naturally present in the environment, to work out how to make people and work be able to interoperate better, without placing a burden of people having to be asked questions, complete surveys, be interviewed, participate in research, lose loads of analyst time, use loads of management consulting time, in order to do all these kind of have-to-do, should-do type stuff, if there's an alternative way of doing that that's more agile, quicker, cheaper, and better able to combine the data together, I think it's kind of a no-brainer to do that.
So, I don't think that it is the end of ever a person being surveyed, but I do think that there is a seismic shift going on where someone would question the relevance of assuming that is the modality you'd use to try and understand people at work.
[0:07:44] David Green: That's really interesting, because we've seen a lot of organisations bringing in passive listening, if we want to call it passive listening, with active listening through surveys, and obviously, and I'm sure you can tell the audience a lot more, passive listening does give you much more, well not necessarily much more insights, but certainly different isn't, doesn't it? And the technology, I guess, is getting better and better now that we can actually do this at scale continuously.
[0:08:11] David Barrett: Yeah, for sure. And as well, that being able to use all existing data all at once allows you to create combinations of how your workforce looks against other companies' workforces. You can't go surveying another organisation necessarily to find out what's going on there, to stack rank yourself against them on a granular level. You know, if there was something you wish you knew from two or three years ago that was an antecedent of a big change, or before a merger or an acquisition or a leadership change, or a whole change in policy or programmes of work, you can't survey back in history. Like, you can't interview a dead guy. But if you're able to use all this existing data that's held within large language models and the public and social news web, you're able to ingest all that and comprehend things in the current tense, historically, look at patterns of information that might have been lead indicators of something, be able to see what has been the impact of change, and then also being able to connect that to external business metrics around things like your JD Edwards score and customer satisfaction with our retail banking clients, or your Skytrax score if you're an airline, or your average revenue per employee over a certain period, based on the way you operated with your workforce or rewarded them; being able to use all these types of data allows business people, culture, context, metrics to become aligned in a super-agile way to actually answer the questions that people really care about, that are to do with productivity and impact, as opposed to it being, "Well, we're just going around measuring the sentiment, the engagement, the experience of people".
To be honest, they are super-important for people, but they're just seen as enablers or characteristics of how we try and treat people to make work better and understand people, if you're a business leader. They're not an endgame in and of itself. And they should never be confused with the endgame, because businesses are ultimately about performance, customers, and outcomes. And us as leaders, workers, and experts within them are there to serve customers and enable a business. The business doesn't exist for the people. The people are in that organisation to provide something that's of value to the customers and the marketplace.
[0:10:36] David Green: This episode is sponsored by Welliba. Welliba—winner of the 2024 HR Unleash Global Startup Award—is redefining people, culture and organizational insights. Their EXcelerate solution uses the latest AI technologies combined with behavioural science to instantly analyse all available public data and deliver deep insights into people and organisations —without the need for surveys. Discover how you can elevate your talent strategy, transform your workforce, and stay ahead of your competitors. Learn more at https://offer.welliba.com/insight222
And, David, staying with you actually, listening to you there, as I'm sure you're seeing as well in some organisations, and hopefully more in the near future, HR has shifted from its traditional role of being a support function to be more of a strategic partner for the CEO and the business, it sounds like what you're talking about is an important enabler of that. How do you see some of these changes influencing HR's ability to drive real business transformation?
[0:12:14] David Barrett: Yeah, well I think these types of agile information systems where you can understand a workforce, a competitive labour market, skills, talent, culture, business metrics, customers all together. They allow a Chief People Officer or a talent expert or an OD person to get onto the front foot, a bit like roles you've had, Katarina, over all your career, where rather than being somebody who's like an order-taker, or a respondent to pre-existing cadence of life, where in effect you're in the HR department, and in effect you're operating some kind of A&E ward in a hospital and you're just responding to things, or as our colleague, I'll use a more politically-correct version of the word he uses, you're operating in a 'crap vortex'! He uses other more colourful language. Everything's getting done to you.
Whereas if you work in this way, over a couple of hours and immediately, you can either respond to a strategic question or you can form a hypothesis that you know will probably be valid and supported by evidence about, "Okay, well really, does it make a difference if people are working from home and have flexible working, in terms of how it affects our customer performance and ratings in some kind of consumer banking?" Now, that's the kind of question someone wants to answer, but to find that out through conventional means just ends up being a big, months-and-months' long process to find it out. And by the time you found out an answer, nobody cares anymore, everyone's formed an opinion and made a decision.
