Unleash Dynamic Strategic Workforce Planning

Transcription:

[0:00:00] John Lynch: Great seeing some new faces that I recognize. Welcome to today's session. Our focus today is on strategic workforce planning. So, we're going to tackle some of the critical questions that business leaders are asking of you all, specifically of HR teams.

Fundamentally, understanding and answering questions like: how do our talent skills compare to competitors? What is the future of automation? How will that impact our workforce? What are the capabilities that we need to deliver on our business strategy?

In today's session, we'll demonstrate exactly how to answer those questions using the data and capabilities available at TalentNeuron. I'll cover a few housekeeping items. Today's session will run for approximately forty-five minutes. We'll have approximately thirty minutes of presentation time, including some demos. We might run over a little bit, and then we'll leave about fifteen minutes for Q&A. And if we run past the allotted forty-five minutes, then I think we will probably be able to hang on for a couple of minutes more. But if not, we will follow up with any answers to any questions that we don't get to.

We have a brief survey at the end, and this is really valuable for us to gather feedback on your experience and understand how we can better serve you with these webinars and in the future. So, if you could take a moment to fill that out, that would be fantastic.

Before we begin, let me quickly walk you through the webinar environment and explain how to engage with us during today's session. Pay attention to the column on the right. That's the blue box there with a chat box and Q&A. For questions and comments during the webinar, you can submit them using the Q&A feature. We'll get to those at the end. Ask questions as they arise, and if we have a chance to stop and address them, we'll deal with them as we go.

Under the "Doc" section, you can find resources where you can download today's presentation, along with additional materials on strategic workforce planning and some of our capabilities that we'll be spotlight lighting today.

• Maturity Assessment

• How to Strategize Around Future-Oriented Skills
  

We will be hosting a live demo. During this session, if you need to expand your screen, you can see a small square box in the bottom right-hand corner. This will help you view more detail in the demo section.

If you would like to learn more about how TalentNeuron can support your strategic workforce planning needs, simply click the 'Request a Demo' button at the top of your screen, and our team will be happy to connect with you after the event.

Alright. Time for introductions. So, Christian, nice to see you. For people who haven't met you or don't know you, would you like to give yourself a quick intro?

[0:03:18] Christian Vetter: Sure. Hello, everybody. I'm Chris, dialing in from Munich, Germany. I've been in the SWP space for approximately fifteen years. For the last eleven years, I have spent my time building strategic workforce planning solutions and eventually founded a company called HR Forecast, a Germany-based company. Since October of last year, it has joined the TalentNeuron family and is now part of TalentNeuron. I'm building out that offering, and I'm very happy to show you today how the product looks and feels and explore some of its functionalities with you.

[0:04:00] John Lynch: Cool. Thank you, Chris. So, my name is John Lynch. I lead content and communications at TalentNeuron. It's great to see everybody. I am dialing in from Boston, Massachusetts, and thankfully, it's not 108 degrees as it was yesterday. So I'm able to do this webinar without melting in this room.

So, to get started, I think we wanted to do a little kind of backgrounding. Honestly, if you, like me, started your career in a room like this — the office space years — in a traditional cubicle environment, you'll appreciate just how dramatically things have changed, not over the last twenty years but even over the last five years and ten years.

The new work environment has undergone a significant transformation due to several factors. At a glance, it is clear that the change for professional workers has been huge and significant, in part due to the work-from-home culture of the COVID-19 years, as well as several other factors. And the result of that has been huge.

Knowledge workers, such as those in tech, media, and professional services, have an average tenure of one and a half years, down from two and a half years globally. According to OECD, within OECD countries, up to thirty-five percent of workers now work remotely at least part of the time.

Therefore, the work environment and employee engagement levels have shifted. In addition to those changes, over the last five to ten years, we've experienced ongoing impacts from factors such as demographic shifts, economic instability, and the yet-to-be-fully-realized effects of AI and automation on specific roles.

So, work has changed. What we're here to talk about today is how that has impacted workforce management and has it.

