Work Intelligence Playbook for CHROs in the AI Era

Speaker A: This episode of the Digital HR Leaders podcast is brought to you by techwolf. I was in Ghent, Belgium a few weeks ago at ah, Tech Wolf's AI Day, and I have to say, it really had me thinking about what is possible when you look at your workforce through three the skills your people have, how their work is changing under AI, and where the labor market is heading. My guest today is helping to make that a practical reality for some of the world's biggest organizations. As such, I'm delighted to welcome back to the show Mikhail or Mick Warnu, co founder and, um, president of US at Tech Wolf, who will be walking us through how organizations are combining these three signals and data sets together to get ahead of what AI is doing to work. And Mick's got some really compelling real world examples to share with listeners to bring that to life too. I'll keep it there, but if you're looking to supercharge your skills journey, this episode is one you'll want to listen to, save, and return to later. With that. That. Let's get the conversation started.

Speaker B: Mick, welcome back to another year of the Tech Wolf Digital HR Leaders uh, podcast sponsorship. We're always grateful to you and your colleagues for partnering with us, and it's always a pleasure to speak with you and what I love about having you on the show, Mick, two things. Number one, every time we speak, everything's really changed since the previous time we spoke. But also, um, in my view, Tech Wolf always seems to be three steps ahead of the skills and workforce planning game. So tell me, tell our listeners, um, what's been happening with you and Tech Wolf this past year?

Speaker C: All right. Hello, David. Good to be back. I think this is the fifth time. We probably speak for the sixth time.

Speaker B: I think it is. I think it is, yeah.

Speaker C: Good to see you again. And yes, a lot of things, uh, have changed. I mean, last year, AI took us by storm. It took the industry by storm. It took the workforce by storm. I think it presented a lot of opportunities for us. Around the beginning of 2025, a lot of our customers started asking us, like, hey, Tech Wolf, can you help us understand the impact of AI on the workforce? We know you've been dabbling in the task space. We know you use tasks for skills inference, and you have a bunch of models there. Can you help us figure out how to assess the impact of AI on the workforce? That was like the core use case going into 2025, and honestly now really, really accelerating. What was very different was essentially that Tech Wolf had been in business for I think by then seven years and we had some learnings from the skill space. Uh, we knew that just giving the data set and then saying, okay, let's go to the next customer wouldn't really work. So we launched an early adopter program that said one, we're going to give you very high quality data on your work and your workforce. So we're going to decompose your work into atomic units tasks. We're going to assess the impact of AI on those tasks. We're going to use the best frameworks available, the standard human agency scale and smarter frameworks, but we're also going to help you figure out how to use that for workforce planning. And so in the early adopter program, we've been working shoulder to shoulder with our customers to essentially figure out how do we go from data to insight to action, but more specifically, how do we do AI transformation in a responsible way? I'd say with most of our customers, um, they have a pretty profound understanding of the fact that AI is going to change society as a whole, that companies, Fortune 500 blue chip companies, are in many ways going to set the example for the rest of the world. So on one hand we've been trying to figure out what's truly the impact of, of, uh, AI on the workforce and how do we guide the workforce through, uh, this transformation. It is a people transformation. Ah, you can just see in the data that work is changing both on a task level and on a job level. So it's very clear that the workforce is going to look different. It's also very clear that you will need less people to do the same amount of work. And many of our organizations are taking that as an opportunity to deliver more value to their customers or to reinvent the way they work. And so we've been working with our customers, uh, shoulder to shoulder there, uh, with some incredible results.

Speaker B: That's really impressive. I actually had the privilege of being at your A.I. day, uh, in Ghent at uh, the start of May. And uh, I was part of the skills workshop that you, uh, uh, were running with some of your customers and potential customers as well. And actually, quite interestingly we had uh, gsk, Tanya from DSK was there and we were interviewing Zaka from from gsk, who's going to be on the episode after this one, actually, Mick. So, uh, so it's a nice, uh, kind of link there. And actually there was quite a lot of conversation around the, the Tech Wolf agent that I think you, you launched last year as a pilot, which I think a number of customers have, have been using in pilot and the feedback was, was, was really good. I don't know if you want to share a little bit more about the, the Tech Wolf agent with, with listeners.