Whereas the way that Katarina and I are working now, we're able to evaluate tens of thousands of companies all at one time, look at then all their customer metrics, look at all their people metrics, and be able to form an opinion about a specific company that that person can then take and argue their case as the HR person around, "Well, we should reward this group more, or we should be flexible with this group or not", or, "We know that it's to do with the way managers act is connected to how customers see our teams in terms of net promoter scores. And it's not anything to do with incentives, so why the hell are you going to pay them more money, just because they're complaining about money, when that's going to do nothing to help our business?"
Being able to have that kind of information in a real, live, on-demand way allows you to respond agilely or to be proactive. And I think that's the big difference. These types of generative AI systems, when trained properly and using explainable AI that's ethical and legal and not in breach of every regulation under the sun done properly, they allow the HR person to be that, to be able to kind of approach the situation with a point of view to prompt action, rather than just be there as a, "Now I need you to do some kind of onerous, turgid task that's going to wreck everyone's head".
[0:15:12] David Green: Yes, it's again, David, it seems like what you're saying there is it enables the HR leader to provide a more rounded view with an external lens, and a more advisory position than maybe, I don't know, an action-taker, or something like that.
[0:15:29] David Barrett: Yeah, so Katarina, you've got wonderful stories with Accenture and Louis Vuitton, for example, of how we've used this type of data proactively to help inform a business, rather than just passively the HR rolling along.
[0:15:44] Katarina Coppé: Yeah, we had one customer, for example, who had an engagement score with super-participation rates; you would think a luxury position, 90, 90.3, 91. And they were thinking, "Okay, well what is going on? How good is 90? Is this against best in class really excellent, or are we as everyone else? Or if we were to compare specifically against a player that is similar in the European space like us, how are we doing effectively against them, like really comparison like-for-like?" And so, basically what I think most of the engagement and eNPS metrics that are currently used and are the more popular ones, they're a bit shallow, they're self-referential. It's only looked at from an internal lens, which means it doesn't really tell us anything. Like, if we don't exactly know and people would say, "Yeah, but the employee-listening providers or technology platforms, they would offer benchmarks", yes, they would offer benchmarks, but they would offer benchmarks calculated on the customers they're serving. And it may not be the customers you want to be comparing yourself to.
So, by really identifying specific players, maybe outside of your industry, you're going to get much more clarity on the competitive advantage or the blind spots that actually are driving your business, and actually give the context to those metrics that are really, you know, the metrics need contextualisation. If I give another example, if you see in an engagement score, people are not satisfied with leadership, as a business leader, I would ask, "Well, are we at risk of losing more people if we continue down the status quo, like if we don't develop and invest in leadership development? Or what are the actual best-in-class people doing? What is our competitor doing on leadership development that we are not considering?" So, it's that contextual information that leaders need to really make investment bets. And I think without the full picture, without the full context of what's happening in the industry, outside-in perspective, not just the inward-looking perspective on the metrics, I don't think we'll ever be investing in the right things. And we can expect different outcomes, but basically with the conventional approach, we won't get there.
[0:17:53] David Green: And I think we'll get into conversations, as this episode develops, about how you can actually help companies access some of the other data to help them answer those questions that you mentioned there, Katarina. So, Katarina, obviously you've been an HR practitioner as well. So, I think this is a good question for you really. So, as HR continues to evolve, where do you think we need to improve? And you've got listeners here, pretty much everyone working in HR or serving HR as well as a vendor or a consultant. What are the areas where HR can do better to leverage people data for real impact?
[0:18:34] Katarina Coppé: Yeah, so I think as David said earlier, the connection to the business outcomes. I think we're often in a vacuum. We're not as commercially savvy typically in the HR function, and there's a bit of change aversion sometimes, so we stick with the status quo. We have these approaches that have been longer-term contracts. So, it's really challenging our status quo and not taking the cost of retention. Like, some people take a retention percentage as just a cost of business, but we should really challenge, "Is there anything we can do differently with an outside-in perspective, looking at another industry that we're not necessarily comparing ourselves with?" So, broadening the perspective I think is a key one, not just looking internally, externally is a key one. The commercial aspect of, "Is what I'm doing and investing now actually going to lead to business metrics and business outcomes?" That's an important aspect, otherwise, again, the conversation with the business leaders, the C-suite, won't be really leading anywhere, and it gets a folded chair rather than a real strategic chair at the table.