[0:06:05] Christian Vetter: Yeah. Let's have a look at that. Before we see it, let's examine the changes and consider how workforces have generally been managed in the past, at least over the last ten or twenty years. I think the people analytics practice has been perfecting these elements, as you can see here. I think it's a very fair summary of key elements and important data points, which are probably necessary. And if you look at them in their entirety, you can see that these covers basically encompass the full employee life cycle. They're very HR-focused and workforce-focused. The key question now is, how many of these insights does the CEO, the board of directors, or the shareholders care about? And let's explore that. We believe it all comes down to one very important question: will we, as an organization, have the skills in place to deliver on our business strategy?

From a practitioner and SWP point of view, I would say this is a very good way of saying, do we have a strategic workforce plan in place that allows us to drive value from that? Or, in other words, do we have the right people at the right costs, with the right skills, in the right roles, in the right locations, to achieve those business objectives?

Sounds like a very simple question, but how many of you, how many of us can actually answer that question?

[0:07:49] John Lynch: So, yeah, what is the knock-on impact, the day-to-day reality of the HR folks? If this is a North Star question, what's the consequence?

[0:08:00] Christian Vetter: Yeah. Let's examine how the SWP space adds an extra layer on top of the employee life cycle layer we previously explored. The difference between these questions and components lies in their focus, which is not so much on analyzing from an HR perspective, but rather from a contextual perspective, with corporate strategy and business objectives closely involved. We also refer to this as the strategic HR value delivery components, whereas the others we've seen before are also very necessary but are more transactional or foundational value drivers.

So, what does it take to answer these questions, and what are some of the elements I really want to highlight for today's meeting? We're not going to run through all 12 of them, but let me pick out a few. I'm picking these because they are currently the ones most frequently being asked or where the most movement is going on.

You see those highlighted ones around job and skill architecture here. That's a big topic. Companies need to know: Do we have the right portfolio of skills and roles in our company? Are we planning for the right roles? Combining that with external data insights brings in context — clean context from market intelligence — to understand whether the company we're managing is moving in the right direction. Are we facing any white spots or gaps in terms of our role portfolio compared to the market?

Looking at the next one on automation assessments, it's a huge question right now. How exactly does automation kick in? We are familiar with the World Economic Forum's statistics, such as the prediction that 25% of today's jobs will disappear within the next five years. Thousands of new jobs will be created, but the real question is: what does it mean for me? What does it mean for the roles I care about and plan for in my industry? These are questions we need to answer with data nowadays.

Looking at the next one — strategy needs and talent needs — and possibly combining that with the gap analysis. The question is: do we have the capabilities to plan and understand future business scenarios? Nobody knows what that scenario will be because the surrounding environment is constantly changing. We need to be prepared for multiple scenarios. The question is: can we quantify those scenarios and understand how they impact risk, demand, and the gaps we'll face? And most importantly, how do they impact the demand within my company?

Last but not least, looking at the B bubble — the build, buy, borrow, bot, break, etc. — the question is: Do we have the capabilities to quantify the transformation and understand the costs associated with each of these Bs? Is there a positive ROI? Is it feasible for my workforce to move into that future setup? What are the right measures I need to operationalize in my organization?

Now, those are a kind of "best of" collection of the questions we're being asked. If you look at the next one, we can have a glimpse at the kind of data we need to answer these questions. Two things come to my attention right away.

Number one: Many external contextual data points are required to answer these questions. We've talked about them a bit before: job developments, skills developments, competitor movements in the space, and emerging roles we need to prepare for.

And then there are numerous questions surrounding skills. You can see these in the middle column: skills extraction, skills efficiency, skills proficiency, and skills evolution. That's important nowadays because skills essentially measure your people. What is their market value? What can they do behind the title they have? It's like the currency of today's businesses, and that's why it's so important to quantify it. But on the other hand, so many companies struggle to do exactly that.

So, John, moving ahead, why don't you tell us a little bit about how the companies we work with try to combine all these elements?