Speaker C: Yes. Um, and again this, this very much came from an observation. Um, in our customer base we have about 50 customers now, all really large organizations. Some of them have a people analytics team, most of them don't. And so in many engagements we are seen as the data provider for working workforce. But there was always a problem in going from data to insights to action. Um, in a few cases, for example, at GSK you have an amazing people analytics team at ServiceNow, at a few other organizations you have amazing people analytics teams. But these people are in very, very, very high demand. So you get them on your project for a couple of weeks, maybe a couple of months and then they have to move on. Ah, board is asking questions, leadership is asking questions. And so that was one thing we saw across our customer base. It was very hard for our customers to build a skills intelligence, a talent intelligence, a workforce planning capability, um, that lasted and could stand on its own feet. At the same time we also saw this massive development in agent capabilities where probably around December of this year, which start um, opus 4.5 or 4.6, it became really possible to do longer running tasks with agents. And so if you looked at the capabilities it was very clear that the traditional or the typical uh, coding, uh, agent like chatgpt or Anthropic or a few others, they could do Python, they could do data analysis, they had exceptional data engineering skills. You could teach them about techwolf and the techwolf API and how techwolf, uh, does its data engineering and data modeling and data storytelling. And you could also teach it about hr. So we've essentially built a virtual people analytics consultant or a virtual workforce planning analyst, um, and essentially made that a way to consume the tech pool data. So it's very similar to ChatGPT or very similar to uh, the interface of ChatGPT where you can just ask a question and you'll get a response. So for example, uh, what skill gap do I have? Um, that usually would take weeks to answer, um, maybe a couple of days, uh, once uh, that code was implemented. Now that gets answered in minutes. What is very important though is that we also wanted to stay true to our partner first uh, philosophy. So instead of trying to own the agentic layer, trying to own the front door, we've again said we're going to make sure that partnering and being the smart agent in the back end or the intelligence in the back end, um, is what we prioritize. So the agent is available in copilot, the agent is available through the other agents because we see that in most enterprises there will be a front door for the employees. There will be a front door that essentially coordinates all the AI traffic. And so what we've built is essentially a very easy way for a customer to go into copilot, ask a question around workforce planning, ask a question around AI transformation, and then get a response from the technical region without having to implement another, uh, region.

Speaker B: Very good. And certainly what I heard from talking to customers, uh, in Ghent was that how it's really, really helping them. So great, uh, work, uh, to you and the team on that.

Speaker C: It's, um, it's, it's amazing to see, especially the cycle times. There's a couple of things that we, um, that we realized that we're, that we're now going to double down on. So we've been scraping labor market data for about a decade now. Two billion job postings, I'd say 95% of customers asked us, hey, Tech Wolf, can we do competitive benchmarking? Can we know what XYZ companies hiring? Can we know what emerging roles or emerging skills are taking place in a certain industry? All of those questions essentially took some professional services time. Now you can just answer it. You can ask the question and you get an answer in a couple of minutes. So the perceived value of working with Tech Wolf, working with a skills intelligence vendor has gone up massively simply because the implementation time is now a couple of days instead of a couple of weeks, a couple of months. So that's been really fun just seeing our customers, um, be able to tell the story much more quickly and in turn get much more executive support.

Speaker B: How's your role changed? I think you moved to the U.S. uh, a couple of years ago now, aren't you, and to kind of lead the setting up of the business in the U.S. but there's been quite a lot of, uh, you've been doing quite a lot of different things.