So, it's, "Yes, can I get your opinion? And now we have your opinion and then we'll move on". It's really to be there to offer the contextualised ecosystem feedback on, "Okay, well what should we be doing to drive competitive advantage?" And I think if we take that external lens, if we look at the business metrics and we look future-focused, that's another key one, most of the metrics currently are pictures of the past. Think of your engagement survey. You close a survey. By the time you analyse it months or weeks later, the problems have either become bigger or the problems are no longer there, and you might be really action-planning on things that have no relevance. I think future-focused business acumen and making sure we are open for change and selling the change internally, I think.
[0:20:21] David Barrett: Yeah. And, Katarina, I think it's more of a three-legged chair half the time people have, if they're in people experience or listening or HR when they're at the table, because they've often done a great job. I was working yesterday at one of the big professional service firms with their global employee experience leader, and she was telling me she'd ran a huge, big survey on Qualtrics and had lots of information gathered, had all the right stats that she wanted about health, experience, rewards, career, learning, well-being, all these type of things that are strong things that should help build a good organisation. And then, all the partner cared about was that they'd lost a load of people who went off working for Revolut. And he said, "I don't care about that. All I care about is why are these people leaving us, going off to fintech companies, like Revolut? And can you answer me that now?"
She then was adoring the fact that it would be simple to actually, if you had a different way of conceiving how the information is, because you can spin up using all generative AI and LLNs, you could have that information at your fingertips to know that and rank yourself directly in a city with a worker group, an occupational group, and have the direct reasons of why someone would leave you to go for them, and answer that kind of quite erratic, impulsive leader who really wants to know something because they care about it. And they won't care about that in a week's time because they'll care about something else like, you know, why are people wearing white sneakers this week in work? But that wasn't fashionable when I was a child. You know what I mean? So, they're interested in what they're interested in now and that's what they want. And it allows the person, you know, that thing of what Katarina is talking about there, is that person in human resource has done a great job taking months to do some super piece of analytical work. And then all that work, in effect, goes to lay, because the person won't listen, or by the time they get anything done, nobody cares, or they found out stuff that people don't want to listen to anyway.
I think that's another challenge if you're in human resources, is you're going around creating loads of information by asking people lots of questions, then creates an expectancy that something needs to be done about it. And companies are probably more interested in working out, "Well, what's actually the right things we could focus on to make our business and people better?" but not necessarily want to be responsible then to have to do everything about everything just because people said something. And I really enjoyed that discussion with the leader in that professional services firm about, like, it all came down to was she able to answer, why are people quizzing and going off to work in fintech companies? And she's wishing now, "Why didn't you just tell me that before?" But now she's happier because she has a way of maybe answering that question live.
[0:23:11] David Green: And again, we always say to people analytics leaders in particular, the better you know your audience or the common executives that you're working with so you can kind of anticipate some of the questions that they're going to ask you, great, but you can't anticipate everything. So having something at your fingertips that you can potentially answer the questions live, that's powerful.
[0:23:33] David Barrett: And we were with another person we were working with the other day who'd read an article in The Economist, they were a German Human Resource Leader, and they were horrified to hear that the average number of sick days for a German employee is like 19.6 currently, and it's about 6.-something in the UK. And then there was a load of expletives of all variations. And the person was going, "How are you meant to know why is this and what's causing that?" And like, "My God, this could not be sustainable". And, "There's no reason why we should have German people three times as unwell as British people. That means we have to have an extra worker, one in ten extra workers to do everything". They were going like, "My God, this is a disaster!" But then trying to work out in some coherent way about what might be the solution, what might be the lead indicators or causes of the problem, or, "Is this a universal thing in my sector? Is it certain types of workers? Is it certain types of demographics?"
To try and understand this intelligently by taking on a project would turn into a PhD. And by the time that happens, there'll be another war or a pandemic or something will have happened. And again, the guy who's asking the question won't care. So, being able to kind of comprehend and get at that fast is so useful.
[0:24:54] 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.
