In this episode of the podcast, Mikael Wornoo, Founder and COO at TechWolf, speaks about how AI and natural language processing is helping to solve a perennial challenge for organisations, namely the process of gathering skills data automatically, continuously, and objectively to understand the skills you have, the skills you need and the gap in-between. 


Many of the conversations we have on the Digital HR Leaders Podcast, centre on skills. Perhaps this is not a surprise given that according to PWC, 74% of CEOs are concerned about the availability of key skills, are worried that the shortage of talent will constrain growth. Also the World Economic Forum’s, Future of Jobs 2020 report, predicts that by 2025, 50% of all employees will need to be re-skilled as 97 million new jobs emerge and 85 million jobs will be displaced by a shift in labour between humans and machines.

This pressure translates to HR, with Gartner finding that the top priority for HR leaders today, is building critical skills and competencies for the organisation. And finally, our own research at Insight222, into the future of workforce planning, found that while nearly all companies want to build a skills based workforce planning process, only a quarter are actively doing so.

Throughout this episode, Mikael and I discuss these topics and others, including:

• The biggest challenges facing organisations in gathering and utilising skills data

• How TechWolf helps customers build a single skills taxonomy in just eight weeks and how this can be used to power technologies that drive employee learning and mobility

• How companies are using skills data to solve a myriad of challenges and help the business effectively see around corners

You can listen to this week’s episode below, or by using your podcast app of choice, just click the corresponding image to get access via the podcast website here.

Support for this podcast is brought to you by Techwolf. To learn more, visit techwolf.ai.

Interview Transcript

David Green: Today, I am delighted to welcome Mikaël Wornoo, Founder and COO at a really exciting HR tech company, TechWolf, to The Digital HR Leaders podcast. Welcome to the show Mikael, can you provide listeners with a brief introduction to you and TechWolf?  


Mikael Wornoo: Hello, David. I am Michael or Mikael. I live in Ghent, the Silicon Valley of Belgium, some might call it. I am the Founder and COO at TechWolf. I have a background in computer science and artificial intelligence. In my free time I am obsessed with boxing, apart from solving the skills challenge, of course. And TechWolf helps enterprises in transformation, understand what skills and skill gaps they have in the workforce through AI. So in essence, you should think of it as instant and on demand skills intelligence, that can help organisations navigate all the waves of change from a people perspective. It is good to be here.

David Green: Well it is great to have you, and it is a topic that I know will resonate with many of our listeners, we will dig into that as we go. 


I am quite interested in Mikael, how did you end up making the leap from academia, you talked about your background in computer science and AI, to founding TechWolf, what's the story behind it?  


Mikael Wornoo: A great question. Essentially it is all about impact. In machine learning or when you are doing research, you are looking at the technology and at some point you are looking or you are thinking about what problems can this technology actually solve. And once you start looking at the business, rather than thinking about all the problems you can solve, especially with natural language processing technology, you really realise that the biggest impact you can have, especially with this particular skillset is when starting a business and when helping organisations solve the challenges.

The skills challenge on its own is such a big, hairy challenge, if we can solve it, we can impact so many organisations, but also so many people at the same time.

David Green: That is great, as I said, we are going to dig into that now. We do quite a lot of research at Insight222, over the past a year, into this topic, in fact it is longer than the past year, actually. So we work with 80 global organisations, as many listeners will know, principally working with the head of people analytics, and we have seen a trend where A) strategic workforce planning is very much coming under the people analytics function, in these organisations. There was a big challenge that we found, when we were speaking to many of the organisations, around getting a more skills-based approach to workforce planning. So in this research that we did last year, our results showed that 90% of those organisations expressed the desire to build a skills-based workforce planning process, but only a quarter were currently doing so.

From your perspective, obviously working in this space for the last three years, why is this interest in a skills-based view of the organisation surging? 


Mikael Wornoo: Yeah, and I think you could have a thousand answers on that question. So let's say from one side you could look at it and say, traditional industrial companies, manufacturing companies, they know what is in their inventory and what equipment that they have. So if your company consists of knowledge workers and provide services, you should just know what is inside the head of your people. That is one thing.  

Then if you extend that analogy, an industrial company wants to start doing preventive maintenance, under equipment, they should know what equipment is being serviced right now. So if we want to do preventative maintenance on our knowledge, proactively re-skilling and upskilling our people, we have to know what their knowledge is first. 