Speaker C: That's been a great fight. Yeah. So I'd say at this point my role changes. Three months. We have a really big team. So I'm, I'm very fortunate that I, that I get to pick my battles. Uh, a little bit around last year, in around the same period that we launched the early adopter program, I also realized that as tech wol, we need to go a little bit deeper and get a little bit closer to our customers. Um, I started reading about four deployed engineers, which are all the hype now. Um, and, and there was something very intriguing about that. At the same time, I also had the realization that we started Tech Wolf as, ah, three engineers in university. We always worked very closely with customers, and you can get a pretty good understanding of the problems that you're trying to solve by just working very closely with customers. But there was always a gap because I felt like I never worked in an HR function, especially for this problem. It seemed like an incredible skill gap I had. So I messaged a few of the Seitros I knew and asked them, hey, can I work for you as a fractional head of AI, or workforce transformation, or as a forward deployed founder? Uh, we're going to do a bigger release around this very soon, but one CTO in insurance took me up on the offer and essentially for almost a year now, we've been partnering and really getting into the weeds of AI transformation, not just as a vendor relationship, but really, again, shoulder to shoulder. Um, I go to their leadership team meetings, I spent time with their team, I flew to Argentina, I made a bunch of decks myself. Um, it was also, it was incredibly rewarding to just do the work myself, but it was also incredibly eye opening to see how big the gap between data insight action actually is. Because ultimately, and I'd say maybe as a more fundamental driver, I felt very strongly about the fact that AI transformation is not just something you solve as a tech vendor, it's a societal problem. And so I feel like I had to transcend the vendor relationship a little bit and just go on site, figure out with customers, um, how to solve this problem. And the learnings have been massive on both sides, really figuring out, like, what does it take to redesign a function? How do you get business from the buy in? How do you tell the story to the CEO? How do you tell, uh, the story to board members? How do you tell the story to the rest of the executive leadership team? And me being there and partnering so closely with that team, obviously also supported by the broader Tech Wolf team, it just created one. A new type of partnership, but incredible support and awareness in the organization. And I feel like we're setting a blueprint on how to responsibly do an AI transformation. Because I think you can take the financial lens, you can take the techno optimist lens and say, oh, AI will create more jobs, uh, than it destroys. It's all going to be okay. But the reality is there are a lot of layoffs happening. The reality is the productivity Gains are real. And so balancing the investor and shareholder demands with humanity and doing what's right, or at least guiding your offers is incredibly difficult. And I want it to be very close to the action.

Speaker B: Now that's fascinating and I think what a great way to, what a great way to learn as well and really get into the weeds. And how has that changed how you see the world of skills intelligence?

Speaker C: So this is so fascinating. In a way, the skills work is downstream of a lot of the task intelligence work, intelligence, AI impact assessment work, but ever more relevant. What needs to happen today? Reskilling workforce planning, buy versus built, buy versus bot. You're doing large scale organizational transformation. I think the most, in a way, funny example is job architectures. Nobody likes doing job architectures. It's, it's expensive, it takes a long time. Um, now almost all of our customers are telling us like we need to revamp our job architecture because we're redesigning work. The work is changing in such a way that we will need to do job architecture. We're launching, uh, something there too. We can double click on that later. But to go back to skills, a lot of the things that we've been preaching around skills based workforce planning around using skills to redeploy talents, skills to understand skill gaps, is ever more relevant, but very much downstream of some of the work that we're doing. Uh, on the AI side.

Speaker B: This episode of the Digital HR Leaders podcast is sponsored by techwolf. The world of work is being rewritten faster than HR systems can keep up. Skills age in months, roles get redesigned quarter by quarter. Chros have quietly become AI transformation leads. And the data they need to lead it doesn't exist in any HR system. That's why the world's most forward looking enterprises have built on techwolf. Techwolf is the data layer for the AI era of work.

Speaker A: It connects three data sets that have never lived together.

Speaker B: The skills your workforce has, how their work is changing under AI and where their labor market is heading. Skills intelligence, work intelligence and market intelligence in one layer. HSBC, AMD T Mobile, GSK ServiceNow, Pfizer and uh, many more rely on Techwolf to deliver measurable impact, including cutting time to a unified skills foundation from 18 months to three, servicing 800 deployable internal candidates in under 30 days and unlocking, um, more than $8 million in projected L&D savings at one global biopharma. If skills work and labor market data is what's standing between your enterprise and its AI transformation, talk to techwolf. The Data layer for the AI era of work, visit techwolf AI. That's techwolf AI. You've got like a, um, I think there's like a three signal framework that you've got skills intelligence, market intelligence and work intelligence intelligence. And I'd love to deep dive a little bit on the work intelligence piece, which you highlighted earlier, as I think there's still probably some confusion in the market. Um, this is another way of saying skills, but I know the nuance here is that, as you said, it's actually more around the tasks that people actually do at work. So the tasks that typically make up a job and what happens to those tasks when AI starts to change how work gets done. Talk to us more about how you're seeing some of your customers gather and use that work intelligence.

