HR Skills That Survive AI - Session 2

Kim Burns | humaneer • 00:00

All right. Okay. Okay. Well, hello everyone that has joined us today for this very exciting webinar called the HR Skills That Survive AI. This is something that Helena and I are really passionate about actually, and so we're really grateful to be here to share this with everyone. My name is Kim. I am one of the co-founders of Humanir and Map by Humanir.

Kim Burns | humaneer • 01:05

I am based up just north of the Sunshine Coast, and I'm very excited that Helena is my new neighbor on the sunny coast as well. So. We, um, I work remotely for the team and we run Humanir as a global HR community.

Unknown speaker • 01:22

So if you haven't joined yet, you know, we love to welcome all sort of big umbrella HR into this space.

Kim Burns | humaneer • 01:31

It is just like genuinely a fantastic community of really just authentic and real HR humans from around the world. Thanks to Andrea, who is our community manager, we have been able to run loads of sort of webinars and discussions. We do a lot of in-person meetups and something that we're really, really passionate about is like helping our industry sort of navigate this. I feel like we've been saying new world of work since COVID But, you know, sort of the, this evolution as we come to sort of AI and what that means for us. We also as part of that have built a system called Mac, which is a people intelligence, people risk intelligence platform. And we'll talk a little bit more about that as we go through today. But we basically built this to help take all of the admin and reactive work off of HR and help us manage the people risk within the organizations, because it's one of the biggest risks that we can't see until it comes to our desk.

Kim Burns | humaneer • 02:44

So that is what we do. That is why we're very passionate about AI and HR community.

Kim Burns | humaneer • 02:50

So I'll hand over to Helena to give a little bit of hello, and then we will kick off.

Helena | GoFIGR • 02:58

Awesome. Thanks so much, Kim, and thanks so much everyone for joining. I know I'm connected to a lot of you on LinkedIn, so you probably get the vibe from the content I'm bleating on about at the moment on LinkedIn as to what I do, but I'm one of the co-founders of a career tech platform called Go Figure, amongst other things. And what we're, I guess, more interested in today is the tool that we've recently launched that assesses the impact of AI on your own job, your team's jobs, people in your company's jobs. So we can help you assess what AI is about to do to your own job, the tasks that you perform and the skills you need to develop. So in, towards the end of this presentation, I'll launch a QR code. I don't think you can see my QR code that's near my head somewhere.

Helena | GoFIGR • 03:36

It's probably a bit small, but you can assess your own role for free. And we also offer services to help you assess the impact of AI across your whole organization. So if you have been given the thorny task of working out what that means for your own team, your own work, your workforce planning strategy, your learning strategy, who you will and won't need to recruit for in the next couple of years, this is the kind of thing that you need to know. I'm going to just start with a quick survey, if that's okay, a quick poll. We did this last time, Kim. I forgot to remind you that we were going to do this today. I know, as soon as you said it, I was like, oh yeah.

Helena | GoFIGR • 04:13

So hopefully if I have launched this correctly, I can't see on my, on your, my screen whether this is launched. Okay, has this launched for me? Okay, wonderful. So 2 questions in this poll. Okay, no great big shocks here that many, many people on this call are working in organizations experimenting with AI. This doesn't come as an enormous shock. And second question is, are you clear on the impact AI will have on your own job?

Helena | GoFIGR • 04:48

It's a case of putting your own life mask on before you help others, I guess. Okay. Well, that's, that's slowing down now, I think. But I think it's clear that 95% of people who answered this poll are working in organizations that are experimenting with AI. We're going to go through some kind of archetypes that we see in just a second. And we do have some people that are clear on their role and we'll open up the mic, you know, for questions and maybe they can tell us what they're seeing towards the end of the session. But there is a bit of murkiness, I think, amongst the call.

Helena | GoFIGR • 05:18

So thanks for answering. This survey. I will stop sharing that now. Now, just remind me, Kim, I am still sharing my screen, right? Correct. All right.

Kim Burns | humaneer • 05:29

And if the poll is still on your screen, just go to the little red X and close it out. For some reason it goes down, and then other times it doesn't.

Helena | GoFIGR • 05:39

Awesome. Well, we'll get going then, and we will, we are happy to share this recording at the end, but do feel free to screenshot, take pictures of these slides. Well, maybe I'll open up by asking you here, maybe by show of hands or thumbs up, who here saw the announcement a few weeks ago from Atlassian where Mike Cannon-Brookes decided that it was time to cut 10% of its headcount, where he announced AI is changing the mix of skills we need and the number of roles we need in certain areas. And you probably also read I think it was in March where Jack Dorsey decided to cut 40% of his headcount at Block, which is Block, Afterpay, and a few other companies. And he said something quite interesting. Within the next year, I believe the majority of companies will reach the same conclusion about AI and make similar structural changes. Now, I'm— we're not all naive here.

Helena | GoFIGR • 06:31

I think there's maybe an element in some of these announcements of AI washing, and there's some attempts to maybe counteract some of the overhiring that might have happened in the pandemic. Um, but. Your board read that announcement too, and somewhere between that headline and other headlines making the newspapers and your next board meeting or audit committee meeting or shareholder meeting, there's probably someone in the organization asking a version of, could we be doing this and should we be doing this? And there are heaps of reports out there. I've read most of them on telling us that AI is either going to create loads of jobs or it's going to create humongous job cuts, but it's not very helpful and it's certainly a bit frightening. And so what my team did, whilst acknowledging that humans are inherently terrible at predicting the future, my team and I decided that we wanted something a bit more specific. And we were starting to have questions and conversations with people in our network and customers that we work with, asking about what AI was going to do to the future of jobs.

Helena | GoFIGR • 07:39

So my team and I built a methodology that expands upon a methodology developed by the International Labour Organization. And we enhanced it. I think we sort of chucked it on steroids to quantify AI's impact on jobs. But we decided to do it at the task level. And there's a few reasons for that, which I will explain in a second. And so we believe that organizations are probably more likely to fall into one of three future scenarios. And so we model all three so that you can see the impact of the different decisions you make.