[0:12:36] John Lynch: Yes. For sure. I love that slide because it's like an enormous laundry list. And then I think this is the simplified view because when we're working with clients, or in discussions with anyone interested in starting their SWP journey, or anyone serious about identifying the data points they need right now, they're using multiple vendors. They're using many vendors and consultancies, and they have to put in a lot of effort just to accomplish the most basic workforce planning tasks. So it's like solving a puzzle. Yes. It's a puzzle when none of the pieces fit together. It's incredibly challenging for clients to find strategic value from their investments.

When we ask clients what tools they spend most of their time using for strategic workforce planning, the answer is likely to be Excel. The reality is that it's the default tool for SWP in many instances. And, frankly, I think everyone kind of hates using it for that purpose. So TalentNeuron's focus has been on integrating all of this critical data within a single view. We want a unified perspective, which allows HR teams to focus on the strategic aspects of SWP rather than spending all that time connecting data across independent spreadsheets and carrying out line-by-line taxonomy reconciliation, among other tasks you've probably been doing for the last twenty years, Christian.

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So, the question we are helping to answer is: Will we have the skills and capabilities to deliver on our business strategy? There is a path forward, and that path forward is always some kind of workforce transformation. The ability to identify, develop, and acquire those skills and capabilities. TalentNeuron provides the components you need to transform your workforce for the future of work and to help answer the big questions. And whether you have some of those pieces in place or you're starting from scratch, this is our capability set. So we do want to spend a little time today showing you what that capability set is. Before that, Christine will get a bit more into detail.

[0:14:55] Christian Vetter: Yeah. Before we dive into the flashy demo, let's take a look at one more slide. I'd like to share with you a little bit about how we at TalentNeuron define the category of workforce transformation. As you can see from the visual here, it's not a single-piece exercise or a single event, but rather a structured process. As you can see, there are three phases: the understanding phase, the plan phase, and the transformation phase. Let me explore these phases in theory here, and then we'll examine how they appear in the tool.

So, in terms of the understanding piece, that's really where it's about understanding the complete talent landscape. That means understanding internal talent — what are the skills I currently have in my organization — but also understanding external talent in terms of how the labor market is evolving, what it costs to hire a specific skill set, and where I can find that skill. Factors such as availability and competitiveness play a significant role here. Additionally, what can I learn from external developments to better prepare myself for emerging skill trends?

To put it into a practical example: let's say, in a classical case, an automotive OEM in the understanding phase would say, "We currently produce mostly combustion engine cars. What are the roles and skills we need to become a global leader in autonomous driving, electric cars, etc.?" These are exactly the data points that need to be brought together so companies can explore them.

Moving to the next phase, the plan phase — and staying with this automotive example — once the company understands the roles and skills they need to be prepared for, the question we answer here is: how many of these roles and skills do you need, and how big is the gap between that and your current skills landscape in the existing workforce? So here it's really about exploring future business scenarios. I mean, that company moving into the new space will likely not have just one scenario but multiple ones. It could be a scenario of rapid advancement, where automation takes a significant role and technological advancements are incorporated quickly. But, it could also be a scenario where global tariff struggles lead to the automotive industry developing much more slowly and experiencing a significant decline in business.

This is the part where we quantify the business drivers and translate them — like a translation tool from one language to another — into FTE requirements. In the end, we quantify the workforce gap in whatever scenario the company might face.

Then, the more important element is the transformation element, where it's about identifying these gaps and understanding what we can do today in 2025 to be better prepared for what happens in 2026, July, or even later. For example, assuming I have a gap of 100 FTEs in a certain role, how much of that can I fill by already starting today to develop existing engineers into one of those future roles? How many of them can I take along? For the remaining gap, which labor markets come into play that allow me to actually find those skills? Can I position myself already today with optimized job postings to attract the talent I'll need tomorrow?

Of course, there's also the bot component. Can I use automation? And how can I use automation to reduce that gap from 100 to 90 or 80 just by utilizing technology?