Speaker C: Yes, and so this also happened somewhere last year where all of a sudden there was a debate between, uh, tasks and skills. Or should we do tasks or should we do skills? I think you should do both. Um, there are two incredibly valuable data points to understand your work and your workforce. I'd say what we're seeing right now in practice is that to understand work task and the task lens is a little bit more useful. To understand your workforce skills is a little bit more useful. But what we do today is saying, let's give you both data points to understand your work for your jobs and your workforce, uh, your employees or your resources.

Speaker B: And I guess the market intelligence is, is the external side, what's happening in the marketplace, because you need to understand that as well.

Speaker C: Exactly. So the way we're helping our customers right now, the way we're positioning Tech Wolf, is as a data layer to help you make better workforce decisions. Field intelligence helps you look inwards. Um, work intelligence helps you look forward, and market intelligence helps you look upward. Three lenses to look at the workforce, all equally relevant. I don't think it's an or conversation, it's an and conversation. And, uh, in practice, that's also what we're seeing specifically on the work intelligence side, our, uh, customers are very much at different maturity levels. I'd say the first step is really understanding the impact of AI beyond the high level. Oh, we have 20% productivity gains here and here and here. Really understanding on a role level what is changing and what does that mean for, um, the workforce? In a way, it's a combination of a workforce planning exercise and a work redesign exercise. The workforce planning component is trying to model and forecast, given the existing workload we have today, how will work change and how many people will we need to do the same work if we implement AI? That's step one, uh, where you deconstruct work into tasks. You try and figure out what the impact of AI will be. That's the theoretical feeling in a way, like this is what we could do. You factor in what the organization has already done and then you factor in how quickly you can implement uh, AI. And so a lot of this work goes from theoretical to practical by taking in some organizational data, uh, points. And that's really on the strategic workforce planning side where you're really trying to understand how is my workforce going to, going to change? On the work redesign side, it's really trying to understand how our roles evolving and changing. So customer service is a good example. What we're seeing in the market, what we're seeing with customers, is that the customer service role is bifurcating into probably three different roles. So one is the human specialist, the senior customer success or customer service person that really handles the escalations. If AI does a lot of the triaging because the easier, um, the easier requests, then the harder requests go to the human specialist or the more senior customer service person. Then there's the AI augmented customer service agent, simply somebody that needs to learn to work within the tools. And then there's the automation operator. Obviously these job titles will change, but it's a role that we're actually seeing in startups in bigger companies across every function, somebody that is essentially going to implement AI, uh, in a way like uh, a very decentralized way of doing uh, IT or tech. It's essentially a tech person embedded within, within the function or within the, within the group that is trying to figure out how do I automate. So that's the, the work redesign, uh, component modeling, how work is changing. So we're helping customers with both. One is really understanding how work is going to change. Two is then figuring out uh, what, what that means for, for specific jobs. Bringing it all together is then how do we redeploy and reskill people to those new roles? And how do we for example use natural uh, attrition or performance management or retirements or hiring freezes to get to that target straight, which is maybe from 700 people to 500 people over time. So with one of our customers we found that natural attrition was very high in customer service, uh, over over 20%. And so by simply saying, hey, we're going to backfill a little less over three years we can get to our productivity target without any um, without Any reductions in force. And so um, that is one incredibly rewarding, um, no layoffs. I think that's a very noble cause to work towards. But we're also helping our customers redesign work into I think more meaningful work. That's say the tldr.

Speaker B: I'm curious, how have you seen customers using this to their advantage? Have you got any um, case studies that you can share with us?

Speaker C: Yes, I think the example that I just presented with SERGIS now at their conference, K26 was a combination of all three signals. So we're moving towards an AI native go to market organization within that organization for deployed engineers are really important. The problem is a lot of organizations are now hiring for deployed engineers. It's uh, the hot new role that is in short supply. So customers and service now are trying to figure out how do we maximally redeploy and reskill people into, into that role. So step one was figuring out like how is the forward deployed engineering role defined? And the forward deployed engineering role is essentially a technical account management role combined with a little bit of engineering combined with a little bit of um, customer service skills. You go on site, you implement the solution, but you're also savvy enough, customer oriented enough to, to hold the conversation with your customer. So you're a little bit of uh, both a very rare breed of person, uh, somebody that is both technical and commercial. Uh, Palantir um, pioneered this model and now a lot of organizations are adopting this model. So we help to understand what does that role look like, what are the skills that are needed in that role, what are the tasks, what are the different archetypes of roles? Uh, within the, for deployed engineering uh role there's seven different archetypes. We help them understand those different archetypes and then we use skills data to help them figure out who can we redeploy internally. Um, they have their talent signature, so they're combining behavioral data, performance data, obviously the assessments that they do internally to essentially go from a thousand possible candidates to a uh, couple hundred. But these are people that are now actively being reskilled and redeployed, uh, with massive cost savings, uh, both on the recruitment side but also very specifically on the ramping side. These are people that don't have to be on board. They know service now and they can be deployed uh, really really quickly. With a couple of customers we've been able to avoid a layoff, um, using the framework that I, that I just described and with other customers it's really trying to figure out how do we drive AI adoption. How are we making sure that this task data and this understanding of how work is changing ultimately goes to the employee for them to get very actionable feedback on how to use AI?