Helena | GoFIGR • 08:13

So there's one scenario which is quite conservative. Maybe your company's doing nothing deliberate with AI. You don't have an enormous and sexy AI strategy. You're not doing big pilots. But there is a catch here because even if you do nothing, there are vendors like me and Kim out there already pumping AI into your tech stack. So it's in the tools that your teams use already. It's in the platforms that your vendors run on.

Helena | GoFIGR • 08:39

So you're being AI impacted or affected whether you're choosing to do it or not. Scenario 2, I think, is where most of our, of us today find ourselves in, and certainly based on the survey we just ran there, is more experimental. So perhaps your organization has an AI roadmap and you're executing against that. So maybe you have some pilots running, you have some automation initiatives, you're maybe a bit further on than Copilot launch, and you're launching AI and productivity tools across multiple functions. So that's scenario 2. And scenario 3 is a bit more transformational. We call it the AI Kraken.

Helena | GoFIGR • 09:18

So what would happen in a world where full agentic agents are deployed, where you've got end-to-end automation, legislation keeps up, we're not scared of AI. So this is a bit more imaginative, let's say. But if we were structurally to redesign work, what would happen? So these are the 3 scenarios that we map in our model. And that's important. And so is this sort of task analysis concept that you're going to hear us talking about, because right now AI is automating tasks, not whole jobs. I'm going to caveat yet, and if AGI or artificial general intelligence is released by OpenAI in the next 2 weeks, I take this all back.

Helena | GoFIGR • 09:56

But what we do is we break down jobs into component tasks, and we do this because technically I'm the CEO of a tech company, but to assume that my job is like the CEO of Google is ridiculous. So because no two jobs are the same, we do task analysis. So we break down every single role into its component tasks, and we then assess how each task is likely to be affected by AI across those three different scenarios. So every task at the moment lands in one of five future states. It might stay with you. So AI really isn't good at this stuff. It's the kind of work that requires human judgment, relationships, nuance, like how you make people feel.

Helena | GoFIGR • 10:38

There are tasks where you lead and AI assists you. So you're in charge. AI makes you faster and better. You know, like the Copilots that help you with your emails and calendar management and so on. The yellow box here, the AI leads and you guide. So this is where AI does the work and you are the human in the loop reviewing the output or the decision before it goes anywhere. There will be tasks that gradually become fully automated.

Helena | GoFIGR • 11:05

We're starting to see that in work now. You'll have read about that in the papers with contact centers and so on and so forth, where the work is fully automated end to end and there isn't a human in the loop anymore. And we're not seeing too much of this yet, but we will start to see this as people really redesign work and jobs where tasks are no longer needed. So the task will disappear. It's not that it's necessarily automated, but AI made that completely redundant. And how work changes around it. So we'll carry on, and I will explain the importance of this in just a second, but I'm going to just pass to Kim now.

Kim Burns | humaneer • 11:41

Excellent. Thank you, Helena. And that gives me— I hope it gives everyone sort of a good understanding of where, where this data has come from. One of the reasons that I love what we're doing here and what we're being able to share is that this is the first thing that I've seen that actually breaks down tasks. It's the first bit of evidence that we have around what is happening to our industry and what is happening to our roles. So huge fan of this, and I'm excited to show you sort of how this works. So this is an HR manager job description we pulled from PwC not that long ago, maybe like just within a couple of months.

Kim Burns | humaneer • 12:20

Excuse me. Oh, hello.

Unknown speaker • 12:27

Nope.

Kim Burns | humaneer • 12:28

Okay, that's fine. So I've lost my train of thought. No, so this is just a, you know, kind of what we would think is a standard HR generalist manager job ad. What we're going to show you on the next slide— I would love the person who's not on mute just to go on mute.

Unknown speaker • 13:05

Am I unmuted? There we go. Here I am. I feel like we're in COVID again. You're on mute. Thank you. Off mute.

Unknown speaker • 13:12

I'm really excited to show you what we found.

Kim Burns | humaneer • 13:15

So using the AI Impact Assessment Tool, what we're going to do is break down these this, this job description into tasks and we'll show you what is going to happen to them. Oh, Helena, you're on mute now.

Unknown speaker • 13:32

When they're all muted, it's tough. Okay. I love that we run tech companies and we're like, you're on mute. Classic.

Helena | GoFIGR • 13:43

You're on mute.

Unknown speaker • 13:44

Okay.

Kim Burns | humaneer • 13:45

So what, what we're seeing here are 18, 18 tasks and we've mapped them against those 3 scenarios that Helena mentioned before, conservative, experimental, and the AI Kraken, the transformative. And now if we start at the top, regardless of which scenario we are looking at, the conservative, experimental, or transform. Building relationships, leading change, advisory, coaching. They stay human across the scenarios. This doesn't surprise me. I wonder if it surprises anyone else here. But I can't replace these conversations or these relationships.

Kim Burns | humaneer • 14:27

If we come down to the middle sort of of the, of the graph, the leading projects doing the documentation process improvement, a human is still leading all of that.

Unknown speaker • 14:42

But if we start to move to the experimental phase, you know, you can start to see where AI takes the lead with the human in the loop, the human reviewing.

Kim Burns | humaneer • 14:54

And if you then go to the whole, we've unleashed the AI Kraken, you can see that in some of these tasks they are completely automated. And the AI is doing the heavy lifting across a lot of the operational and some of the analytical work. And then if we go all the way down to the bottom, the bottom sort of 4 roles in the conservative scenario. So remember the conservative scenario is if we don't do anything, if our organization doesn't do anything, I think AI is still going to be leading in some of these tasks. And if you go to the experimental and to the transformative, you know, they are going to be completely automated. What we can see, and again, this is just one HR manager role and 18 tasks, but there is a consistent pattern across here that the interpersonal leadership strategic, the judgment-heavy tasks will stay human regardless of scenarios. But we're starting to see the analytical, the administrative, the operational, the execution-heavy tasks are progressively going to get handed across to AI.