And for me a skill is just the fundamental building block of knowledge, fundamental building block of 21st century work. If you will. I can’t really pinpoint one factor, it is more of a combination of, what I just said, and multiple transformation drivers, impacting the business at the same time.

So, think about faster innovation cycles and digital disruption, the supply chain shortages we are seeing right now in the car industry, ship industry, due to COVID. Cybersecurity threats, ransomware attacks are really just the beginning of what is going to happen in the cybersecurity world, but also things like the need for a carbon footprint reduction ability and sustainability issues.

They are all catalysts for rapid transformation and they are all contributing to a, ever-present, skills challenge. Organisations need to upskill and re-skill, they need to product and figure out what their critical skills are at the same time, your skills shortages in STEM. 


So to handle those effectively, you need to know your as is states to do any preventive maintenance, you need to know what you have first. So that is my thoughts on that. What do you think?  


David Green: It is interesting, it is almost like a perfect storm at the moment. Isn't it? I mean, obviously we have been talking about the future of work, which is a bit of a trite statement sometimes, but Davos, The World Economic Forum, have been having quite significant talks over the past few years about the fourth industrial revolution. And obviously serious concerns from countries and leading organisations about automation. On one hand, automation making certain tasks perhaps redundant within organisations, but obviously this new technology creating a whole host of new responsibilities, new jobs, new tasks as well. 


I can't remember the exact data, I might quote it in the introduction to the podcast, The World Economic Forum estimates that of the jobs that will be displaced by automation, far more jobs will be created. Which again, if you look back in history, that is what happens typically in an industrial revolution. 


It all seems to point towards skills, doesn't it? And organisations moving away from jobs as the currency, to tasks and skills as a currency. Almost deconstructing jobs, effectively. We had John Boudreau, on the podcast a couple of years ago, and he was very much talking about that. 


It is challenging, isn't it? A lot of the practitioners that I speak to talk about the need to create a skills taxonomy, which I know is something that you help organisations to do.

What would be great actually is if you could talk about firstly, what is a skills taxonomy? I think that would be helpful for some people listening. 
Why should organisations have it? And then if we look at the traditional way of doing things, how companies are currently building, or typically building, skills taxonomies?

Mikael Wornoo: So essentially for me, skills taxonomy is just an overview of all the skills you have in your organisation. 
And why, I think for all the reasons we just said. If you want to start thinking about your workforce, in a data-driven manner, you need data about your people and the fundamental data piece is that skills data. And then on the other hand, actually what I hear most HR leaders tell me, is that they have no systematic way of obtaining that skills data, that skills taxonomy.  

So most approaches today are a combination of asking the employee to use self-assessing tools, surveying and Excel files. It is subjective, time consuming, and frankly, impossible to keep up to date, while you want that on demand overview, it is impossible to have an up-to-date overview of the skills. We, as humans, are prone to all kinds of biases, recency bias, being the most important one.

To give you an example, A CHRO told me that a year after implementing an employee experience platform, and after multiple nudges to employees to complete their profiles, 64% of employees were struggling with self assessing their skills. So actually they notified the organisational leadership that we don't like this process, had about 11 skills on average, generic skills, project management, communication skills, the type of stuff that everybody puts in.

So on one hand, it is crucially important to know what you have in your organisation, on the other hand, there is no real systematic way of obtaining that skills data. And where data is held, it is unstandardised in different formats, so you can't really do anything with it.  

So before the rise in AI based applications to tackle this specific problem, it was also the only way to get access to skills data. So like you said, it is the perfect storm, but we also need a way to get that data to master that.

David Green: Using AI, and big data, and analytical approaches, how can you gather that skills data without asking employees? Because as you said, that is time consuming, it is subjective, it quickly gets out of date, and actually just doesn't work, you never get the data that you need to analyse. 
How does big data, machine learning, and artificial intelligence help?

Mikael Wornoo: Apart from making it feasible to do in a sustainable way, I see three big areas where AI makes the biggest difference. So AI can do more with less, it can scale the process and speed it up, and it can interpret and consolidate multiple different data sources.  

An AI algorithm can actually learn that if you, let's say you sold software to a Fortune 500 company, that you can do obviously enterprise sales, that you can present a business case, that you are probably a good storyteller, you know how to negotiate, that you know stakeholder management. A human can only, typically, self-assess enterprise sales and the sales skills. So a lot of information is lost there. Using the AI, you can get a richer image. If you compare the 11 skills that you get, on average, by self assessing, you get 34 or 33 on average with AI.