Speaker A: I want to take a short break

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Speaker B: forward/program and join our group of global leaders. That's really good. And then the sort of next layer of that, uh, is obviously as you said and as I know and some uh, listeners know, you know, with Tech Wolf, Tet was not a platform as you said. It's, it's the data layer that allows organizations to maybe get some better return on investment from some of the technologies that they've already invested in to where does this intelligence actually live then? On a day to day basis?

Speaker C: Yes. Um, same as before, really, really big focus on partnering. So we're servicing it into employee work. So that's ServiceNow's uh, agent layer. We're getting it available in Joule and Sana. We're getting it available in Copilot. The idea is still the same. Uh, we don't want to own the uh, agentic layer. We want to make it very easy for our customers to consume the insights that Beckwolf, uh, brings. There's plenty of amazing agents. Uh, the agent to agent protocol is something that maybe is interesting to double click on. Um, it's essentially a way for agents to communicate. So, um, let's say you have ChatGPT. You ask ChatGPT a complex question. Hey, can you design me a workforce client for my customer service function? ChatGPT could answer that by itself, uh, using the data it has, or via the Agent to agent protocol, it can look in its own catalog and say, hey, maybe I have other agents that can give me a better answer to this. In this case, that will be the Tech Wolf agent. And the Tech Wolf agent does the calculation, um, and then feeds it back to um, to to, to chatgpt. So in a way it's a manager employee type of setup. Um, the managing agent or the manager in this case is the, the agent of choice that the customer has. And uh, the techwalk agent just does the calculation and ah, feeds all of the information, the visuals back to the customer. And we're pretty confident that that is going to be the paradigm. If you look at Claude Cowork, if you look at Codex, um, these platforms are building essentially front doors to work. We don't want to compete with that at all, but we want to make it very easy to consume the insights that we provide.

Speaker B: So I mean we can see why Tech Wolf is growing as a company providing that task intelligence, uh, work intelligence as calling it as well. And obviously we mentioned earlier Mick, you've been in the US now for, for a couple of years. Um, and I know because we work with many of the, the companies that you're working with at Tech Wolf that you're really expanding into the U.S. um as well. Um, and, and I'd love to you know, in, in that North America market. I'd love to hear though, given your heritage as a, as a European company and you are a European headquarter tech company, which is great to hear in this day and age. Do you believe that companies in North America come at this problem differently to their counterparts in Europe?

Speaker C: The North American society is structured very differently to the European society. And something I had to learn. All of us, we consume the same media, we're all raised in a way by Hollywood and we have this idea that the west is this uniform um, area or at least uniform, uniform culture. Especially uh, with the, with COVID and the digitization, it's very easy to trick yourself into thinking that the cultural differences are not that big and profound as they are or centralized into the things that we hear in the media. So oh, healthcare is very different or education is very different, but being here and living here, you just see that society is structured very differently. Um, in Europe the government takes care of you in many ways. There's a lot of regulation in food, there's a lot of regulation in chemicals. There's in, in many ways Europe is, is a very safe place and I think it translates into how businesses are built up, uh, and it translates in, in how some of the innovation is adopted. In the US Everything is more competitive. A business is much more competitive. Organizations are adopting AI uh because of this competitive landscape and so because of that competitive pressure that is much, much, much more present in uh, in the U.S. i think also because a lot of the companies are listed, a whole uh, or a big chunk of the world has money invested in the US there's just a much bigger focus on, on results. So that's, that leads to AI being adopted way more quickly. There's also less regulation uh, around workers. And so the equation is very different there with the European counterparts I think one, there's the regulatory shield that is just preventing uh, innovation in AI from seeking in or uh, being adopted at the same pace. Even when I came um, to the US two years ago, I realized, wow, this AI train is going so much faster than I anticipated. And I was an AI founder so it was a big wake up call. And apart from the regulatory shield, there's also uh, the strong employment, uh, law and employment protection that also prevents drastic or more drastic um, more drastic moves. And so yes, as a second or third order effect, uh, you see ah, a difference in people strategies and how organizations um, are approaching this topic. The more time I spend here, the more nuanced. I think the answer to this question, uh, to this question is I think as a society we're figuring out this new paradigm. It's very clear that it's going to change society and I think the different areas in the world will adopt it differently based on the existing culture.

