Optimize Your Skills Strategy with Labor Market Data (APAC)

[0:00:10] Shagun Anand: Hello. For those of you who have joined us, welcome to TalentNeuron's webinar on optimizing your skill strategy with labor market data. My name is Shagun Anand. I'm a member of TalentNeuron's consulting team, and I'm delighted to facilitate today's dedicated APAC webinar.

I have a background in data analysis and advising clients on talent trends in several geographies, including APAC. I'm based in Bangalore, India, and I frequently connect with clients in Australia, Singapore, India, and other APAC locations. Some of these clients are here with us today.

TalentNeuron is the world's leading labor market data company, and I will share a little more about our work in just a minute. But first, let me share a few tips to enhance your webinar experience.

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Now that we are settled in, let me hand over to our presenter for today, Clare Moncrieff, Senior Consultant at TalentNeuron.

[0:02:12] Clare Moncrieff: Well, thanks, Shagun, and greetings, everyone. It's a pleasure to be here today to discuss this topic with you.

For those of you I haven't met, I have about 20 years of experience in HR and professional services. Over the years, I've had the pleasure of working in many countries across the Asia-Pacific region. So, I'm really looking forward to today's conversation.

I'm going to hand back to Shagun, though, before we get into it, to give you a little bit of context on where all of this data comes from that we're going to be talking about today.

[0:02:40] Shagun Anand: Thank you, Clare. If I may say so myself, we have exceptional global data coverage spanning more than 40 countries and continue growing. Our platform covers 90% of the global GDP. Our comprehensive data suite in key markets includes supply and demand, salary insights, and diversity metrics for several talent profiles. We have a strong presence in APAC and robust coverage in India, Australia, and Southeast Asian nations. We are constantly expanding the depth of our data in China and other APAC markets. Major global economies like North America, Europe, and APAC are well represented in our datasets.

We have versatile data offerings with a full suite available in numerous countries, which is also supported with custom research capabilities to meet specific needs. Our continuous expansion and improvement plans aim to increase the data depth and geographical reach, with a special focus on enhancing APAC coverage.

With the data, we empower global workforce decisions by providing crucial insights for strategic planning and supporting businesses in navigating diverse labor markets, especially in APAC. All of this data is pivotal in addressing the challenge at hand.

Today, we find ourselves in a structural shortage of digital skills. The skills are evolving so fast the newest and most leading edge are in very short supply and businesses are struggling to keep up. Today, we are going to talk about just how big the challenge is and what you can do about it.

Clare, let me hand it back to you.

[0:04:23] Clare Moncrieff: Thanks, Shagun. Well, I think the title on this slide gives away the punchline. The digital mandate is as urgent as ever. The pace of technological change is driving significant focus.

93% of executives agree we need to innovate with purpose, and 95% think generative AI alone just one category of digital skill will require a complete modernization of technology architecture.

But on the right, 70% of digital transformations fail. Given the increasing pace of change across digital skills, there's a depressing irony that even if you get it right, it can take nearly three years to begin competing in the digital market.

And not on the page, but important, digital transformation failures are very expensive. I've seen a data point estimating that by 2026, global digital transformation spending is forecast to reach $3.4 trillion. And yes, that is a trillion with a "T."

So, digital transformation is a mandate. You cannot afford not to do it if you want to stay competitive. It's probably not news to anyone on this call that lack of capability is one of the key drivers of both success and failure.

And yet, just when we need more digital capability, there's less of it out there. We've pulled some data from our platform to illustrate the point. We searched several large international markets well known for tech skills and the digital skills of large language models. Those numbers clearly show across the page that this skill is very difficult to hire regardless of location. It scores a 9 or 10 on our 10-point scale of difficulty pretty much everywhere.

We could have chosen any number of other digital skills to illustrate this point and gotten the same result. In fact, across this webinar, we'll be looking at a variety of digital skills and roles to do just that.

But digital transformation is creating rapid skill evolution. World Economic Forum data tells us that employers estimate 44% of their skills will be disrupted in the next five years. And 60% of businesses say skills gaps in their local labor markets hold back business transformation.