Kim Burns | humaneer • 16:10

So if we just go to our next slide.

Helena | GoFIGR • 16:13

So there's an easier way to look at this. I know that there was a lot of data in the past slide. The simpler way to think about this is even in the most conservative scenario where your organization doesn't do anything deliberate about AI, you just carry on about your business as normal, 83% of that HR manager's tasks will be AI impacted within 3 years. So the blue stuff is what AI won't touch, which Kim described before. The yellow is what AI transforms, and that's— the human still will be there, but it will— the AI will start to change the way the work is performed. And red is what AI automates or potentially even eradicates. And just remember that Scenario 1, we didn't really do anything, but by the time you get to a more transformational stage, you're looking at a role that might look quite different than it does today.

Helena | GoFIGR • 17:05

Mm-hmm.

Kim Burns | humaneer • 17:07

I think when Helen and I have talked about this before, when we— because we, we like to chat about this stuff, like the, the people that we are, you know, even just putting it down on paper and seeing it from like evidence and data is that even if we're doing nothing, work is still going to change. And I think that really matters a lot for us in our roles and in our industry as HR, because if the tasks are changing, how we add value to the organization's change as well. And so these are the conversations that we're really excited this data gets to kind of highlight. And, you know, it is what does our industry look like in the future? You know, now that we can kind of see through evidence and tasks that, you know, work is changing for us, you know, what does that mean for the human capability? Of our, of our function and where our value now sits. So these are, these are the, the discussions and the conversations that I'm loving having with this data.

Kim Burns | humaneer • 18:14

And it goes really nicely down to sort of that value is, you know, if AI is changing what we're going to be doing in our roles, it's also going to be changing what we need to know and what skills we need to perform those tasks. So this data doesn't just show us, you know, what tasks are going to be automated, but it also shows us what skills we're going to need. And so you can see, you know, through this data here, you know, the skills shift for— we're basing this one here on a HR manager. You know, we can see we need to double down. We need to keep focusing on those human type skills, the engagement, the change management, the coaching. You know, if anything, they're going to become more valuable. And I think we, we're all kind of, I will say vibe, we're all kind of getting that vibe, you know, The humanness of us will become more important as AI continues to kind of impact our, our day and our lives.

Kim Burns | humaneer • 19:17

And so in the middle though, these are the, these are the questions that I think we're asking ourselves is kind of like the, well, what do we do now? What, what happens to, to the HR function and to the skills that we need? So we can start to see some of those skills that are required. So AI output evaluation. So, you know, that's just, that's we're talking about there. You know, how do you know that the AI that you're using isn't hallucinating? And that they're, you know, how do you make sure that, you know, you've reduced the bias as much as possible?

Kim Burns | humaneer • 19:49

The workflow orchestration, and we've had some of these discussions when we've had in-person sessions, you know, how do you, how do you make sure humans and AI agents are doing the things that they need to be doing and who's doing what and who's managing the agents? And if someone leaves, what happens to the agent? And do we need to start performance managing agents? And these are all conversations we, we were lucky enough to have in Brisbane. And, and the one that I'm really excited, and I think we almost need to start with, is around, you know, the strategic people analytics and workforce redesign. You know, what type of person and skills and capabilities do we need for the future tasks.

Unknown speaker • 20:35

And we're also seeing, you know, I feel like I've gone, right, these are all the skills that we need to work on.

Kim Burns | humaneer • 20:41

You know, we already don't have a load of time. So it's almost like, let's look at the skills and the tasks that AI can start to handle and stop spending as much energy there. We're already seeing, you know, reporting, policy, compliance, documentation, you know, getting AI-led, if not automated. So I think that's really important to look at our teams and go, where are we spending a lot of the time? Where can we start to build new skills? Where can we need to double down? Something that's really interesting when I look at this data though is.

Kim Burns | humaneer • 21:22

I don't know about anyone else in the room, but through my HR career, I always have been judged on, you know, sort of the, the outputs that the human skills, that soft skills. And I say it like this because I think we all know that those soft skills are the hardest skills that we need to build and coach in an organization. You know, they're the ones that are actually increasing in value. And the output, the skills that we needed for output such as reporting, policy compliance, you know, that is going to increasingly be taken by AI. So it's more just like our roles are shifting, you know, from the work that we produce to how we think. And so I'm seeing a real shift between what value does HR bring to the organization and how do we continue to grow those skills. And so I think the question that we throw out, and no one needs to have an answer for this, this yet, I don't know if anyone really does, but, you know, how are we going to plan to make the space to develop these new skills?

Kim Burns | humaneer • 22:33

That we're going to need as the workplace continues to evolve. That was a bit of a rant, but it's kind of things that we're learning through, through that. And someone just popped in the comments like those soft skills that they're no longer soft. I hated that they were ever called soft. No one ever had a good option. But yeah, power skills in a world of AI. That was from Anu.

Kim Burns | humaneer • 22:55

So agree, I've rambled, but I think it's just important to flag that and think about. I think it's you because I've just talked loads.

Helena | GoFIGR • 23:09

So that was one role and that was a real role that we assessed. That was just one. So if we zoom out, we, we put 100 HR roles through our AI Impact Assessment. These are jobs that were posted in the last 12 months across people and culture, talent acquisition, organisation design, L&D, people analytics, tech, and reward. We assessed 1,800 tasks in all across all of those 3 scenarios that we talked about to see if it looked similar or different. Now, the pattern holds, and in some cases it intensifies. The proportion of tasks that we predict will stay human remains pretty stable throughout the 3 different scenarios.

Helena | GoFIGR • 23:56

But what does change dramatically, depending at the pace you adopt AI in your company, is the pace at which analytical, administrative, and execution tasks move from human-led to AI-augmented. So, to sort of summarize this, the pace of AI adoption in your organization, or even, I guess, at a personal level, has a massive impact on the work that you and your team do and what that will look like over the next 3 years. So this is the, the 100-job sample size.

Unknown speaker • 24:32

That's a lot.