This is what we call the end-to-end journey, ending up in clear action plans. But you can also see here that SWP is not a one-time exercise. It's a process that repeats itself. We wake up another day, and the global economy might have changed, regulations might have changed — we need to assess new scenarios quickly. This has been a huge pain point in the past. It used to take a long time to quantify these business scenarios in a workforce plan. Still, with the technologies in place nowadays, you can assess those scenarios quickly and be better prepared for whatever might come.

So much for the theory, let's have a look, John, at how that looks in practice. And how do we bring all of this together?

[0:19:54] John Lynch: Yes. Yeah. I think we've noticed a few questions in the chat. I will come back to those. Thank you, Klaus. Thank you for your comments, everybody. Christine, thank you for your comments. We wanted to demonstrate how things connect to those big questions. So, when there are specific functions that show you the specific functions that answer the big CEO question, we'll ask you a few quick questions. You'll see a few polls coming up on your screen. Please take a moment to answer those polls; it will help us guide our conversation.

Alright. Our first question concerns job architecture. Let me know if you can see this. Okay. How current is your job architecture for critical roles? Does it reflect what the market now expects to see? And can you see me? I just want to make sure everyone can see the poll. Can I see it under the tab? Yeah. K. So we're going to be able to see some of these answers coming in. Thank you.

So, how would you gauge and evaluate your capabilities? Alright. Okay. What we can see there is that most people evaluate their capabilities — their ability to see job architecture for a critical role — as intermediate. So we have 31 out of it. Our group here, by and large, has updated some roles but not consistently across the organization.

I think it would be helpful if we could show you a little bit more about how we can give that kind of visibility or how we can improve that process. So, Christine, you're going to show us how you can specifically ensure you have the right people with the right skills at the right time, roles, and locations to answer that business strategy question: Do we have the right capabilities in place?

[0:22:20] Christian Vetter: Yeah. I'm going to give you a quick glimpse of how we manage job architectures with our toolset. What you see here on the screen is the Libraries module that we have. Essentially, customers upload their job architecture here. It doesn't matter whether it's a one-, two-, three-, four-, or five-layer architecture, and whether it's organized by department, job family, or otherwise. Everything is set up in here, and some customers claim we don't even have a job architecture. We can, of course, also use the market data we have to build one tailored to the business, industry, and current maturity.

The magic then happens by connecting these customer roles to our skills intelligence. The first step we undertake for each of these roles is to connect them to a market profile. We have thousands of these industry-specific, generic ones — whatever is required. And through this connection, that whole skills intelligence bucket is suddenly unlocked. That role will be populated with current skills associated with that specific profile. Something truly unique and very helpful for customers is that it will also provide an outlook into the skills of tomorrow, based on our market data and trend analysis, which are likely to be associated with these profiles in the years to come.

So it's two views: the current view and the future view. We also provide you with the capability to benchmark your current job descriptions, if you have any job postings, against market data, so you can understand, either in the current or future setup, what gaps exist in today's descriptions based on market insights.

Typical use cases for the talent acquisition group here would be updating job descriptions so they can be much better tailored to the market if they can already connect with the skills of the future, to be much better prepared and more proactive.

For the L&D target group, it is really useful to ensure that the learning curriculum or training portfolio they provide to your organization supports the development of these future skills.

This module will also be used by strategists or workforce planners to gain insights into the costs. We also have the costs for these different skills. What are the costs for these skills in specific locations, such as cities, etc., to really evaluate those 'build versus buy' scenarios? What are the skills adjacent to my existing profiles? How difficult or easy will it be to build these roles from within?

These are all the kinds of insights or use cases that can be unleashed by connecting a customer's job architecture with the magnificent market data that we bring in.

[0:23:59] John Lynch: Alright. Awesome. Okay. So we have... okay. That's a great view of roles, locations, and understanding. So, I think the next part we wanted to talk about was identifying skills gaps. We have a poll here again, and it's great to hear from you all. How well can you identify skills gaps in current roles versus market expectations?