Talking about scaling and speeding up, it doesn't really matter if it is a thousand, five thousand, fifty thousand, 200,000 employees, AI doesn't really get tired. So it is just the same thing for all of our customers, essentially, we have been able to give them that initial snapshot in under eight weeks. So you can not expect to get that with a survey.

And then lastly, you just have the ability to interpret multiple different, implicit data sources, skill data sources. 
So that is the external labour market data, your learning history data, your career history, personal development plans and ambitions. But as I said, you don't need all of that. You can just start with, let's say someone's current job title, and already have quite some information on what somebody’s skillset is. 


So in short, it just gives you access to skill data without surveying people, by looking at implicit data and translating that to skills.

David Green: And as you just said, you can do it in eight weeks, which is pretty amazing when you consider how long it takes to do it manually. 

Mikael Wornoo: Yeah, exactly. And then the skill doesn’t really matter. People always think that they don't have enough skill data, but when you start looking at skills as a data problem, you start thinking about, okay, what are the data points we have for everybody and what is the data point that is a relatively good proxy for someone's skillset. That’s their current job title.

So one of the things we do is, we specialise in translating people's current job title to skills. But a problem we see is that organisations have 2,000 job titles for 4,000 people and they think that they always need to start consolidating before we can even start using AI and that is not true. You can actually look at those job titles and by using labour market data, we can already translate every job title in the organisation to associated skillset. So even if you have just one job title, you already have a great proxy for someone's skillset and you can start using that.

David Green: A couple of questions on that. So firstly, how do you validate that data?

Actually we had Diane Gherson, who is the former CHRO at IBM, on the podcast a year ago and she said something that was really striking. She said that they infer skills using similar technologies to how you described. I think it was 360,000 IBMers, it might have been 350,000 employees and when they went out to ask employees themselves, whether they captured the information correctly, I think 80% of those said that they captured it 100% correctly, which sounds quite amazing. 

Is that a similar sort of thing to what you do, in terms of how do you validate the data that you are collecting and inferring? 


Mikael Wornoo: Yeah, exactly. One of the big things we saw, when looking at this skills problem, is that we didn't want to build another front end application. So what we do is we push skill data back to the systems that employees actually use. So if your talent marketplace needs skills data to operate well, then just push that skills data to the talent marketplace. And then you can look at the data in the talent marketplace and see how employees are validating. You can A/B test the adoption of your talent marketplace without inferred skills in there.  

So yeah, we are seeing similar things. I think it was Josh Bersin, who said, the cat is out of the bag, it is really possible to infer someone's skills with AI, even with limited data. 

David Green: Because one of the other challenges I hear from practitioners is, we have effectively got several skills taxonomies and all the different technologies we use, but they don't necessarily talk to each other. 
And a problem you are solving is you are helping integrate all of that data together, so that they enrich each of those systems with up-to-date and valid skills data. Am I correct in my understanding around that?  


Mikael Wornoo: Yes, exactly. A lot of tools have their own ways to talk about skills, people have their own way to talk about skills. If you ask people to describe your own skillset, you will get a thousand different forms, so that is where natural language processing technology comes in. Understanding that language, whatever language it may be, and translating it to skills. And then it doesn't really matter if you use taxonomy A, B, or C, you can just consolidate all the information.  

It is also important to not get carried away with what the skilled taxonomy is. Essentially a skill taxonomy is a tool to help look for skills, and it is also important that you know which business problem you are actually going to solve with that skill data. 


Most organisations are already getting carried away with what skills taxonomy they should use, but I really see the organisations that use a pragmatic approach towards that skill taxonomy, and focus on the business problem they want to solve, just provide value more quickly.  


David Green: Yeah, so effectively if you have got business challenges related to skills, workforce planning, helping organisations to see around corners, then obviously the work that you do helps answer those questions. 
But as you said, lead with the business question not with the data, the first rule of people analytics I have heard it described as.

When we last spoke, you described something which I think is a nice little quote, skills is a data problem, not a tooling problem. 
You have talked a little bit about that already, can you expand a little bit more on that specific quote?

Mikael Wornoo: Yup. Exactly. We have essentially given part of the answer already whilst talking. Every HR leader I talk to regarding the skills topic, tell me the same, we simply don't know the skills of our people. 
So even with the Lamborghini or the Rolls Royce of the HR tech landscape, even with the best tools, they don't manage to get a continuous overview for skills.