In fact, it's the top barrier globally. So it's not only that the skills are scarce the competition is also fierce. It's hard to buy digital talent no matter where in the world you look, even in several well-known tech hubs.

Our labor market data clearly shows that places like Bangalore are full of tech talent. We looked to see who else is trying to hire for those large language model skills. Take a look at some of these well-known brands you'll be competing with for this talent in global locations.

Of course, these companies can offer very compelling employee value propositions, or EVPs, and we'll take a deeper look at this kind of insight later. But here on the right, we've called out just a few of the offerings, including flexible location, which IT and tech employees expect more than any other function.

So, digital transformation is a must to stay competitive. It's hard to do it right, and you need the right skills to make it successful. But we are in an era of structural digital skills shortages. Both building and buying digital skills require investments.

So, how do you make the right investments and use the right strategies? There are two key steps. First, identify the digital skills that are your organization's highest priority. Once you know what they are, use AI and labor market data intelligence to make build-versus-buy decisions in your most critical markets.

We're going to look at both of these with a focus on Asia-Pacific, starting with the first: identifying high-priority skills.

Identifying high-priority skills for your organization is the first step, and it's really about improving skill sensing using talent intelligence to help your business leaders anticipate what their organization will need.

On the upper left, most of us involve business leaders in our discussions of what the future holds. But the issue is that leaders do not have a crystal ball when it comes to skills evolution. Skills are changing so fast that leaders cannot keep up better than anyone else.

What leaders can do well is help you interpret the data in the context of your organization's strategy and help assess what is likely most relevant or not. As I said, most companies use senior leaders and cross-functional teams, which you see in the lower left of the page here, to make educated guesses about what skills will be needed.

Many also have skills inventories that help them understand their existing skills and how those are evolving, as seen in the upper right. However, given the pace of technological change, those methods alone will not be fast enough.

You can identify talent and skill implications faster with external skills analytics, which we've highlighted on the lower right-hand side. And I want to give you an example of how to do this. Now, before I get into my example, Shagun and I want to define a few terms and explain how we think about skills evolution at TalentNeuron.

[0:09:45] Shagun Anand: Thank you, Clare. Our skills evolution model for a given role is based on two factors. First, what proportion of job postings for the role in the last 12 months advertised for the skill? Second, how has this proportion changed over the last few years? This is usually done within a cohort of competitors that are most relevant to you with respect to the type of role, business, industry, organizational structure, footprint, and other factors.

We illustrate skills evolution as shown on this page. There are five stages of the skills life cycle, from new on the left to declining on the right. The vertical axis captures skill prevalence in the market. The higher the curve, the more that skill is found in the labor market.

Now, let me go through the five stages, starting on the left with new skills. These are skills that were advertised for the role in the last 12 months but not before then, so they represent new requirements for such roles.

And if you're using this type of data to build hypotheses for your organization, these are skills we would suggest watching and monitoring for the role you're considering. Until we see a bit more traction, we wouldn't necessarily recommend acting on them, but they are very interesting early indicators. You can track these types of new skills on our self-service platform.

Then we have emerging skills. These skills have a relatively low share of job postings for the role in the last 12 months, but this share has grown exponentially over the last few years. Then we have growing skills. These two have a low share of jobs posted, although they are notably more present in the market than the emerging skills. This share has also been growing over the last few years.

[0:11:38] Clare Moncrieff: And it's these emerging and growing skills that we really recommend paying attention to. Talent availability in the market is usually low, competition is high, but these skills are growing in demand, and the chance is very good we're going to need more of them in the future. To get enough of these skills, we advise embedding them in L&D plans and offering upskilling opportunities to professionals with adjacent skills because you probably won't be able to hire enough of them straight out of the market.

Core skills have the largest share of job postings, either growing slowly or remaining steady over the last few years. So talent with these skills is generally available in the market, and recruiting efforts can be focused on acquiring those skills.

I'll set myself on mute. Then we have the declining skills. The share of these skills has been declining over the last few years, as they must be losing relevance or becoming the implied minimum requirement for the role.