If you go to the box on the right-hand side of the screen, you'll see a poll tab there. That's where you can answer the question. Christian, you can see the responses in the left menu bar.

Alright. Super. So beginners. Again, many intermediate individuals or those at the beginner stage lack visibility into how their current skills align with market expectations or identify gaps, often through manager input or performance reviews — a manual process.

Alright. That is valuable. So, Christine, you're going to give us a brief overview now on how to identify current skills and compare them with the market based on what you already have in your organization. Is that right?

[0:26:53] Christian Vetter: Yes. That's correct. As part of the SWP process, companies would then connect future roles with current employees to ensure that — using the automotive example — the diesel engineer is assigned a suitable future role that the company will soon need. And then that future role will likely be informed by market data, as it presents a new opportunity for that particular company. And yes, once these future skills requirements are made available, we can jump in and start understanding — zooming out to a 30,000-foot level — how well my current workforce fits my future requirements in a specific scenario.

In this particular example, we see that there's roughly a 60% skills match between my current workforce and what I will require in the future. Obviously, to give context to that number, you need to measure it over time. You want to ensure that the number moves toward 100% and doesn't remain at this level.

The more relevant part than that overall figure — which is good to report for SWC initiatives — is understanding where that gap is. I had to filter already on the HR side, but you can then explore, like, on the job family or job role level, which are the families and roles that are already pretty much ready for the future.

You can see here the data analyst, for example — the people in that department — they are already pretty future-fit. And then if you scroll down, you see in this example the HR business partners. Those are the target groups for building initiatives to ensure they're supported with the right training. And perhaps this profile is constantly changing and evolving — the future profile, where many new skill requirements become relevant. Therefore, you need to ensure that you train these people upfront. Alternatively, it may be that the current skills listed in the profiles do not align perfectly. So this helps you explore.

What I think is also really interesting to understand — and also what already fits — is IT. What are the specific skills we are missing? So, is it enough to just give everybody Microsoft Excel training, and then that will be alright? No. In this case, we see that those are specifically the skills the organization needs in the future, which are missing in that particular unit.

Of course, we can also zoom out to a more global context and understand, skill cluster by skill cluster, what the actual gaps are in your company—both in terms of availability and proficiency—to explore. People might have problem-solving skills, but they are not mature enough. The requirement that you have for this skill in the future is just much, much more mature than what the people currently bring in. You need to build upon existing skills to strengthen them.

[0:29:56] John Lynch: Awesome. Alright. We have a couple of questions coming in, but I won't pause yet because I think we can revisit these questions after the event. But, there is one particularly relevant question, I'm afraid. I'm going to pause on skills inventory. So, thank you, Theresa, for your question. How do you keep the skills inventory of your workforce up to date as people continually acquire new skills? Could you elaborate on that a bit?

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[0:30:24] Christian Vetter: Yeah. Sure. So it's all about connecting with the skills infrastructure of our customers. Typically, they come along with a large HRIS, which may host a multitude of structured or unstructured data points. There's always a way to start from scratch, building a skills portfolio within our solution, but we typically recommend — this is how we have built the product — to connect it with existing data silos. We can retrieve that information from training databases, CVs, and various other data sources, including job descriptions, among others. We can infer skills, or we can also use those data points as evidence to provide to people, so they can start with an 80% completed profile and just add the missing 20%. Keeping it up to date essentially means continuously harvesting information of that kind. We understand that the person has now completed training on AI and has successfully finished it. Then we can fetch that kind of information and extrapolate the skills insights from that.

[0:31:35] John Lynch: Awesome. Thank you. I hope that answers your question, Theresa. If you have an additional question, please drop it in the chat. And for anyone else, please note that we have time set aside to answer those. I think we couldn't go any further than now before discussing automation and AI, as we touched on at the start of the session.