So the solution can not be putting another tool in front of people, it cannot be just asking people for skills data, we have tried that, it doesn't work. It is about leveraging data that is already present.

And when looking at skills as a data problem, many interesting things pop up when thinking about the solution. We realised that there is plenty of implicit skills data lying around, and you just need to talk to those systems and grab the data and translate it to explicit skills data. 


You also realise that it is not just the HR systems, but your general digital footprint. People don't work in Workday, or Degreed, or Gloat, it is also not the place where they are going to create the bulk for skills data. So you also need to look into systems where people actually spend their day. 


So that is what we do. We are approaching it as a data problem. We specialise in creating AI, natural language processing, that can translate all those different data sources and translate that to skills. So we have built technology for just that particular use case. And sometimes it baffles people, like why would you do that? Why would you just focus on getting to know the skills of people? Because we believe that skills data will be an instrumental piece in creating smarter technologies, a competitive advantage, and also more engaged employees.

Even just this year, there are two types of platforms already that that really needs skills data, the learning experience platforms and the talent marketplace. In the future, that will probably be project tools and the number of tools that will rely on skills data, or data on what you can do to perform properly, will only increase.

So skills data is going to be the key to unlock more engaged employees, but just in general, a better employee journey, a better employee experience. 
It is already beneficial to the entire employee journey. So employees expect organisations to assist in their development and their career development and that is what skill data enables.

David Green: It is really powerful, isn't it? You were very humble about what it helps to do, with the stuff that you do at TechWolf, but a lot of these HR tech systems can be quite expensive and effectively, by providing the skills data you provide the fuel to these systems, to help organisations get a better return on their investment, I think in these technologies.

And I think the other thing that is really interesting around the skills data, and certainly something that we are seeing talking to our customers at Insight222, it is almost the link between what have traditionally been quite siloed talent operations, such as learning, you talked about learning experience, such as talent marketplace, internal mobility, giving people the opportunity to maybe do projects using the skills that they have got as part of their work within companies. And then obviously that link to workforce planning as well and helping the organisation. I am going to borrow from Jimmy Zhang, who said on the podcast a few months ago, helping the organisation see around corners, in terms of helping them really make sure that they have got effective resource planning for now and the years to come.

So really, really powerful. It would be great to hear, again Mikael, if you have any examples that you can share, I appreciate if you are not allowed to actually name the company, but it would be good to hear some company examples? 


Mikael Wornoo: Yes, definitely. Because a lot of organisations, they intuitively feel that there is value in skills data, but they don't necessarily know where the biggest area of impact that could be. They don't really know where to start.

So we are working with a branch of Liberty Global, they are all about using skills data for strategic insights, and improving the employee experience. And those are really the two big use cases.

So when talking about strategic insights, I am really talking about questions like, what is the strategy from a people perspective? So let's say a strategic growth area is B2B IT services. How is that being reflected in the people we hired? How is that being reflected in our current population? Or agile and digital skills propagating through the population? Because it is not just that snapshot you want, you want the evolution over time, a CHRO should be able to report that our key critical skills or our key digital capabilities have increased 7% year over year, or quarter over quarter. It is about answering questions like, are our key digital skills in house or just being borrowed? By looking at skills or contingent workforce. So organisations don't have a lot of information on their contingent workforce, but by looking at the digital footprint, you can even map the skills for your contingent workforce and start thinking about, okay, are we just borrowing digital skills or are we actually becoming a digital company?

It is about what skills are entering and leaving the company and they are using all that strategic information to shape company-wide re-skilling programs, and theirs is called Switch, learning and development investments, and recruitment decisions.

Another example is an organisation that wants to identify which skills drive performance, and that wants to align a personal learning initiative with the overall business strategy.

So current learning infrastructure is great at making people learn, but aligning that learning with the company strategy, is still a struggle. So one of the end goals of the CHRO, is being able to measure the ROI of learning and development initiatives in real time, because again, that is how you make a business case for learning initiatives. 


Another example we see returning quite often, is that the business laid out a digital strategy and the question is, okay, how do we align that business strategy with our enterprise capability strategy, with our strategic capability building, or just strategic workforce planning in general? Essentially the questions we discussed, that is what strategic workforce planning is all about. If you are setting up a new business unit in the next few years, which people can be redeployed, re-skilled, upskilled. We see examples in offshore wind, expanding into new therapy areas in life sciences, setting up data and machine learning centres of excellence. So it is quite the endless list.