Kim Burns | humaneer • 24:33

Um, okay, so same data, different conclusions, and we're seeing this play out in the market right now. Some of us will look at this image here and see two faces. Some of it— some of us will look at it and see two bars. No, one bars, I hope. If I'm seeing two bars, I'm— I need to get my eyes tested. Um, but what, what it's saying here is, you know, one set of data can lead to two completely different futures. And what we're seeing right now with some of these larger organizations is, you know, they're looking at this data and saying, great, there's a cost opportunity here for us.

Kim Burns | humaneer • 25:10

We can automate work, reduce headcount, we can be leaner. You know, we're seeing that with Atlassian, we're seeing it with Block. I just saw something on LinkedIn today about 8,500 public sector jobs in New Zealand. We're seeing— yeah, we're seeing it across the board. That AI is being used to reduce headcount. And I think, as you said at the beginning, Helena, you know, how true that is or not is, is up for debate. On the flip side, an organization and leaders can look at this data, the exact same data that we've shown you, and said, wow, what becomes possible for us if our team is no longer drowning in that low-value admin work?

Kim Burns | humaneer • 26:00

What higher-value work, what competitive advantage can we bring to the market if we've got space to do that thinking? And the question for me, which I love, is like, what could HR contribute if we had more headspace and were able to move out of that reactive administrative space that we often think in? It's important because. It's a message that's out right now in the public is, do we love AI? Do we hate— do we love AI? Do we hate AI? Because it's true, AI doesn't like— from what we're seeing right now, it doesn't automatically create a better future for organizations, employees, and HR.

Kim Burns | humaneer • 26:44

It's down to how, you know, organizations interpret that opportunity. And do we, do we strip back our orgs, or do we actually elevate them in different ways. It's not a small difference. It's, it's obviously quite, um, quite big different strategic approaches. But one of the things we're really hoping to do with this information and these tools that we're doing is how do we get HR into the room to help have these conversations and influence which way the organization goes.

Helena | GoFIGR • 27:23

So, to sort of echo that and also to maybe dispel some of the assumptions that all junior jobs are the most susceptible, or there's, you know, just one type of job is the most susceptible to the impact of automation, your exposure to AI disruption or your opportunity to automate, depending on which which way that you choose to look at this, is actually mainly determined by the type of work you do. And so what we also do as part of our assessment is categorise tasks across those 100 roles to work out if there are patterns, and there are some. This is probably worth screenshotting. We're going to issue a white paper as well, so there'll be some, you know, some downloads that you can have if you need access to this information. But on the left here, you can see that the sort of the type of work that will be automated or gone sits in around administrative and routine tasks. So probably not shocking for anyone here, things like documentation, reporting, data entry, scheduling. In a more transformational scenario, this work is effectively gone.

Helena | GoFIGR • 28:40

AI will eventually do it faster, cheaper, and with less errors. So if a significant proportion of your time or your team's time is spent here, pay attention. In the middle here, that yellow column, the transformed column, this sort of is where we're finding that analytical and cognitive type tasks are sitting. So things like workforce planning, compliance monitoring, policy interpretation. AI is going to take a lot more of the execution where we're finding that humans will keep more of the judgment. This is your unique value. The tasks won't disappear, but AI will change your role in that task.

Helena | GoFIGR • 29:21

So your team will start shifting from doing all of the work to increasingly reviewing it and being accountable for what the AI produces. On the right-hand side here, the blue stuff, the stuff that we see and we believe that will continue to stay very human, is interpersonal, leadership, strategic, creative. So things that involve stakeholder relationships, governance, strategic advice, ethical judgment, how you kind of make people feel as well, if that's another way to look at it. AI barely touches this. So as that routine task or routine work becomes automated, this is where you, you and your most experienced people should be spending more of their time, not less of it. So it is worth considering, if you have time, what's the actual distribution of your team's time across these three different types of categories, because that will tell you whether you're exposed or where your investment should go if you are looking to free up headspace to do more of that high-value work. And again, this is the analysis of the skills impact.

Helena | GoFIGR • 30:30

So just as a reminder, this is an analysis of 100 roles that we looked at. Your company's tech stack, your own company's AI roadmap, the exact tasks that you do in your job will shift where you land personally on this. But what we're looking at here is the changes in skills that impact all of these functions. What we're going to show you next is a drill down into those different teams separately. So we're looking at skills that our model predicts that all HR teams need to consider. So on the left here, um, the Double Down column is the skills that are worth protecting— things that involve influence, relationship, knowledge of your business, and your ability to solve problems. These become your really defensible capabilities.

Helena | GoFIGR • 31:16

In the middle, the stuff that you probably need to start building if you haven't thought about it already. So collaborating with an AI, which is quite a skill. I'm going to be writing some more content on that now as our company goes through its own journey. Knowing how to work alongside AI tools effectively and not take up more time on tasks than you should, which we sometimes find ourselves in at Go Figure. So not just using tools to write better emails, evaluating the output of AI models, being able to review what it produces, make judgment call on it, understanding legislation around AI. These are the kind of things that you need to be aware of because only humans are going to be able to catch some of those nuances. And your broader ability, as we said before, about how to orchestrate an army of AI and design systems that still have humans at the centre.

Helena | GoFIGR • 32:07

This is— we're talking like sophisticated workforce redesign here. And we put change leadership kind of in its own category here because one has to bring the rest of the organisation through it. You are being disrupted in your own job, your team is being disrupted potentially or changed, and you're also expected to support every other function through, through this. So it's not just a new skill,. It's a new skill for everyone, and the stakes just got a bit higher for you in HR as well. And then in the bottom here, the purple, the transformative, the governing AI. So, the skills that sit in that transformational column.

Helena | GoFIGR • 32:44

So, if you are more in the AI-native type of company, so you're going to be asked to govern AI, make ethical decisions about AI. And these aren't the kind of skills that you just pick up in an afternoon workshop. They really mean being accountable for what AI produces on your watch or your company's watch. So understanding, you need to understand this black box well enough to know when something's gone wrong, how to unpick it, what to do, how to correct it. And that person doesn't really exist in most companies yet. Someone's gonna need to either develop these skills or bring them into the organization somehow. And then on the right-hand side, I guess we've talked about this already, are some of the skills that increasingly, you can let AI handle.