Well, David, I think we're leading to talking about some of the technology that is available. You've mentioned some of it and how it can help people provide answers in real time. But despite all that technology being available to us, many organisations are still struggling with siloed data, lack of actionable insights. What do HR leaders need to do differently to connect the dots between data sources and turn those insights into meaningful action?
[0:26:32] David Barrett: What I would say, I think everybody knows that, yes, if you could combine external business data, internal people data, cultural data, managerial data, leadership data, you could definitely work on brilliant cause-and-effect comprehensions of what's lead indicators of what. So, I think that's probably not the more useful thing to get into, in my opinion, because I think every woman and her dog would know that that's a good idea if you could do it ethically and legally. I think the interesting thing in this is that we're speaking to a global audience here, but you could break it into like three big groups, if we say Asia, Europe, North America. USA has an enormous lead on Europe in the area of the uses of AI, because their regulatory system has oriented more towards innovation, fragmented laws. There are some strict laws about fairness, transparency, auditing, reporting, but in general, you can kind of combine information in ways to help you solve a business problem, as long as your actions are then legal.
In Europe, GDPR is a wonderful invention and is brilliant for privacy and respect to people, and that information is only used properly and for what it's meant to be. But that is the parent of the EU AI Directive, which is, in the main, a badly thought-through concept, and is designed, I think, mainly to try and punish companies that tend to have built an enormous economic impact. And Europe doesn't have vendors that are comparable to them. I think that the EU AI directive has made it very difficult for European companies and clients to think about, "How can I work with data? Because if I try and bring things together, firstly I'm worried about will I get fined 4% of my turnover? Am I going to be in breach of GDPR? Is this going to be classified at the wrong end of the pyramid?" all these kind of things around how you combine and use data, "Do I have the right permissions? Is it being used for the purposes under which it was gathered?"
I think the right way to look at things in Europe at the moment, until Ms Van den Eynden and her group start to evolve the way the EU AI directive is, is that it is a way better idea to not use PII or stuff that has to go to loads of infosec and data privacy, and to use all this existing type of data that's held within the LLMs, like TextCortex, Claude, Grok, Gemini, Anthropic, Perplexity, OpenAI. There is billions of datasets that's created by your organisation, and it's pushed out into that public space, and it's in that environment. And that's where all of the resources and ingredients are to help you attack the questions anyhow. And they're not subject to then this thing of, you know, the silos we talk about, or, "All this information is here and that's there and I can't combine it, and it's difficult to get at, and the law and the regulations". It just ends up with all the European HR Leaders in effect almost hiding under the table for fear of, you know, the sky will fall down.
This is a super way globally, be you in Asia, US or Europe, to be able to attack the situation by not doing anything that's in breach of privacy, nothing that's illegal, and nothing that is designed to do any harm to a person or a group. So, rather than I think that's about, you use the word like 'reframing' things a lot, and being able to 'reinvent' or 'reimagine'; so, rather than trying to work out, "Oh, how do I get all this previously difficult-to-combine data and all these silos together and do it legally?" that'll just turn into a 'no good deed goes unpunished' with murderous problems along the way, I think it's better just to reinvent your way of thinking and start with all this data that doesn't have to be dealt with in that way. And you'll get the same answers at a hundred times the speed and for a tenth of the cost, and you won't end up with a set of handcuffs on, or being tomorrow's news story about being the bad man or bad woman who did something with the data.
[0:30:44] David Green: And we'll come to you in a minute, Katarina, because I really want to talk about the human element of this as well. But I mean certainly, I mean I'm sure you both see this, when we go into big companies, sometimes, well, maybe it's not such a good idea, they won't let their employees use some of these LLMs within the organisation. So, as we know, there's plenty of reports of the employees using them outside on their own. How can we kind of educate leaders and how can HR educate leaders that these things aren't, as you said, the sky isn't going to fall down if people actually use them? And maybe, David, you can explain how Welliba actually helps organisations use these tools effectively to get insights that support business decisions and outcomes.