And then throughout the employee experience, that is what we touched upon briefly, just a few minutes ago. It is about enabling second careers, internal mobility, upskilling, re-skilling, but focused on the employee. We see that skills, and it is what you said, it is the bridge between talent management and learning projects. From a HR perspective, those might be traditionally siloed, but it is all part of the work of an employee, so it really touched the employee in multiple facets of their working life.

David Green: Yeah, so really powerful. And I guess, if you are working with organisations say, that wants to create a new business unit, wants to understand the skills that they have currently got within the organisation, to actually achieve that. Your technology can help them understand the skills they have got, maybe the gap between where they get to, give information to help them understand how they could potentially close that gap either through learning, because I guess there is a whole piece around skills adjacency as well. Potentially by looking at labour market information, help them understand where they may want to go out and buy those skills, working with that sort of technology as well, where they might want to buy that from a location perspective as well.

Then I guess what the great thing is, as you take those interventions and try to close that skills gap, 6 months, 12 months down the line, you can actually give them a snapshot of how they are doing about that. Are they actually closing that gap? And what have been the most effective ways of doing that as well? That way you may be wanting to double down on your investment in that area. So really, really powerful stuff. 

I think we are going to move the conversation in a little bit, to looking at TechWolf in terms of how you are growing the organisation and everything else. I think it is a fascinating topic as well.

Just to close the topic, conversation, around skills for now. If someone was to come to you and say what should we do first? What are some of the steps that we should do first? As you said, the typical problem is we don't know our skills. If you had to give maybe 5 or 6, maybe less, maybe more, tips to that practitioner, what would they be?

Mikael Wornoo: The way we see customers start with, is start with a strategic business question. And I mentioned a few, like the offshore wind example. We map skills for the organisation, okay, let's start with a strategic business challenge we want to answer. And then apart from solving that business challenge, it is about propagating information through the managers and through the employees.

So the tips I would give are actually very similar to the tips you would give to any practitioner. 
Start with the business problem, use that to create buy-in and think about, both the strategic, tactical, and operational, facets of skills data.

If you want to create business buy-in, you have to start simply by solving that business problem. I had this one CHRO tell me, we really don't even want to bother the business, we want to show them value first, before we start asking things, because we have asked them things in the past and it didn't go well. So we want to provide value first and then use their buy in to do more things.

David Green: Well, I think as you said, that important thing, I know we have said it already, but start with the business question. We see so many companies and practitioners not doing that, getting lost and excited by the data and they are not connecting it to something that actually, the business cares about or is important.

So yeah, I think that is really good advice.

So let's move on, we will certainly come back to skills at the end. What I think is really interesting when talking to 
founders such as you, who has started a HR tech company, but it is not in a traditional era. It is not like you are creating survey technology or technology that aggregates data and visualises it, although you probably do that as well.  

But in this area of skills, which is a new and burgeoning area, can you tell us a little bit about the experience of founding TechWolf and what it has been like working in the rapidly evolving HR tech field? There is so much investment going on at the moment, it must be an exciting place to work?

Mikael Wornoo: It is extremely exciting because it is equally a perfect storm. So there is a lot of M&A activity happening, there is a lot of advancements in this space happening, we have had COVID which has accelerated the attention on HR in some sense.

Going from academia to entrepreneurship is equally challenging, as it is rewarding. So we have very few skill adjacencies, so you continuously need to up-skill yourself and it is a continuous process of learning. You really learn as the company grows. We grew from 6 to 25 people, over the past two years, also we plan on continuing that growth. But the things you do when you were a company of 6 people, is completely different when you are 12, and completely different when you are 25.  

I had this one lady tell me, who was really into the VC and investment space, as a founder, if you look back at what you were doing six months ago and you are still doing the same, then you are doing something wrong. 
And that is really what the learning curve is all about in just founding this company. It is just an extremely exciting experience to be at the forefront of all the advancements in artificial intelligence as well and being able to apply them in HR.

So natural language processing, the sub-field of AI that we are most active in, is actually one of the most rapidly advancing fields in AI. It used to be that computer vision, was driving the state of the art, so computer vision is looking at images, understanding what is in there and trying to reason up from there. And now it is natural language processing. So all the advancements in AI as a whole field, are really being driven by advancements in sentiment analysis, text summarisation, translation, algorithms, chatbots and voice recognition. So that is incredibly fun.

And thirdly, it is just really nice to work with, what we call “difference makers” in organisations. Individuals that move the needle in an organisation, that have a strong, compelling vision, not of where HR should be in 6 months but where it should be in 5 years, and what they want their HR function to look like.