Helena | GoFIGR • 33:28

Kim, is there anything— when we first went through this together, I know we've done this a few times before— is there anything here that surprised you?

Kim Burns | humaneer • 33:37

I don't think anything really surprised me. It validated the shift that I was already seeing within like sort of that the HR industry and the people that we talk to. I think again, it gives us a really clear roadmap. It is, and again, these were when the, the screen that we showed you before was about the HR manager skills that you needed to double down, develop new, and let AI handle. And I think having done the analysis over 100 different roles within HR, and these are like big umbrella HR roles, so anything from a generalist HR to comp and benz to talent, you know, we are, we are seeing a pattern now, and we. Never have enough time to lift ourselves up out of the day-to-day work being in HR. So I am really excited by this to say we actually have a bit of a roadmap now.

Kim Burns | humaneer • 34:39

But at the same time, I'm like, oh my God, how the hell am I going to learn all these new skills and make all this work and do all this? So I think one of the biggest takeaways for me and to our community and HR is that, you know, it's, it's okay. Like, no one's got this all figured out. I think we're one of the first industries that I've seen that actually has data to help us along the, along the path, and that we can start with this data. And again, I will say this is only really high-level data. Imagine what we can do if we're like in your organizations or spend more time with you. You know, it's— this is where we can start to be really intentional and not try and do everything at once.

Kim Burns | humaneer • 35:23

But we can just, you know, start building capability like one by one. So that, that's kind of like my things that come, come around it. The other big thing that I don't know where we talk about it, I might be jumping ahead, but you know, all these develop new skills. I reckon if you put those into Google or whatever to try and look at training for this,. Like if you can find some, that would be great, let me know. But they, they don't necessarily exist and they're not a one-day course that we're going to be able to take. So I think it's really important that we understand that developing these new skills is a bit of a journey as well.

Kim Burns | humaneer • 36:09

That's it.

Helena | GoFIGR • 36:12

So I'm going to move on now and back to you, Kym, if that's okay.

Kim Burns | humaneer • 36:15

Yeah, yeah, I mean, I'm loving talking today. I think I've been on my own for a while. So this is, this is the skill. And again, like, please, we will, we will share these, but feel free to take a photo. We've kind of gone HR manager, 100 skills. And now we're breaking it down to some of the functions within HR. Just to show you in a little bit more, like a little bit more of a nuanced layer.

Kim Burns | humaneer • 36:41

About some of the skills in these functions of what you should double down on, what you did, what you should develop new. I think for me, when I look at these, there's a couple that really stand out as really. Welcoming and interesting skills. And I think it's the OD and culture again, you know, it's— we, we know it's about facilitation, conflict. Like, these are the, these are the human skills that will, will continue to stay. But, you know, the designing for human agent teams, strategic foresight, like. It's exciting if we've got the space to be able to do that.

Kim Burns | humaneer • 37:20

And I think if we look at learning and development, it's the human-centered design, it's the storytelling. Um, reward and comp is another one. You know, when we start to get more of these systems into our, like, organization— organizational ecosystem, like, how do we, how do we orchestrate them all to work together? How do we reward humans, the agents? Like, there's just, there's so much that is going to come out of this. It's just, it's, it's very interesting, scary, exciting, all of it. And I think it leads really nicely though to the question.

Kim Burns | humaneer • 38:02

And, you know, feel free, I'd love to get some of your insights in the chat as well. But You know, how do we, how do we do this? I guess is kind of the question on what we're, you know, I'm really keen to know. So yeah, please feel free to pop it in the chat. You know, what's gonna stop us from building these skills? Like we've, we know change is coming. We know work is evolving.

Kim Burns | humaneer • 38:28

What's going to get us— what is going to get in the way for us to do this? Because the conversations that I'm having, it's not about resistance to this. It's not about lack of interest, but it's about the capacity to do this. It's— we're drowning in BAU. There's— we're so overwhelmed because we're just getting— if your algorithms are anything like mine, you know, you're just getting slammed every day with AI this, prompt this, do this, new tool, flash, flash, flash. And we're also, we've spoken about it, we're also trying to support our organization's adoption and change with AI while also trying to focus on our own roles and our own teams. So yeah, any, we've got some people doing in the chat, so I might just raise some of the things.

Kim Burns | humaneer • 39:22

I think, yeah, it's like the volume, as I said, it's the volume of AI tech and, and which one to use. And it's the other one that Pip said as well. It's about the company, the company. And Helen has mentioned that as we've gone through this session, you know, the AI change and the impact on your role is going to be largely driven about your organizational readiness as well. So.

Unknown speaker • 39:49

One of the big things, because we don't like just to solve, you know, tell you there's all these problems and things to do.

Kim Burns | humaneer • 39:58

But, you know, we know that if we can get out of documentation, note-taking, process, admin, you know, the fun— the stuff that we need in the function but is kind of time-draining, we're going to have more space to be able to grow into these areas. So if we don't have the headspace,. Nothing's really going to change, or it's going to change at a really slow pace. So really encourage you to look at— don't start looking at redesigning your whole function. We always talk about, you know, choosing a problem or a pain point that you have and looking at how you can potentially automate that away. That is genuinely one of the reasons that we built Mac by Humanir was to reduce all of that administrative work that's on HR's plate, because we could see that all of this was coming along. And there'd never been a tool out there that kind of looked at how HR do their day-to-day work.

Kim Burns | humaneer • 41:07

So I do encourage that as a great, you know, first starting point to look at. Um, because that might be able to help you really quickly to reduce some of that admin and help you with some of that automation. Next slide. I don't know what's next.

Helena | GoFIGR • 41:26

Kim, I'm going to skip the next one and go straight to the end so we can get the questions flowing in, if you don't mind. Right. Go. So, sorry, I'm going to skip this one. We are going to— I'll stop screen sharing in just a second. We got submitted a bunch of questions before this webinar. We're going to try and tackle the ones that we can tackle in the 15 minutes we have left.