Speaker B: So when some, and it might, this might apply to some of our listeners as well. So, so when some practitioners hear AI skills, intelligence, workforce intelligence, market M intelligence, they may think that this entails a really complex transformation program, large investment and significant risk. I know it's not the case because I've spoken to you and your colleagues many times before. But is that exactly what it takes to get started or is that a bit of a, of a misconception? Because I think you've run a number of successful pilots for some of these big organizations first before they, they scaled.

Speaker C: I think it's a misconception because in the old world if you wanted to do any type of data collection in hr, that meant a survey, that meant bothering in a way, um, employees, people that are really busy. Especially now, everybody working very hard with tech and obviously also a few, a few other providers. We're taking a different approach. We're really modeling the workforce and we're giving HR data to base some of the decisions on or to essentially help them focus their efforts. So step one is definitely not a big transformation. Step two, once you start redesigning work and redesigning the workforce, you're not doing that all at once. Um, we're seeing that a Lot of this work is being aligned to existing technology implementations. So it's not saying, oh, this is the effect of AI on the workforce. Um, dear business leader, uh, we're going to go and transform your work. No, it's aligned with technology implementations. Those technology implementations, um, are happening. And so our customers that are most successful right now are essentially trying to figure out what are the company's big AI bets. How do I align my work with that, how can I be proactive, um, with that and, and go to business leaders and say, hey, look, we see that you're investing either a couple hundred million or um, a lot of time, energy resources in ABC AI bets. This is the impact on the workforce related to that bet. We would like to redesign the workforce and the jobs in parallel. And that's a completely different conversation. I'd say one of the most practical and actionable examples of being proactive, being strategic and being, being uh, being a true partner.

Speaker B: Yeah. And for organizations that have got started with you and actually we've got a fantastic example in next week's episode with Zaka, uh, farhat of JSK. They've been on their 18 months into their, into their skills journey. So we'll all hear from them. Um, and she talked about a number of things that being important such as leadership alignment. And I think that really talks to what you shared with me previously. Mika, Mick, in previous episodes, you know, what's the business challenge? What's the why, what why, why do you want to do this? But that leadership alignment, that single data layer which obviously you can help provide governance around this, you know, such as having uh, uh, a, uh, skills, uh, committee, you know, and stuff decommissioning. So using an investment in this to, to actually reduce the amount of investment, uh, in other, in other technologies or platforms and the HR capability build as well. And I thought that was a really good way of sharing that. And hopefully people listening to this now will be waiting eagerly for next week's episode to find out. But I'd love to hear as well. Um, Nick, you know, some of the other companies that you've maybe got started with, what does it actually look like in the early stages? What, what do they do first?