[0:31:38] David Barrett: Yeah, so I know that I might sound like a bit of an adventurist there from some of my previous outbursts, but Katarina and I are very well used to working in highly-regulated environments to comply with EEOC laws around equality in the US, and we have huge clients who have to comply with all SOC 2, ISO, GDPR. So, we're hugely competent in data privacy and IT security. But the secret to this, and I'd love Katarina to comment as well, is that the important part of these systems is to have them as explainable AI. So, in effect that you can reverse engineer how did you come up with a decision and advisory and outcome. So, I think one of the bigger fears about the legitimacy or validity of what comes out of LLM-driven type systems or generative AI is something about, even if it was right or wrong, so if I ask Perplexity or Anthropic or something about something, it'll make a pretty cool answer, make a nice table, make a graph, make advice. It's like a little mini-walking management consultant. But the problem is, you can't then verify or establish how did it go about forming that quantitatively or qualitatively, or how can I evidence that that is true, correct, defensible, appropriate, right.
So, the secret to, I think, the right way to do it, for anyone here who's a researcher or another entrepreneur or a provider, is that you're going to have to put a lot of work into validating some kind of a model that predicts something that has a cause-and-effect relationship, and then be able to work out how, when you're requisitioning all the data and you're classifying it and modelling it, that in effect, if you had to run the system backwards through all its sequences, that you'd be able to take it back to its root cause to be able to explain, how did I get to that answer? Now, I know that with LLMs, the data iterates super-fast, and it depends on the timestamping, and some LLMs have live connections to the internet and some have timestamped arrangements, and it's not a totally perfect science. But you can control for nine-tenths of all this, and you should be able to have explainable AI in the same way as people had to explain how psychometrics worked to comply with correctly predicting performance, but doing it in a way that's not discriminatory to minority groups. Big companies don't use psychometric tools that don't have the ability to be documented and articulated correctly like that. There's no reason why you can't do something very similar, or expect your vendor to be able to do something quite similar with these generative AI, LLM, external data, internal data type tools. So, that's the first point.
Then secondly, I think the bit about being worried about your employees using these types of things. Well, if they're using something like that, I don't think there's any particular risk, because then you're getting something that's quite explainable and it's contained within a well-organised system of advice from a vendor, agreed with your HR and your leadership. Now, I think what you're referring to, David, is more when people are putting things into LLMs or asking questions, you know, they're putting company information in. So, that's back to the same old chestnut of InfoSec, not actually LLMs. Because Katarina and I would be advocates of, you shouldn't have to be going getting any information anyway from inside the company to power these things, which actually, again, it's a reframe. It changes the problem statement of, you don't have to be worried about that because no one's shoving anything into something anyway. But this thing of people putting big spreadsheets with pay and company strategy documents and payroll files and everything up into an OpenAI, well that's real buyer beware.
Like, Katarina, I don't know what to say about that, but you put it all up into Instagram either, would you? You know, you shouldn't do that. Or if your company wants to do that, they should have like a secure area in Azure or something, where it's a locked-down version of it and you're training it for your own self. That's an education problem. But of course, it's more of an information security thing, too. Employees shouldn't be putting big piles of sensitive company data up into some third-party system. Katarina, do you have anything to say about that?
[0:36:11] Katarina Coppé: For me, and coming back to what David said earlier on the human aspect, I think how we use it, it needs to apply principles that when we orchestrate a question and we want to look and compare, we need to be able to trace back and we need to orchestrate it as evidence as you would look at it with a technical expertise lens on it. So, otherwise, why? Because I think the employee-listening approaches right now, it's a bit of a self-fulfilling prophecy. I ask, "Am I doing well on my strategy?" all of the questions people are asking in their yearly surveys, it's just asking things that you don't necessarily know are the more important ones. So, for me, really understanding the human beings in the company, it's going broader and holistically, right? So, it's understanding the employee experience in a holistic way, it's the sentiment, rather than you guiding them on a specific question. It's just, what is there that is actually making or breaking a good experience, or the sentiment, compared to your peers. So, I think if we have an approach like this, the human aspect is actually, instead of looking at an anecdote or someone internally shouting, "We need this or that", it's actually taking a scientific approach and a validated approach to analyse if this is really the thing to invest in. So, I think that's hopefully giving a human aspect, rather than the anecdotal human aspect, more importance.