Another poll question for you today. We would love to hear from our audience on this: How confident are you in identifying the roles and tasks with automation potential within your workforce? You see the poll open for you there. It'd be great to hear from you. Look in the poll tab on the right-hand side; you can answer there.

So I think, Christian, you'll be showing us in just a moment how to identify those roles and even specific tasks down to the granular level that are most likely or most exposed to automation.

And I think, again, if you look at the results coming in — thank you — I think most people are at the intermediate or beginner stage. Either we've just started identifying tasks that could be automated, or we are just beginning to explore the potential for automation. We have a handful of advanced professionals who are already factoring automation into their workforce planning roles.

If you're in that advanced group, especially, I'd love to hear from you in the channel what you're doing so that we can hear about your success stories.

But okay, those are some pretty interesting responses, and people starting off their journey. So, how can we support that in the platform?

[0:33:13] Christian Vetter: Yeah. Going back to that library, besides equipping you with the skills of today and tomorrow, we also provide you with insights into the duties, how we believe activities behind roles evolve, and what your existing activities look like, extrapolated from the data. And then, essentially, automation is triggered by task, not by skills. Yeah. So we can then explore — let's stay with the data scientists for now. These are the 10 main activities allocated to that profile. And you see here that for every one of these activities, we provide you with insights on how heavily this activity is impacted by automation, driven by freely chosen factors such as AI, robotics process automation, and industrial automation, among others.

And then we provide you with these aggregated figures, showing your domain-impacting technologies and how automation takes effect. And this number can then be transferred into the planning scenario tool, which I'll show you for a few minutes now, where you can explore scenarios where I continue to grow with regular productivity changes. Let's say, over time, I'll end up with a demand of 777, with about the same amount of roles growing in demand as declining in demand. However, if I invest heavily in automation technology, which is now a scenario you can model in the tool, I can then explore how the demand is actually going to drop by around 40 FTEs. And only one of the six roles I'm planning for here actually ends up being an in-demand role.

Let's explore which one that is. That's a machine learning engineer. While the other roles in my portfolio will then be decreasing. This is a powerful tool for understanding how automation will actually impact the FTE numbers. Additionally, it enables a cost analysis to determine, in a non-automation-driven scenario, that I'll end up with a workforce cost of roughly USD 80,000,000. In an automation-focused scenario, I'll be able to reduce costs. So you can use these models to quantify or justify our investments into such technologies to explore potential ROIs here and to then also understand, if we go down here and, for example, break this down by job role, to understand I'll heavily need to invest into machine learning engineers to achieve those AI gains. However, in return, I'll be able to reduce many of the costs on the recruiter side, as I might invest in recruiting automation technologies, for example. This really helps you break it down by specific target settings as well.

[0:36:10] John Lynch: Alright. Super. I think that is one feature that has had a very positive response — automation analysis. If, as a reminder, anyone would like to see a more in-depth view of any of these features, they can use the Request a Demo button, and we'll be happy to follow up and provide that view.

We have one more thing to spotlight here, and then we'll get on to some questions, so please drop those questions into the chat.

Our final question concerns forecasting. So, how precisely can you forecast future talent gaps or surpluses? How successfully can you understand what is coming down the line? If you click on the poll tab, you'll be able to answer questions there.

Thank you. I'm already seeing some results coming in. There we go. Again, I think many people are at the intermediate and beginner levels — some general headcount projections, but no detailed forecasting. Seeing a few forecast gaps — able to forecast a few gaps in key areas. That makes sense. Unable to forecast one or two, you can forecast gaps and services by role.

Okay. So, Christy, I know you're going to dive a little deeper into this one and show us how we can help forecast in the platform according to various attributes — location, skills, and jobs.

[0:37:46] Christian Vetter: I'll gladly do so. I think it's a bit of a trick question because precisely forecasting is very difficult, as you wake up the next morning, and the world around you has changed. Numerous things are happening. So I guess what I would answer to this question is, if you know how to do scenario planning, you'll likely end up being better prepared versus not doing so because only relying on one likely accurate scenario might also end up being a very bad idea as that scenario might not end up being exactly the one that you wish for.