Speaker C: So it depends. Right now we still have two offerings. So on the skills intelligence side, I think we've spoken about this many, many times, they pick a pocket of your organization. Usually it's R and D technology, uh, sales, uh, a strategic pocket of the organization to do this work with. And it's aligned with a business Poland, I think what is different now is the fact that AI is in many ways everybody's business problem. The AI transformation in some way shape or uh, form is um, is is affecting the business on the work intelligence side. It's in um, in many ways a little bit easier being we're always aligned with, with the big AI transformation. Step one is assessing the impact of AI on, on the workforce which gives you a lay of the land and ultimately that feeds into a buyer built decision, um, for very tactical roles for future roles that they want to hire or uh, a more strategic conversation with leadership about what is the workforce strategy that we're going to um, um align with the business strategies. Um and there's probably two types of businesses, uh businesses that are more in a steady state, slow to medium growth which uh, which are historically uh seeing headcount grow as a proportion of, of revenue. So maybe we need X percent uh more more workforce to have Y percent more more revenue. And then you have organizations that are really in high growth modes, uh semiconductors, um, everything that is, that is happening in, in, in the AI stack or any company that contributes to AI stack. That is how can we still keep growing or growing, grow a little bit more quickly but just deliver more value to our customers. So depending on where the customer is, different use cases emerge. But ultimately for both it's figuring out how can we redeploy reskiller people um based on what we know AI is going to do in the workforce. So it goes to the LND transformation or the upscaling and rescaling and it goes to redeployment. Those are probably the two big and then the very operational stuff such as redesigning job architectures. Um, I'd say that's downstream of that. But more and more we find ourselves uh, helping customers uh across the world. Uh SLB is a good example. Ah, we just uh, started working with um, an Italian pharmaceutical company where we're helping them with uh, their job architecture. So uh, it's on one dimension a very broad range of problems that we're helping to address. On another dimension it's all related to a big workforce restoration.

Speaker B: Very good. So I mean I think you gave some clues to the next question here for people listening. You know, I'm sure we've got HR leaders listening to this thinking this really resonates with what my company needs. You know I want to move forward, I want to move forward on this. Um, where should they start? What's the first conversation they need to have internally? Presumably it's with a Business leader.

Speaker C: I'd say it's fair to say that an AI initiative is happening. Getting a good sense of what the big AI bets of the company, of the company are, um, and then how, uh, then it's about figuring out how do we align the people strategy with that. Going back to um, the workforce planning conversation. Workforce planning used to be about really predicting the future. That was very hard. What are the future skills in energy? What are the future skills in oil and gas and technology and 5G. Very hard to do. I'd say workforce planning changed because essentially now you're modeling a technology adoption. Um, the technology is already here. Um, in many ways the future is already here. It's just unevenly distributed. So companies are trying to figure out how quickly can we adopt this technology. And they know the end state somewhat, uh, once they, once they adopt that technology. So workers planning in a way is not about predicting the future anymore. It's just a, uh, planning exercise. So workforce planning exercise, put some, put some threading through. So I think after having a good sense of what's happening on the business side, it's, it's creating that workforce plan, a strategic workforce plan. You've spoken about the strategic and strategic workers planning for a very long time. I'd say right now it, it very much seems possible and it's very, very, very, very good.

Speaker B: Got a couple more questions going to do the, the question of the series Mick, and then, then they're going to ask you about what's next for Tet Wolf, what your future plans are there as well. So question of the series. So we're asking all the guests in this series, which um, you and the team at Tech Wolf are kindly sponsoring. It probably talks a little bit to what you've already talked about a little bit. So please feel free to summarize or obviously add something new as well. Where should HR leaders, where should chros start if they want to turn AI into real impact at work?

Speaker C: There's, I'm trying to remember who said it. CTO that I was talking to said, look, I see my own mandates as having two big pillars. One, it's driving operational efficiency in my function. That's AI in hr. But the broader part of my mandate is really figuring out what AI is going to do for the workforce. That's an org wide mandate. She said, in this case I have somebody in my team figure out how to implement AI in HR to drive that operational efficiency. I am really focused on making sure that the workforce uh, can navigate this transformation successfully. So I'd say depending on the function, uh, and the HR teams makeup. Those are two good starting points. I'd say understanding the impact of AI on the workforce and on work is a no regret move. In Tech Wolf, we like to think about no regret moves. I'd say figuring out what the impact of AI is, how work is going to change, what it means for all of the talent processes is a no regret move. Because one thing is clear. AI is going to change work for, for every organization and it's already happening. Really good.

Speaker B: I think you're right.

Speaker C: I think that, I think in many

Speaker B: respects HR has a unique opportunity here. As you said, it's not just about reinventing the function. It's about helping the organization be successful as it goes through transformation as well. And I love the way the example you gave there with that Chro. I've got someone operationally looking at hr. I'm looking at the business. Very good. So Mick, before we wrap up, you know, what's next for Tech Wolf? What's next? What are the plans moving forward? What are we going to be talking about next time?