If you're investing in L&D for these particular skills, your investments can probably be reallocated. Opportunities for automation or outsourcing can also be explored for these skills. Today, I want to show you how powerful this data can be as you build hypotheses about what skills your business will need in the future.

I'm going to use a machine learning engineer as my example because machine learning is a widely discussed topic. Those of you who like to read footnotes will notice I'm starting my example in the US, and that's intentional because technology skills tend to be more mature there. However, I'm also going to show you how this data can be used in Asia-Pacific in just a few pages.

On the left, you can see emerging skills for this role, and on the right, you can see some growing skills. If we look at the left, some of these emerging skills include things like Hugging Face, which is actually a technology used in this space, real-time software, and Pivotal. Some of the growing skills include generative AI, large language models, statistical language models, and so on.

The difference is that the skills on the right are slightly more available in the labor market, as Shagun explained. But in both cases, these skills are on a path of ascendancy for ML engineers, and in general, they're hard to find in the labor market.

Now, I want to layer over an industry perspective. Digital skills are in demand across every industry. Financial services happen to be the fourth-highest industry represented among top employers of digital skills. So, I'm going to show you how to use this powerful skills data to build a hypothesis for what skills you might need in financial services.

Here, we're looking at the skill evolution curve that we shared a moment ago, specifically for AI/ML engineers in financial services. A couple of things to note: PyTorch is a growing skill, while on the left, we see that cross-functional integration and ChatGPT are new skills for this role in financial services.

As we look at these new skills, including ChatGPT, we recognize that generative AI is at the forefront of most organizations' minds as we shift toward a more automated way of working. Cross-functional integration is a skill that reflects increasing collaboration between professionals across domains as organizations incorporate AI/ML into their workstreams.

PyTorch may seem like a relatively new machine learning framework, typically used in natural language processing and computer vision. So, it makes sense that it's not yet widely prevalent in the market and is still seen as a growing skill.

[0:15:28] Shagun Anand: Clare, I bet the audience is wondering why financial services is a declining skill if this is an example of financial services.

[0:15:36] Clare Moncrieff: Yeah. Great question. It is a financial services example, but the role is an AI/ML engineer. What this tells me is that companies are no longer looking for experience in the financial services industry in this role. We know that almost all companies compete for tech talent now, and this particular role is not limited to a specific industry. Within the financial services industry, that skill requirement is falling out of job postings, so they don't limit their talent pipeline. This is really great data if you want to get ahead of the curve in terms of what's coming in financial services.

But if you really want to get ahead of the curve for digital skills, you actually need to look further afield. Let's see what happens when we look at the evolution of skills for this role in FinTech. Now, we're looking at the skills evolution curve for the same role — the AI/ML engineer — and FinTech companies as the comparator cohort. I left the financial services skills in the picture for reference.

So, PyTorch is still growing, but some other skills have moved along the line from new to emerging. The new FS skills are more prevalent in the FinTech companies' market. Cross-function integration has shifted to emerging. Rather than posting for ChatGPT, we see FinTech companies posting for conversational AI in general, suggesting a bit more maturity in the skills they're seeking. They could be looking to develop their own conversational AI tools integrated into their platforms. As we see the cross-functional integration skills shift further into emerging skills, they're also a bit further along in their journey to integrate AI into products and other aspects of their business.

However, we still see PyTorch as a growing skill. So, while they're further ahead in some respects, PyTorch hasn't yet become core within this industry. Clearly, the point here is to look at innovators in your industry to extend your field of vision when it comes to digital skill evolution. But I really want to show people on this call how they can look at the most cutting-edge companies to extend their field of vision even further.

What happens when we look at this critical digital role and how its skills are evolving in leading technology companies? Specifically, let's see what happens when we look at this same role of machine learning engineer at Google, Apple, Meta, Microsoft, Amazon, and Alphabet. Here, we see PyTorch has become a core skill, along with deep learning, which is related to improving AI's ability to process data and think more like a human brain. Now we see skills emerging on the left of the curve — AI voice is "new," and virtual reality is "emerging."