Helena | GoFIGR • 41:48

And I'm sure— I haven't been looking at the chat, but I'm sure loads have come up on the chat that we, we should tackle here, and you are welcome at this stage to unmute and join in the conversation. But we wanted to just quickly, while we've got your attention, just draw your attention to the two QR codes here. You heard us talk about the AI Impact Assessment. If you, on the left here, the purple QR code. Scan that one. Hopefully you can scan that with your phone. If you would like to see how AI will impact your own job, you can try our free impact assessment by Go Figure there.

Helena | GoFIGR • 42:21

It will take you about 2 or 3 minutes. I would love you to share this with your colleagues. I've got workshop templates if you want to do something fun with your team to get the conversation going. I guarantee you will have a really interesting, fun conversation if you do this. Sit with your team and do it and open up the chat about what you can be doing with AI and how AI might change your job. So feel free to scan that. That one's completely free, takes 2 or 3 minutes.

Helena | GoFIGR • 42:44

And Kim, your QR code.

Unknown speaker • 42:47

Yeah, so we've got a QR code though there because as we talked about, it's not just we've got this information, now off you go.

Kim Burns | humaneer • 42:57

It's how do we start to build those skills? How do we build them within ourselves, within our teams? And so we're putting on workshops and we're going on a roadshow around the country where we want to spend time with individual HR and also HR teams about how do we, you know, actually building those skills. So not how do we build those skills, but we want to spend time with people that are in this space to say this is how we can build the AI orchestration, the bias thing, the judgment layer. So that is just to register so that we can contact you when we're coming to your city with some more info.

Helena | GoFIGR • 43:38

Now I haven't been looking at the chat, but what's come through that we need to be picking up? Hi there.

Andrea Kirby • 43:45

So I've been doing this. So there was one from a hitch because the names aren't here as full names. So this person has said we're talking about the shift to these new types of skills and the heavier people skills and things which creates a stronger cognitive load. Therefore leads to potential burnout because we're not doing some of the more simple tasks. So how do we, how do we look at that for HR if we're just focusing on these larger skill sets in our roles?

Kim Burns | humaneer • 44:26

I'm happy to jump in and just give some of my thoughts around that one, um, because I think that's an issue now. Um, I think that burnout and, um, the, the emotional toll HR takes on HR professionals is already a, an issue. Um, and within the human community, Andrea We haven't talked about this, but there's definitely things that we can do to, to continue to raise awareness for that. The data here is showing that the skills that we're currently doing are things that we need to continue to do. But as this work evolves, I think it's going to be about not us just shouldering all of that on our own, and it's how do we upskill if we've got more capability. The way I look at it is if we've got more capability because we're not drowning inside kind of this reactive admin work, we've got more capacity to coach managers and others in the organizations to kind of have those frontline conversations. But I think, yeah, it's an issue that we're already facing now, how we handle that cognitive load.

Kim Burns | humaneer • 45:39

So I'm not sure if we're going to see an increase or. What, but it's something we need to do with, deal with now too.

Unknown speaker • 45:47

I can't help but feel if you lose the mundane, there is something about just feeling more excited about your role, but I was never one for admin. So Paul D has asked, with that automation scenario, especially the data entry and systems admin, do you have any data showing that companies are shifting away from outsourcing this work to other countries such as the Philippines or Vietnam and how it will now move to AI instead.

Helena | GoFIGR • 46:18

I think that's starting to happen. I think there is— we haven't done an analysis of this here, but you see World Economic Forum data that suggests there are definitely occupations, occupation groups, and countries where I think where work is more susceptible. And I'm also going to call out that young people are more susceptible. I know I said earlier that it depends on the tasks that you're doing, but again, CEOs are making decisions on vibes. What they believe the technology can do, not necessarily what the technology can do, not necessarily what their technology in their company can do. And it's already clear from the data that the entry-level and grad roles are on the decline, and women occupy a lot of roles that are the most susceptible to automation. So, um, yeah, I can see this vibes-based decision-making starting to happen.

Helena | GoFIGR • 47:12

And I think this is kind of why we said it earlier, is that many of you in this room on this call, uh, have the opportunity to— we still have a choice, right? We still, we still, we're not sort of passive participants in AI just stealing all of our jobs. Many of you have seats at the strategic table and should be thinking about what role you want to play in, um, the responsible deployment of AI. But yeah, it, it is definitely starting to happen.

Kim Burns | humaneer • 47:37

I think, Helena, just to, to go on there, I think you talked about the vibes-based decision making. I think there's a massive fear of missing out vibe going on as well when it comes to CEOs and organizations, you know, feeling like you're the only company or the only one that's not just rolled out AI to your organization. So we know that decisions based on vibes never tend to work out really well for organizations.

Paul D • 48:07

Thanks for answering that question. I think that when you asked about barriers as well, if people are making those vibe-based decisions, I think also that another barrier would be that there's not a lot of data out there regarding the return of investment on AI, and everyone's implementing it, but some companies are like, oh shit, we've got to backtrack now because it's actually not giving us the return that we thought it would. So I think that would be a massive barrier. There's no real big case studies in large businesses to say you will actually save this much money if you cut this many people. Um, so yeah, I think it's, it's still in the exploratory phase for a lot of businesses as well.

Helena | GoFIGR • 48:43

But you know as well, it's not like you don't just launch Copilot and get 5 minutes of incremental gains from everyone and be like, we can now get 5 minutes of salary back from everyone. Of course it doesn't work like that. So the, the reason, um, there was this awesome podcast, right, that I listened to the other way— this is not my original thinking, But let— I run a tech team, or I have a tech team. I don't run it, my co-founder does. Let's be really clear on that. If we can develop software 10 times faster, but the whole system isn't improved, it's no good me having software developers that develop 10 times faster. Because if I can't release software 10 times faster, we've just locked that productivity gain up in a box, right?