But, going back to the analysis that the tool performs, it's essentially a scenario modeling tool. You have different AI scenarios, different growth scenarios, and different attrition scenarios. Also, we shouldn't forget about the supply plan. So, when you bring those together, I mean, bringing them together looks something like this, where you see that this is the workforce I start with. In this scenario, I will lose people due to attrition, retirements, and other factors. I'll end up with the future supply, and I'll mirror that against my future demand, which, I think, is a topic worthy of its webinar.

However, coming up with that future demand and then providing you with insights on the gap in business areas, job roles, or even location — whatever you want to plan for — to explore how and why these things happen. So here you can explore for the recruiter. A huge decline in demand mainly drives the surplus, while for the machine learning engineer, growth is triggered by both a high rate of attrition and a slightly increased demand. And this is why you end up with such a big gap. This helps you understand it, and then you can flip between those different scenarios.

I'm going to show this in this heat map because it's nice to visualize. Understanding different scenarios essentially means that regardless of which scenario you plan for, you will always have a surplus for the recruiter role. That requires a different strategy than for the data scientist, where, in one scenario, you'll end up with a rather big surplus, while in another scenario, you'll end up with a huge shortage. So this is exactly what you want to find out through SWP: what are the roles that are not so trivial, where I need to really focus on today and be flexible — maybe hiring through contingency or with limited contracts — versus being more certain that whatever scenario may kick in, I need more sustainable measures for my organization.

Just a very last sentence on this. We've been very quantitative here, but obviously, the tool will also help you explore the qualitative side of things. So, once the planning is completed, you can also explore what skills I, as an organization, need to start building and what skills are losing importance for our organization. What is the actual general skill shift I will explore over the year? You get both of these dimensions: qualitative risks and quantitative risks, under certain scenarios.

So, I guess this is a really good tool set to be well-prepared for whatever may come. We're not going into the gap closing, the modeling of measures — again, that could be another webinar — but of course, for the sake of completing this cycle, you will end up defining the proper build, buy, borrow scenario based on the market data on skills availabilities, costs, skills adjacencies, and internal development path that the system helps you find the right trade-off between those different B's. And again, here, making sure you have the right portfolio of measures in place.

[0:41:50] John Lynch: Alright. Thank you, Christian. That was a thank you for the run-through of all those different features. I think we have a lot to cover, but also there's a lot more that could be covered in this section. And we could probably take an hour and a half, not forty-five minutes. However, we do, as you've been demonstrating, and there have been some questions from the audience, so we can take some time to go through those now.

If you have any further questions, guys, after that demo, we can drop them in there. If we can get to them now, we'll do so, and if we don't, we'll follow up. There are some really good ones, so I just wanted to spotlight a couple of the more recent ones here.

Scott, thank you for your question. Here's a specific question about geography. As you forecast within TalentNeuron, can you model this based on country?

[0:42:43] Christian Vetter: Yes. You can model on whatever you want to model on. I always take the job role because it's very easy to comprehend, but we have companies building and planning on skills and country level as well.

[0:42:56] John Lynch: Mhmm. Yeah. I think that answers your question also, Sarah. Regarding geographic targeting, there is a very granular level of targeting available, allowing us to target according to MSAs or other geographical dimensions, depending on the market you're in. Alright. We have some more, also some really good comps, but give me one second here. Okay. In terms of data, I think this raises a couple of questions about where the data is sourced from — whether it is internal or external data. Could you say a little bit more about that? Where is the data coming from for profiles, skills, etc.?

[0:43:45] Christian Vetter: Yeah. So, depends on whether you look at the internal or the external side of things. For instance, on the internal side of things, I mentioned earlier that we process existing data points, whether structured or unstructured. Being originally from Europe and now a global organization, we face a significant challenge, of course, with different languages. So the algorithm that crunches through the data speaks around 30 languages. This is essentially a semi-automated process.