As we saw earlier, the fact that PyTorch and deep learning are core here reflects that they're quite prevalent at these tech leaders, which is no surprise given PyTorch was actually developed by Meta in 2016. But it does show that other organizations have been slowly adopting it into their workflows. Seeing all these other skills come into the picture tells me the focus is shifting from just figuring out how to use AI to evolving AI to new use cases — like AI voice to develop natural voice sounds and also AI being integrated into virtual reality.

It shouldn't be surprising to see these big tech companies expanding the possibilities of what can be done with AI, but it shows that other industries can gain a view of what the future-focused digital skills may be — and what may be important in the next five-plus years.

So, if I am in financial services and I'm looking at this data, I would probably be starting to think: if AI voice is emerging or new, am I paying attention to that? Could I be integrating it into my chatbots over time? Should I be thinking about how it will enhance my customer experience, and so on?

This first step is all about figuring out what skills your digital transformation needs. Armed with this powerful skills data — showing how certain digital roles are evolving in their most progressive iterations — you can circle back to the business strategy and add more value.

Let me pause here and see if we have any questions from the audience that we would want to address.

[0:19:48] Shagun Anand: So we have a lot of interesting questions here, and I think we should start with the sorry. One second. Let me scroll to the question section. Which company should wait before investing in a new skill? A skill being categorized as new means that it has been posted, albeit only in the last 12 months.

[0:20:14] Clare Moncrieff: How do we gain a competitive advantage? Yes. I would say this is a great example to use with what's on the screen here. I would advise not making investments necessarily in the new skills until you see whether they gain traction or not. It's possible that new skills can come onto the scene and then perhaps not continue to emerge or grow. But the power of having the early indicators is that even if you don't make large investments, you can start thinking through some potential use cases and business applications, like the example I just gave with AI Voice.

If I'm in financial services, I might not start trying to program voice into my client interface today, but I might start thinking about what that could look like and whether I have the capabilities in-house to do that if I wanted to. What sorts of investments might be required so that if I start to see AI voices moving into emerging and growing, I am prepared to take advantage of that opportunity?

So, it's a way to help you see around the corner, peer into the future a bit, and get a sneak peek at what skills might be affecting your work.

[0:21:28] Shagun Anand: Thanks, Clare. I bet the audience is also wondering how we can ensure that our data sources are reliable and unbiased when using them for skill identification and decision-making.

[0:21:42] Clare Moncrieff: Well, I think the first thing you want to be wary of there is self-reported data. Most skills tech tries to validate skills through demonstrated evidence, but do ask how that's done and how bias is removed from the data.

The beauty of using tools like TalentNeuron's big tech and AI-driven platforms is that we deal with a huge volume of data, and our algorithms are designed to minimize noise in the system. We have a very high confidence in the data we report. That's why we're such big fans of adding external labor market intelligence to internal processes — to help remove bias and bring objective, live market data into the discussion.

Okay, great. I know we have time for more questions at the end of the webinar, Shagun, so I'll get back to you then. But I'd love to continue showing folks what to do next once you have your skills. Of course, the next question is whether you're going to build, buy, borrow, or pursue any of the other “B's” out there — and the rest of the webinar is really about making those decisions and how to best align your investments afterward.

Let's start by actually making the build versus buy strategy decision. Back in the olden days, we used to say that pages like this were great tearaway sheets you could photocopy and distribute to your team. But in the digital era, this is a great summary page to refer back to and highlight when sharing the recording of this webinar with your team.

We tried to highlight the build, buy, and borrow decisions along the top and the critical areas of data that can help you down the left. In the remaining time today, we'll look at how labor market data informs each of these.

The time to buy is when external talent availability is high, you have a great EVP, and you can offer location flexibility. If, on the other hand, external talent availability is low, your EVP is weak, your brand isn't well known in the market, or you can't offer flexibility, you're better off building or borrowing.

The bottom two rows are really interesting. It's important to determine whether the skill needs to be proprietary and whether you have adjacent skills in your workforce. If either of those is true, build is the best way forward.

Remember, we're in an era of structural talent shortages, especially for digital talent, and there's a rapidly growing demand for new skills. We need to leverage data to decide whether we can realistically buy talent or need to build it. We can't just assume we can buy.