Helena | GoFIGR • 49:22

It's, it's of no consequential gain to my company. So you have to look at the whole system. So, I mean, we're going through this ourselves. It's like, how do we work together with agents? How do I let my ego go when my co-founder built a marketing agent when I was away for 3 weeks? Like, how do I, how do How do I let that, um, not be threatened by that and embrace it? How do we stop letting Claude, like, constantly tell us that we're not finished?

Helena | GoFIGR • 49:48

You can keep improving, you can keep improving when the task never ends, but you, you can't just look at a teeny tiny task in isolation. You have to start there, I really agree with that, but you have to then think of your tasks, your job, your team, your company. It's, it's pretty difficult and pretty big, and you won't see gains. All you— in fact, all you'll see is, is, is cost pull. If you're, if you're kind of like a Microsoft Copilot expecting gazillions of dollars of return on investment because everyone can do emails faster, you, you're going to see the opposite. But I do love it. I love all these case studies, and I love seeing that companies are having to rehire people they fired as well because they got it wrong.

Unknown speaker • 50:23

Well, Clay's a great— Clay's a great example of that, who, what, like 12 months ago, 6 months ago, fired their whole customer service team and replaced it with AI.

Kim Burns | humaneer • 50:33

And then about 6 months after that rehired them all because they realized that the tools couldn't do exactly as Helena said, you know, the tools couldn't do what they thought it could do, and it just caused a massive, massive disruption to their organization. So we've actually seen examples of organizations that have made those. Organizational decisions and then had to reverse on them.

Helena | GoFIGR • 50:58

But it's so seductive. You get a lovely share price bump. So seductive.

Paul D • 51:02

Yeah, there was one company that just changed their name to AI in the title, and when they released, they were just making shoes or something before. But they changed their name to AI in the title. Yeah, bonkers. Insane.

Unknown speaker • 51:15

Can I also just add to the discussion around tools and things?

Andrea Kirby • 51:19

Because Simone, who's still online here, said, you know, there are the better AI tools out here, but one of the challenges is the org tools.

Helena | GoFIGR • 51:28

Yep.

Andrea Kirby • 51:28

So MS Copilot rely heavily on attaching bunches of files, and you have to download from 6 systems to make it work. So until we can analyze across all those 6 systems, um, you know, it's very manual to collate everything.

Unknown speaker • 51:48

So she was keen to hear what other large orgs are doing with those restrictions.

Helena | GoFIGR • 51:53

I think they're actually, uh, I think the first thing is recognition that there is a restriction and that you've got, you've got all these beautiful expensive tools that are great at email generation but useless. And I'm starting to see now, I do a roughly, I don't do it every fortnight because I don't always have time, but I do keep an eye on Futuristic jobs that are happening in the market. I am seeing AI enablement as a new capability, so I'm seeing I don't think— I think so. I'm not sure any of my friends on this call are here, but I am starting to see, uh, HR teams grab this, so they are, um. Upskilling people in their team to work alongside IT and actually enable people to use these tools more effectively. So I was with a company doing a workshop last week who made it one of their concrete actions to understand exactly what systems, tools, and documents their Copilot had access to and didn't have access to so that they could make sure this tool actually added benefit. But I think one of the hottest new capabilities you're going to see is AI enablement.

Helena | GoFIGR • 52:59

And I think if I were in HR now, I would be trying to grab this because it's interesting, it's important, and it's, I think, yeah, strategically relevant.

Kim Burns | humaneer • 53:10

Well, we've already seen, I think, was it Atlassian or Culture Amp or someone wrapped up this? Yeah, wrapped up there. They're now like Chief People Officer plus AI Enablement Officer.

Helena | GoFIGR • 53:23

Well, if you, you are now talking about digital workers, right? So is it, it's kind of like HR and IT territory. You have to kind of like get married and you've had a little digital baby together and suddenly this baby is what you're rearing. So I think you're going to see a lot more unions of HR and IT teams in some way, shape, or form, like it or not.

Kim Burns | humaneer • 53:45

Well, it's also, it's also not HR and IT that have babies. It's HR, IT, and the employees that build their own agents. Like grandchildren, babies. And I remember in our Brisbane session when we ran this, we had a couple of questions around, you know, what happens when someone's built an agent that's doing a critical piece of work, but then they leave the organization. And everything shuts down. And that was like a real scenario that, that people were kind of, some of our HR people in that room were going through and how if you've got loads of agents connected, you know, if there's a little problem over in this agent who's responsible for finding out that there's that problem and the flow-on effect. So there's so much when it comes to managing our I really like digital babies.

Kim Burns | humaneer • 54:38

I'm gonna use that a lot.

Andrea Kirby • 54:43

And Adrian has just put in here, like, Claude AI is something that they're looking into in the people data space. It has a lot better systems integration. And I know in Humanir, Corinne's sending us down this path as well. So we're having lots of fun with Claude and all our systems and things. And checking up on my posting of the wrong marketing material.

Kim Burns | humaneer • 55:08

Yeah, I would say I don't— I would say when it comes to these general large language models like Claude and ChatGPT and Grok and all of these, there is still. You know, we use these tools day, like every day, all the time, but there still needs to be that human in, in the loop. I think it would be very irresponsible to, to trust these tools without double-checking. Helena, I don't know if you have the same experience with the tools that you're using.

Helena | GoFIGR • 55:44

We do. And I, I gave this presentation, an equivalent of this presentation, to a group of Chief Risk and Audit Officers the other day, and I was It was amazing. We did the same analysis, 100 risk assurance and audit jobs, and they are in the same stage that we all are in, which is, oh my God, how does it affect my job? Oh my God, how does it affect my team's job? And still getting to grips with, oh my God, what, how do we audit and assure this across the whole organisation? So don't be thinking they've got the house in order completely. But sorry, I interrupted you, Kim.

Kim Burns | humaneer • 56:15

No, no, that, that I'm glad because it's what we're seeing and it's also nice, I think, for us in HR to. Know that we're not the only function or industry going through this uncertainty and disruption and unknowingness, if that's a word.