Then secondly, I think in terms of investments, I think anything that has to do with AI-driven recommendations or any smart summaries, typically someone needs to analyse this and the human aspect will always have to review, "How relevant is this practical recommendation for me in my maturity, my company? Do I bring the stakeholders along? Can I sell this idea internally?" If I can't, then you can have a great career framework or skills framework, but if no one takes ownership of their development or people are actually stuck in their own ways, the manager doesn't help them to develop, then all of the beautiful recommendations will not work. So, we'll always have to have a human in the loop to understand not only how does this fit in my ecosystem; which competitors do I want to compare? So, the prompting even needs to be with the human being in mind, right? "In my organisation, I'm losing talent against a given specific competitor or industry. What are they doing differently?" So, the prompt already is a human scoping a problem, scoping the challenge, but then analysing all of the results and making sure that the investments are made on the things that make most sense for the company.
[0:38:46] David Barrett: Yeah, Katarina, like we have a big Midwest hospital we're working with, and they're kind of the poor relation against three other big mega healthcare provider hospitals around them. And the person needs to work out what are the frailties and weaknesses of the other hospitals in terms of how they acquire and retain nurses and, "What are the things that we can do even though we're the underdog to defeat these other hospitals, to get these prized nurses?" Now, that's all that counts, how to attract, get, and retain and develop that gang. What doesn't count is all the work that goes on to try and work out what are the reasons and where's the information about what. That's just work. That's just hole-digging.
[0:39:32] Katarina Coppé: We're trying to get to the answers more quickly without the friction.
[0:39:35] David Green: So, Katarina, staying with you, I mean we've talked really about the importance of moving beyond just doing surveys and employee engagement scores. And we've talked as well about what you're advocating very compellingly, is a different way of understanding our employees. So, maybe there's areas around that that we haven't talked to yet. Please highlight some additional points, but maybe you could also then tell us who's doing this well? Presumably you're working at Welliba with some organisations who are actually understanding their employees in a more nuanced and better way, and as you said, connected to outcomes as well?
[0:40:16] Katarina Coppé: And they're all sizes, all industries. It's not like this is purely your global company. It's actually available for anyone with the size of, I would say, 500 employees more or less. So, it's really open to anyone who's open to innovate, right? So, we have multiple clients in multiple industries, whether it's from Louis Vuitton, as David said, Accenture, Medtronic, there's really clients even in Europe that are in the agriculture sphere that are in retail B2B, it doesn't really matter, as long as we have people who want to challenge the status quo thinking, but also have a more open mind on what is employee experience, because sentiment has been a very limiting concept, and engagement as a metric is a past-looking metric. It's an outcome, it's not a driver of the engagement, so how can we really formulate, and that was one of the foundations we started with Welliba as well, is to give a more holistic perspective on employee experience, not just, "Are you happy with your onboarding? Are you happy with the touch points that the service provider, or Workday, is offering you in your company?" it's all of the above, but it's really linking human beings and the interaction they have with what's offered in the organisation and understanding that dynamic and splitting it out, so that we know what can we fix in the context or what needs a development of people angle, rather than a contextual change in rewards or in things that have to do with the organisation.
Because, of course, if you're an early careers person and you are put in a context with a leader that is micromanaging you, well for the early careers who doesn't know anything else yet, maybe that's great, that's still motivating. But if you have other people employed in the team that are ending career, having a different purpose in what they want from life, they will see what's given to them in a completely different approach. So, understanding this holistic perspective of what is it that makes people thrive, what is linked to people, what is linked to the organisation, having a good concept of employee experience to understand what drives the business outcomes, that's how we connect behavioural science basically to the outcomes. And that's, to David's point, one of the core building blocks for us to be able to compare. If you don't have a model, you can't compare, because if someone writes something on the internet, and they write, "My manager is micromanaging me", okay, well that's an evidence point, it's negative. It's saying something about autonomy, saying something about manager effectiveness, but I want to compare, in the same way, to another company. So, we need some kind of model to classify them and to visualise the information in a very similar way.
But again, the model is one way to make sure we can categorise if a customer has a model. In the banking industry, we spoke about risk culture models that are really mandatory. These are models that sometimes seven to eight FDs are analysing, analysing manually, but they don't have the story around the analytics or the data. So, here again, the approach could help to structure, analyse, and give practical recommendations to what they can do to improve.