There's fierce competition in the labor market from tough companies to go up against. So, even if supply numbers look high in places like Singapore or Sydney, don't assume you'll win talent there.

Now, we're showing who the top competitors are for the digital talent segment in a major location — in this case, a cloud engineer in Bangalore, India. Bangalore looks like a great tech talent market until you see who you're against. It has many companies hiring cloud engineers — Deloitte, Oracle, JPMorgan, SAP, and others — and each is hiring in volume. It's not just one or two hires, but tens or, in Deloitte's case, over a hundred cloud engineers.

If you want to hire there, too, chances are good your candidates are also talking to the companies on this page. Let's see what they might be talking about.

On this page, we took the top competitors from the previous slide and looked at their EVP offerings in our new EVP module. This data is specific to EVP attributes offered to cloud engineers in Bangalore.

This kind of data is helpful in many ways. For example, perhaps you have a strong diversity pitch that sets you apart from Deloitte, who don't seem to be focusing on that for cloud engineers. Or if you can offer meaningful work, you'll stand out against Oracle, where that's not a key strength.

This data helps you see how competitive the market is for the digital skills you need, what you're up against if you decide to buy, and what you need to do to ensure your EVP is compelling.

If you decide that buying is the best strategy for certain roles or skills, you need to do three things to set yourself up for success: know your competitors, know yourself and know your gaps.

Now, building on the EVP point, we're looking at how those same competitors structure job design so you can compare. Again, we're looking at the cloud engineer role in Bangalore. Cloud engineering is critical for all three companies — especially Deloitte. JPMorgan, though, doesn't really emphasize communication or leadership.

Sometimes, clients find they're not including critical skills or emphasizing skills that no one else in the market is discussing. Ask whether candidates will be intrigued or ruled out if you have unique skill requirements.

The second piece is knowing yourself. Let's return to the earlier skill evolution and apply it to the Asia-Pacific. How are you showing up in the market relative to those skill evolutions?

Let's play out a scenario. Suppose I'm a talent acquisition partner of a financial services institution or HRBP. I could draw several hypotheses from the skills evolution data:

• We need to use external intelligence on how skills are evolving to inform talent implications for business strategies.

• Our digital evolution is behind big tech companies, and if we want to accelerate, we should look at what leading companies are doing.

• Maybe our EVP is outdated. We're competing with tech companies for talent, but we're not competitive.

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Here's what our platform can do to help. We're using HSBC as an example— a Hong Kong-based global bank. Our platform helps size gaps in the market. On the left, you can see what HSBC has been hiring for. One top digital role over the past two years has been a software developer.

In the middle, you see actual job titles hired variations by level and language focus. On the right, software engineering, Java, and more are the top skills hired.

You can compare this list of skills to what top tech companies are hiring for in the same role. You can see the difference even using a real-time view, not just evolution data. Java is still prominent, but now there's greater emphasis on coding, JavaScript, and artificial intelligence.

Knowing your competitors, yourself, and your gaps helps you move from identifying critical digital skills to seeing what's around the corner using external labor market intelligence. This data is the next best thing to a crystal ball on skills evolution.

I want to summarize what we've covered today and then open up the floor to see if there are any more questions. So first of all, digital transformation is a requirement to remain competitive.

But in an era of structural skills shortages, investments in build versus buy must be made using comprehensive and objective data. We talked about two things that really support build versus buy decisions for talent strategies in Asia-Pacific.

First of all, identify the right skills for your organization strategy, many of which will likely be digital, and then make the right build versus buy or borrow decisions based on data. Hire where you're in a strong position relative to your local market and when you build, again, using data to prioritize the right populations with adjacent skills to increase the likelihood of learning investments and accelerate business outcomes. So, let's look at a couple of summary pages for you on each of those two.

In terms of identifying future skill requirements, it's about understanding your needs, but also what your competitors' hiring patterns are, what skill evolution is going to likely affect you, impacts to those key trends and emerging skill requirements so that you can get out ahead of them, and then understanding where you have the biggest gaps relative to the market.