Andrea Kirby • 56:33

Yeah. Now, Renee has actually talked about wondering whether AI will prolong a task, i.e., going down an AI rabbit hole.

Helena | GoFIGR • 56:43

We call it the tumble dryer in our company. We call it the rabbit hole.

Andrea Kirby • 56:48

You never get out of it. Yeah. So rather than simplifying it or make it faster, you know, you, you're just going down this, this black hole and that you could have used AI for a specific task until it's so far down the track and you know, it's all too late. I did go onto the chat, Renee, and say you really have to look at Mac for this. And I know that I'm the community manager for HumanEar and Kim and Corinne are building it, but I've used it myself and It just takes away so much thinking and stopping the rabbit hole. So definitely take a look at it. But what do you think?

Andrea Kirby • 57:32

Are we heading down a black hole?

Helena | GoFIGR • 57:36

I mean, I'm guilty of it, but the model's job is to keep you engaged. So its job is to tell you how you can keep improving and never let you finish. And so if you listen to an AI blindly and don't use your critical thinking skills, you will never finish that task because it will never let you.

Unknown speaker • 57:54

So yeah, I literally noticed it. I don't, I don't feel— I don't know about anyone else, but I feel 6 months ago it wasn't as— it wasn't like this.

Kim Burns | humaneer • 58:05

Like, I noticed a real shift fairly recently when I was using ChatGPT. It was like, I can do this for you next. I will do this. Let me show you how to make that hook 3 times more interesting. And I'm like, make it the right one the first time. Like, I don't want to spend 20 minutes. We always in human ears say, you know, if you spent more than like 5 goes iterating something on AI, you've lost your productivity gain.

Kim Burns | humaneer • 58:31

Um, but I mean, I'm guilty of going down rabbit holes all the time. Um, and it also makes me feel really good because it tells me how good I am to ask these type of questions and that I'm amazing and I should be asking these questions. So it's that weird dopamine. Like, hit—.

Helena | GoFIGR • 58:55

They want those tokens out of you. You know, it's how they make their money. You got, got to think. I feel great.

Andrea Kirby • 59:00

You know what, that's eye-opening. They want those tokens out of you because they get that you're actually, you know, paying for some of it. But also, um, Paul has made a good point that, you know, maybe critical thinking is disappearing because of AI, and maybe it is a skill we need to double down on.

Helena | GoFIGR • 59:18

I agree.

Paul D • 59:19

Yeah, we moved away from having to Google everything to now go to AI for everything, so people don't even think because memory is embedded through repetition, and when we don't actually study it, we lose it. So that's another thing society— I would say that, uh, that's an interesting piece because we don't actually learn anything if we just ask it all the time. Yeah.

Andrea Kirby • 59:41

Um, and just, is there anyone else on the call that would, uh, like to come on and ask a question or make a comment? Please do so now.

Olga Rankin • 59:52

Don't want to ask a question, um, or make a com— oh, I want to make a comment about something. Um, I think what you, you were saying, Kim, and what you were saying, Paul, is right, that we do, we do lose an element of of critical thinking, but we need that critical thinking to write the correct prompt so we don't have to iterate 5 times. So we need to train ourselves and coach— get— having— take the time and effort to learn how to prompt correctly. And when we prompt correctly, we're not going to be iterating 5 times. We might iterate twice to. Tighten up what we've done. But once we write that correct prompt and take the effort to do it, which might take half an hour, then we're going to have that prompt forever.

Olga Rankin • 01:00:37

And that prompt is going to be something that we can then build on to build that next level down and next level down. I— this just my—.

Kim Burns | humaneer • 01:00:44

No, I think, I think that's spot on. I, I tend to think the way I view AI usage at the moment is you need to know what you want out of it. You need to know what good looks like in able to get to that output, because if we just took the first thing that AI produced for us. Most of the time it's pretty, pretty lackluster. Like, we need to know what a good policy looks like. We need to know what, um, what else I would put in there kind of looks like to know, is this right, is this wrong?

Olga Rankin • 01:01:22

And that's my concern with people coming in in entry-level roles.

Helena | GoFIGR • 01:01:25

I was going to say that, Olga. Yeah, exactly.

Olga Rankin • 01:01:27

If they're coming in in entry-level roles and those things that we did at the beginning of our careers to learn what good looks like, uh, isn't there anymore. How are we going to educate that? And I, I was talking about it with someone else this morning, we kind of need a sandwich course that sort of teaches people that little bit that might have taken them 3 or 4 years to learn previously, so that when they step in, they're not stepping in to fail, they're stepping in with a level of, of knowledge. And that might mean longer onboarding for more entry-level people. To ensure that they have some skills. Because I don't know about the rest of you, but I learnt from a bit like what Paul was saying before, making mistakes and doing it over and over again. So I worry that people aren't going to be doing that gritty grind sort of work, and where will they get that knowledge to know where good is or what good is?

Kim Burns | humaneer • 01:02:27

Yeah, I think we can all just— a lot of people have agreed with you in this, and I think that's where it comes to HR. I mean, I feel like everything comes to HR, but, you know, as we're designing our early graduate career programs and our onboarding, you know, it looks different. I think, I think you're right. It looks different now.

Helena | GoFIGR • 01:02:51

There's a saying I read about the other day. It's don't eat your seed corn. Yeah, you told me that. Yeah, I love that. It's like, it feels efficient until we've got no blooming pipeline and we're all dumb in like 2 years or something. So yeah, yeah, or maybe, or maybe erase that bit from the transcript. But yeah, it does worry me.

Helena | GoFIGR • 01:03:11

Yeah. All right, we didn't get to cover every single question. We have written them down and we will take the chats and we will probably produce a piece of content tackling the questions that we didn't get to cover, but we will wrap it here. Thanks so much for staying on a little bit extra. Thank you. So nice to see some familiar faces. Thanks so much for joining.

Helena | GoFIGR • 01:03:34

We will distribute the notes, the chats, the, the recording, and so on.

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