Speaker C: Uh, what are we going to be talking about next time? Good question. Um, um, we're realizing that, um, and this might seem very obvious, but this is really the time to get very close to customers. Um, our development speed has gone up significantly. You're getting the sense that we can help the customer solve a lot of problems around work and workforce. I'd say that's probably one of the biggest changes we see going forward. Instead of saying just skills or just tasks, we really want to help our customers understand work and workforce. And uh, we want to help them make the best possible decisions for the work and their workforce. And the way we're going to get there is essentially embedding ourselves deeper with the customers, figuring out what problems we can solve. I think job architecture is a very good problem. We had one of our product managers who sat down with partners with our customers and said, look, tell me all of the challenges you have. We're going to map out the process. We're going to build a solution for you to not automatically generate a job architecture, but at least make the entire process go from a couple of weeks of the variation, even a couple of years, to let's create a draft in a couple of days and then we can validate with, uh, with a consulting partner or internally. Um, and so to compress that entire process, that is manual boring work, into something that supports a work reinvention. So we're seeing many more of those Opportunities to solve either very tactical or more strategic problems. We're also seeing that token spend is becoming part of um, the workforce cost. I think that's probably one of the biggest trends that we're looking at. I think I recently heard that Salesforce spent 300 million uh, on tokens. I'm not sure if it was in the last year or this year. Regardless, probably the biggest non workforce line item. So I'd say in the past we've mostly helped our customers with buy versus build in subsense. I'd say in the future buy versus bots or optimizing token budget. Figuring out uh, how we can model that line item in the workforce is going to be something um, that we're going to be experimenting with uh, with customers and then just make it very, very easy for customers to get value. I think that that's been the focus this year. I think with AI, um we see that we can identify some of the services. So things that used to take weeks can now take days. And so it's just easier for customers to uh, tell the story. The TechWave agent is a good example. Questions that would take weeks to answer uh, now take minutes. And so that makes it just so much easy, so much easier for customers to um, achieve their goals. So more, more of uh, what's been working in that sense.

Speaker B: It's been an absolute pleasure. It's always good as I said at the start, it's always good to catch up and talk to you uh, on the show. Can you share listeners how they can follow you? Because I know you do a lot of um, stuff on LinkedIn as well, uh, and all the great work and how they can find out more about Tech Wolf as well and maybe any upcoming events that you're going to be speaking at uh, in the near future.

Speaker C: So there's the big HR conferences. We have the SAP conference, the Workday conference, HR Tech, the, the Gartner conferences. We will all be there. We have a very busy September and October, uh ahead of us. You can follow me on LinkedIn. Um, we also have a podcast. Not to compete with your podcast, but I think Julius is doing some amazing, amazing, amazing work.

Speaker B: He is, he is doing some good work.

Speaker C: We, we want to give our customers um, the opportunity to share their story. Um, I think Julius has, has done a phenomenal job at not only elevating some of our customers, uh, and their voices, but also just thought leaders uh, in the space. I think that the Tech Wolf insights are obviously relevant but I'd say the cool part there is really giving the floor to people who are doing this work, uh, in the field and in the trenches. So definitely check out the uh, Techwolf podcast and follow us on LinkedIn or go visit the website@techwolf.com great.

Speaker B: And I'm sure I will be seeing you at some point, um, at one of those events or unleash or something else as well, Mick. So, uh, thanks again, um, for being a guest on the show and yeah, see you soon.

Speaker C: See you soon. Bye Bye.

Speaker A: Thank you again to Mick for joining me today. It really was a fascinating conversation, one that I am sure will be an eye opener for a lot of our listeners. For those of you who are listening, I'm curious what stood out for you the most from today's episode? Is there anything you would like to add to the skills conversation? Come and find me on link, LinkedIn and my post about this episode and let me know your thoughts and perspectives in the comments. I read every single one and the conversations that happen there invariably build on the one I've had with the guest on the show. And if you think a colleague or friend would get something out of this episode, please do share it with them. It really helps us bring more of these conversations to HR professionals across the world. And one last thing before we go, for those who would like to keep up with what we do doing@ uh, insight 222, follow us on LinkedIn or head to insight222.com you can also sign up for our bi weekly newsletter uh@myhrfuture.com to get the latest thinking on Ah, HR people, analytics, AI in HR and everything shaping our field.

Speaker B: Right.

Speaker A: That's us for the day. Thanks for listening and we'll be back next week with another episode of the Digital HR Leaders Podcast. Until then, take care and stay well.