And on our second discussion topic today, making this build versus buy decisions, it's really about identifying where those structural skill gaps put your business at risk, considering the availability, competitiveness, and so forth of your EVP, what else you're offering in terms of things like location flexibility, and just how aligned the skills are to your proprietary requirements versus some of the more interchangeable skills.

Think about how those things affect your team structure, your workflow, and your role requirements. We also do a lot of work on adjacent skills. We haven't had time to go into that in-depth in our webinar today, but if you have questions about using adjacent skills, we'd love to hear from you. And with that, in fact, are there any questions that we can address? Let's open the floor and see what has prompted you to ask us a question. Shagun.

[0:32:12] Shagun Anand: Thank you, Clare. I think while the questions are coming in, I would also like to say that your feedback matters and helps us make these webinars relevant to you. So please let us know if you would like us to address any particular APAC topics in future webinars. You can leave those in the chat as well.

So, let me see. We had a two-part question from Subhikshi. One part was: "In labor market insight, I see data does not show the right picture as it gets clubbed with multiple industries. Is there a way to get or review data only in the financial space or DCC or BFSI segment alone?"

For this part, Subhikshi, although I left your note in the chat as well, we study the skills evolution within a competitor cohort to ensure this analysis is relevant to you. This cohort is designed in consultation with you and us. It usually includes competitors who are similar or relevant to you in terms of their business, type of roles, geographical footprint, and other factors.

The second part of the question was: "What is the frequency? As earlier, it used to come quarterly, but later changed. Is there a plan to share at consistent intervals?"

We have two factors. One looks at the job postings for that skill and role within your relevant competitors over the last 12 months. The other examines how it has grown over the past several years, typically four years. Those are the two-time frames we use when running this analysis, although our skills information is updated almost daily.

[0:34:10] Clare Moncrieff:  So it's true, Shagun, that if anyone went onto our platform and looked at the skills today for a given role, they'd see very live, real-time data. The analysis we showed you on skills evolution is how we use that data to understand the patterns that emerge over time. But if you want a snapshot of what skills look like today, that's very much real-time data in our platform.

[0:34:35] Shagun Anand: Yes. And then we have another question from Varun: Is there a plan to increase the coverage for APAC countries? Yes, Varun, there is definitely a targeted plan to increase that coverage in APAC countries, both in terms of depth of the data and geographical coverage. Alright, so those are the ones I see in the chat.

[0:35:08] Clare Moncrieff: Okay, excellent. Well, this is the opportunity to ask your questions live, so we'd love to hear anything that comes to your mind as you've been reviewing this. I'm sure you can already imagine that the example I gave you for financial services is only one industry. I mentioned that I chose that industry because it happened to be high on the list of industries focused on digital. But all of us are really focused on digital these days. Every company we talk to in almost every industry is hiring for technical skills, and this type of skill evolution data is available for any role that interests you.

So, it becomes quite interesting when you start to think about how the roles are evolving in your organization and what the implications are. I've just been having some very interesting conversations with clients about roles in the HR function, for example, how those are evolving, and what the impacts of new and emerging skills are.

On that note, Clare, actually, there was a question on whether we have the skills evolution for the HR function. Of course, we do. We can do it. Yes. And as you said, Shagun, all of these are customized around the client's request. So if a company comes and says, "Hey, tell us what skills evolution for HR looks like," we will design it for you around what role in HR you are particularly interested in and what cohorts of competitors we should be looking at.

And how can we design that investigation to give you the most relevant skills information? So it's not that we publish a generic skills evolution because that's not how skills work. They're very specific and targeted. So, our analyses are also specific and targeted to your needs and your specific questions.

We'd love to talk more about that. In fact, if you'd like us to give you a snapshot of what an example of that could look like, feel free to click the "Request a Demo" button at the top of your screen. We can certainly set that up and give you some live, more specific examples for your particular questions.

[0:37:22] Shagun Anand: Alright. So I don't see more questions coming in, but we saw one question asking about the relation with job descriptions for this event.

[0:37:41] Clare Moncrieff: Well, let's both take a stab at that one, Chigoon. Job descriptions and job postings are very important factors in our work, and we use them as the basis for much of what we do. Job postings you share show other companies, and us, what you're looking for in the labor market, and they form the foundation of a lot of this analysis.