[0:43:11] David Barrett: Yeah, and David, I really like the tactical stories. I'm a great believer in the ground game and that things are built from the bottom up in how things work. And I love the clients who pick something like, we had a Japanese company who's trying to build its HVAC, heating, ventilation, engineering, and service business in the Czech Republic, and they were having trouble competing with DYSK and the other big European company. And they wanted to know, "How could we get more, better people for building this technology in the Czech Republic?" And they were not able to work this out from Tokyo, but they had a real clear thing of, "I want to work this out". And the same thing with the banks. We've got a couple of US banks, and they spend their time arguing 'til their heads fall off with the Chief Executive about the return-to-work thing, and what are actually the real consequences or not in terms of the money-making that are connected to stuff to do with how we treat the bloody people in the retail bank. And most of the time, everyone has an opinion, that's also part of the sentence with inappropriate language, you know, "Opinions are like something else".
But having the data, like I'm really proud of the work we're doing there, where it's allowing the HR practitioners to have a sensible discussion about the likely business impact, as well as the people impact, of if you were to tweak around certain things about work conditions or flexible working or working from home or the technology attached or the way meetings and management works. There's a solution that is not toxic and can get a good outcome for customers and business. And I really like when the customers are super-tactical, because I find that much more interesting than when people are just talking in terms of grandiose things like, "Oh, how would we get the national sick leave in Germany down to 6 instead of 19? That's very interesting, but why don't you boil it down to, "I'm a man or a woman here and I'm in Stuttgart and I have this company in this sector with these types of workers. How do I fix my feckin' problem? The country will have to sort itself out, I want to fix my company with my workers". I think that's what's interesting. I love them clients, the big grandiose statements of, "Oh, $8 trillion was lost in productivity because of something, something, something", whatever about that, that's irrelevant. What matters is what's going on in your own house.
[0:45:38] David Green: Yeah. That question sounds like a job for government rather than individual companies.
[0:45:42] David Barrett: Yeah, that's beyond our pay grades. But I love when people are really locked onto something that has a high value and that everybody's interested in, and being able to go at it fast is a really enjoyable thing for the workers, the colleagues, and the business and the customers.
[0:46:00] David Green: So, David, again, we talked about this a little bit, but you might want to add to what we've talked about. I mean, it's all about the business objectives at the end of the day and how we can use data to give us insights to help us have better outcomes. But what are the steps that HR leaders should take to ensure that the data they're using is relevant and aligned with their business objectives? There's a mindset thing, I think, here as well as a technical thing, isn't there?
[0:46:27] David Barrett: Well, I think that they should have a mindset of being proactive. So, I think the best thing to do is to find some way that doesn't have a lot of friction or onerous work or time-consuming work or loads of cost, to try and go and check out a few hypotheses. So, I'm not going to tell any one client whether they should care about on-time performance or something, people being sick, average money they make per person in their sales force, whatever. They can pick within reason whatever it is, whether it's a risk management thing, a productivity thing, something about developing, retaining people, it can be a talent or business question, whatever tickles their fancy, you know what I mean? I'm not sure if that'll translate very well, David, but it means whatever prompts them to be curious. They should go and form a couple of opinions like this and then bring those ideas into the table, you know, say, "This is what I'm trying to contribute. I would like to make our organisation better". Either we'll have faster turnaround times if you're a government agency dealing with the public, less errors, or we have a couple of central banks who would say, "We could use these types of things to look at the people and culture of the clients that we supervise, and then try and work out, is there interesting things that could help us train and build policy to have better and more ethical behaviour going on around culture in banking, that would then be good for the public and good for the banks and good for people?"
To go with those ideas and have a bit of evidence built around it, you won't get every idea through. Not every idea will be interesting to the person you're trying to talk to, because maybe he'll just say, "I don't care, I want to know why the Revolut steal all our staff!" But my advice would be, try and get on the front foot, try and get yourself some data, try and have an opinion.
[0:48:25] David Green: And Katarina, what's the first step that you would advise listeners who are really interested in what they're hearing to get started on their transformation journey?
[0:48:35] Katarina Coppé: Yeah, for me, similar to what David said. I think unless you challenge the status quo, we're very busy often in HR. There's a lot of tactical things and responses and reactive questions that people try to get answered, running around, which means taking a bit of a holistic perspective, looking not just inward, but outward. I think that's definitely for me a key one. Challenge the conventional thinking, because you won't get different outcomes if you continue to put old wine in a new bottle, right? You want to really make sure that the status quo is challenged. And that means, as you said, mindset as well as data. It's the openness and mobilising the change with the data to back up what I try to change in the company.