Crunching through existing data points, on the external side of things, we explore by analyzing job postings, validating that with patents, and also other statistical or scientific publications that we can find out there. So, basically, the more data, the better. In terms of internal data points, typically, the main complexity is the structure of the data, but we've been doing that for eleven years now. I think we've found the right path to accomplish that.

[0:44:53] John Lynch: Terrific. Alright. And we have more of a — I'd say this may be the last of the questions I have right here, but let me just take a look. So, Luz, here's a detailed question, so you can open up on this one. What prerequisites are needed to move to a sophisticated strategic workforce plan: job architecture, job leveling, updated job descriptions? And if none of those, what intermediary tools would you suggest to accompany the journey and maturity curve? And that's for a health care organization. So I suppose the question is where to start and how to move along that maturity curve.

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[0:45:30] Christian Vetter: Yeah. So, the minimum requirement is having the number of FTEs or headcounts by role, if you want to plan by role. That's it, because if you don't have a job architecture, we can build that out from market data. If you have one, even if it's a little out of date or out of shape, it helps because we can then provide you with insights on gaps. But if you don't have any, we build that up from market data. We also need to determine the number of people in each of these roles, so you can develop supply and demand scenarios. The skills component can also be introduced, as I've shown at the beginning, through external data points to understand which skills are current and which are those of tomorrow. And what's important tomorrow, so you can start with that. And then, obviously, there's a lot of nice-to-have data that would make your results more accurate. We can, of course, explore that together in a separate meeting as well.

[0:46:31] John Lynch: Awesome. Yeah. And I think we've already in part answered this question. Vincent has another question about the skills piece. Now, how do we evolve those skills and the data that supports them when the market is evolving so quickly? So, I think that's a key question that we're going to delve into a little bit more. Yeah. Hughes, I'd love to see your 1974 skills library for sure. So, how do we evolve that skill set? Probably going to put my question to you, Christian.

[0:47:03] Christian Vetter: Sorry. What's the question? How do we evolve the skill set?

[0:47:06] John Lynch: How does your skill referential naturally evolve in a market where they change form and reshape so quickly?

[0:47:13] Christian Vetter: I mean, we monitor skills every day by crunching through sources such as job postings, patents, publications, etc. We have a dedicated team that only manages the skills and job library. We always ensure that we stay up-to-date with the latest skills and jobs on our radar. Then we feed that information with market data to ensure it's built with intelligence, understanding adjacencies between skills, their associated costs, and their availability. Based on these skills, we build out that whole intelligence, and it's a continuous process. It's a never-ending race. And it's a high-speed race, as you mentioned. They change every day. This is how we ensure we always stay ahead in that race.

[0:48:08] John Lynch: Fantastic. As we've been discussing, we've received a number of excellent follow-up questions, particularly related to geos and specific sectors. I think most of these may be a little hard to answer on the call, so we will follow up with them. So please keep an eye out for that outreach. Additionally, if you click the 'Request a Demo' button, we will be able to contact you and help you understand your specific use case.

Going back to the maturity piece, that was a great segue that I passed up, but I'm going to take it now. We have a maturity assessment that helps organizations figure out where to start along this workforce transformation journey and where to begin on their SWP journey.

They help them stake out which data they have, which data they need, and start to define a plan. You can find this resource under the 'Doc' section on your screen. There's a QR code in case you need to share this PowerPoint with anyone else, which can also be found under the 'doc' section. However, we'd be happy to walk you through it or share it with you so you can take it at your own pace.

If you would like to continue the conversation with our team, please click the 'Request a Demo' button. We'd be happy to provide you with a bit more detail.

We ran a little over time, but I think that was a valuable extra four minutes. So thank you for sticking with us, everybody who's on the call. And Christian, thank you for taking the time to demo so thoroughly today. I hope you have a great rest of your day.

[0:49:42] Christian Vetter: Have a great day. Thank you for being here. See you soon.

[0:49:45] John Lynch: Alright. Thanks ever so much, everybody. Bye-bye.

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