The importance of job descriptions for you is also that they capture job design: how you've structured the role, how you expect the work to get done, and the kinds of skills you expect people to have if you're going to hire them. Those elements shift over time. They create a virtuous cycle where others' job descriptions can inform you to understand how your own, and even your job design, may or may not be keeping up with the broader labor market.

What would you add to that, Shagun?

[0:38:40] Shagun Anand: Yes. Job descriptions are very key to this analysis. Like Clare mentioned, you are comparing yourselves to the job descriptions of your competitors for this specific role. We also use your job description in conjunction with those of others to compare where you might have skills that were previously core to you, but for other competitors, they are becoming table stakes. Those kinds of patterns are something we can highlight as well. Alright, I will have a quick look through the chat session. There are a lot of questions about whether we should publish reports on skills trends industry-wise, of course.

[0:39:45] Clare Moncrieff: Yes, and we have published a couple of specifics recently. In fact, I think we did one on finance roles in the last couple of months. That's also a great question and great feedback for us because it would be interesting. So, we'll go back. We do publish those reports, as Shagun mentioned. But publishing something more generic than we typically work on might be interesting. Let's see what we can publish for you. In the meantime, please follow up on today's session on our website. We have a number of insight reports that start to address some of these skill-related questions.

[0:40:31] Shagun Anand: While I continue to look at the chat space, I would also like to ask the audience to fill out a poll at the end. You might see it anytime now. As I mentioned, your feedback matters to us, so please share your thoughts on this session through that poll.

[0:41:00] Clare Moncrieff: Great. If people don't have questions now, they might think of them later, and that's okay, too. If you come up with a question after the webinar and want to ask us, you can always reach out, and we're happy to answer those questions in retrospect. If you think about the webinar later and something occurs to you that you wish you had asked, it's not too late. You can always contact us, and we'll happily answer it. If you want to see the tool and understand how it could help you specifically, click "Request a Demo," and we can take you through your specific questions with some real live data.

[0:41:38] Shagun Anand: And with that, let's take a last question. There is a question on how you see the shift from role-based to skill-based structures evolving, and what strategies organizations can use to effectively transition to a more skill-based approach?

[0:41:53] Clare Moncrieff: Oh, yeah. Great question. And it's actually at the top of my mind for so many of our clients right now as everyone's working toward becoming skill-based organizations. It's a difficult transition, so I'm not going to sugarcoat it or explain that it's super easy. It's not that easy. However, it's essential, and technology is increasingly supporting the promise of a skills-based organization.

The critical thing to consider is that shifting to skills-based organizations will likely require you to think differently about the way work gets done, and it will undoubtedly require you to think differently about all the talent management processes your company runs. If you're operating a skills-based organization, for example, how are you promoting people based on their skills or development? How will you know what skills your people have or develop? How will you validate, to my earlier point, the skills your people have and understand which skills are essential to the operation of their jobs or their work?

Many organizations doing this work are also considering whether jobs are the right framework whether they should think more about tasks or projects. And that is a different lens from what we've been doing in the past. When you start to examine the way companies are structured, a lot of it is based on jobs. So it's a pretty holistic change that organizations are undertaking, but a pretty exciting one and a real opportunity to engage employees in their development in a new way.

I mean, when we can use technology to show people that by developing certain skills, they can advance their careers or potentially make more money, that is very compelling for employees and can be an exciting way to encourage development and align that development with the skills your company needs to deliver your business strategy.

So, it's an exciting time, but companies are undertaking a multifaceted set of changes to get there. We're happy to have further conversations about that in detail if you'd like.

Yes, that is just about all the time we have. Thank you for the conversation, Clare.

Yeah. Thank you, Shagun. Really interesting discussion.

[0:44:14] Shagun Anand:

And if we don't get to your questions today, we will follow up with you afterward with an answer.

[0:44:20] Clare Moncrieff:

Great. Yeah, so keep those questions coming in. Thank you for joining. It's a pleasure to be here, and we're looking forward to the next time.

[0:44:32] Shagun Anand:

Bye-bye, everyone.