[0:49:16] David Barrett: And one other thing to add to that, and I mean this in a kind way, as opposed to an antagonistic way, but everybody has had horrific experiences in big companies dealing with data privacy officers and their legal counsels. And that doesn't mean that all the lawyers and data privacy people are bad people, you know what I mean? Like 95% of them give the other 5% a bad reputation. But it's a really difficult job, it's really difficult. Once you get into that interface of the lawyers and the data privacy and the InfoSec, you're getting into starting off with such enthusiasm. But you have to have such stamina and patience and know-how and experience to navigate this, you're highly likely to get stuck, especially if it's more new to you and you're not an expert. And then, you get derailed because they are highly expert and they're very risk-averse. You should start with something that does have nothing to do with GDPR or nothing to do with taking information from out of your own company into a system of analytics to work out how to do something. Use stuff that exists in a public space that doesn't have any PII. And that would be a good way to avoid a 'no good deed goes unpunished' type thing. You'll actually achieve something then, and you won't need to go through all this complexity, which is necessary to run the legal obligations of a big company, I appreciate that. But you shouldn't start off down that end of the field and get yourself shot in the ditch before you even got out onto the park.
[0:50:55] David Green: And to wrap things up, firstly, thank you very much for being on the show. I've really enjoyed the conversation. How can listeners stay in touch with you and find out more what you're doing at Welliba? Now, you kindly showed me Welliba, the platform, a few days ago, and it's very impressive. So, maybe as part of your wrap-up, Katarina, maybe give in a paragraph what does Welliba do and how can organisations find out more about it?
[0:51:25] Katarina Coppé: Yeah, so I think as we discussed, we're on a mission to disrupt some of the employee listening and insight solutions to help organisations make better decisions in, "How do we transfer my workforce; how do we really attract and retain talent?" So, it's really important that that new approach is something that is low friction, that is really easy to adopt, and we definitely want to make the HR leaders make it easier for a new solution to be brought in. The insights and the approach that we've taken actually, we were awarded the global startup award by UNLEASH. So, it's not that we are saying it ourselves, it's also been seen as from industry experts' perspective, as a new innovation in technology to solve persisting challenges. So, I think finding out yourself, following on LinkedIn, welliba.ai, or connecting with David or myself, even to reach out to us for having just a free consultation showing you what it would look like for your company, seeing the insights for yourself, how that outside-in perspective might be interesting and meaningful for you and seeing the impact firsthand, like you've seen, David, I think that's an invitation to every listener.
[0:52:37] David Barrett: We probably have your data already, and I'm highly impulsive. I'll respond to anybody who messages me on LinkedIn.
[0:52:43] Katarina Coppé: Yes, and then the final point is, and there's so many different perspectives on the listening group, I'm assuming some of the people have more of a science background, we have written quite a lot of open-science framework articles around how good is this approach compared to traditional listening, and having seen how do we predict eNPSs and engagement scores, like comparing to Gallup, for example. So, again, reaching out to us to find out what type of science do we have to back up the solution is definitely something we're very happy to share with the audience.
[0:53:13] David Green: Well, thank you both for being on the show. I've certainly enjoyed the conversation and I'm sure our listeners will. And it's great, I mean you've really brought what you're doing for a number of organisations in different industries, in different countries, with different challenges that they're trying to solve, really to life. So, thank you very much for being on the show.
[0:53:33] Katarina Coppé: Thank you very much for having us.
[0:53:34] David Barrett: Thank you, David.
[0:53:37] David Green: That brings us to the end of this episode of the Digital HR Leaders podcast. A huge thank you to Katarina and David for joining me today and for sharing such thoughtful insights on how we can better connect data, technology, and human understanding in the workplace. Their work at Welliba highlights just how powerful personalised, real-time insight can be in shaping not just better employee experiences, but stronger organisational outcomes. If you found this conversation valuable, please do subscribe to the podcast, share it with your network, and leave us a rating or review. And don't forget to head over to insight222.com, follow us on LinkedIn, sign up for our weekly newsletter at myHRfuture.com. That's all for now. Thank you for tuning in, and we'll be back next week with another episode of the Digital HR Leaders podcast. Until then, take care and stay well.