The organisations that worked with us in the beginning, two years ago, they knew the product wasn't perfect, but they were perfectly convinced that the journey that we were going to take them on, was going to help them get there.

So in some sense, those are true entrepreneurs as well, it is the people that use innovation to drive change. Out of all the things, I think that has been the most rewarding.

David Green: It is interesting you talk about NLP, and I know you co-wrote an article for the myHRfuture blog last year with Adam McKinnon. I am not sure I will get the exact quote right but I have heard Adam talk about, finance might be the home of numbers, but HR is the home of text. And if we think about what you just said about natural language processing, one of the things I hear a lot from organisations that may be struggling to really have impact with people analytics, is they say, we haven't got the data. Well, actually in many cases, particularly thinking about skills, the whole use case problem that you are solving at TechWolf, they do have the data, they just haven't necessarily had the means to activate that data before. The advancements in natural language processing is helping that to happen.

And when we think outside the skills piece, we think about employee feedback, we think about surveys, very much now a lot of the deeper analysis that is being done is on employee comments, rather than the traditional sort of scale of questions. So it really is an exciting place to work.

I would love to get your thoughts on where you think it is going to go next? What are some of the advancements that we can expect to see around natural language processing, from a people data perspective, in the coming years?

Mikael Wornoo: Surveying is one really interesting area when you combine it with the natural language processing, just truly understanding what people are saying. Let's say you have an organisation of 10,000 people, it is impossible to sift through everything that people wrote, but every individual is contributing useful information on what the health of your organisation is. Let's say you can cluster and summarise that information and you get the key insights and the key action points out of that. I think it is not something that is going to happen in a year, but in the future, those are things that will be made possible.

And then you can really start to freewheel here if you think about, what would an algorithm that could summarise text do, or what could having a perfect translation algorithm do, or what could a chatbot with voice recognition do if used correctly. Some of these are already being used, especially on the customer level, but the applications in HR and just internally will be as powerful.

David Green: And I suppose if we think about that whole sophistication around using data from a customer perspective, we are a little bit behind, from an employee perspective, in HR. If we look at some of the things that are happening in the CX world, we can probably think about how we can apply those in the field of people data as well. So really interesting.


There is a lot of research going on and obviously as someone like you, who comes from that academic background and as you said, stepping into entrepreneurship and growing a burgeoning HR technology company. How important is it to balance servicing customers, but also helping advance the field in terms of research as well?  


Mikael Wornoo: In our company, we invest a whole lot in research and development and we have a separate customer solutions department. So the research and development departments, if I can call it a department already, is just looking at applying new techniques, but also figuring out new techniques to solve fundamental problems in natural language processing. 


We really publish papers. We really want to advance the field. We have published papers on low research language modelling, on automatically enriching job oncologys like ESco, we have done some work for them. So it is about making sure that you keep pushing the state of the art forward, that will also give us our competitive advantage and then making sure that the input from the customer solutions team is also being taken into account in solving this problem.

So I think it is a beautiful synergy between actually hearing what sales is saying, what marketing is saying, what customer solutions is saying, going back to the R and D table and seeing how we can balance the two. It is a continuous balancing act, but in balancing the two, you also create the most value because you are essentially pinpointing ideas. Matt Ridley said, that innovation happens when ideas have sex, and that is really what happens here. You have ideas from the nontechnical side, you have ideas from the deeply technical side and then being in the middle of that, that is really where the magic happens.  


David Green: And as you said, it is so important to keep one eye on what is happening from a research and develop perspective because the field is moving so fast. If you don't do that, then you risk standing still, as you said, it is very much linked towards your competitive advantage. So it benefits the field as a whole, but it also benefits customers as well, like you said, it is a great combination.

Okay. So finally, Mikael, this is the question that we are asking everyone on this series, and I appreciate that you have talked to this throughout our conversation. You might just want to summarise a little bit here.

How can HR help the business identify the critical skills for the future?

Mikael Wornoo: For me, that is exactly what a strategic HR functions needs and what the skills intelligence is all about. And of course that starts with understanding what you have in house today, but just embracing data and evidence based decision-making and embracing the technology, will just fundamentally change HR’s job and make it more strategic.  

How HR can really help the business identify critical skills for the future, the answer won't come from doing the same thing we did 20 years ago, it is really rethinking the job of HR. Marketing became a strategic function by embracing data. It is not a one-on-one relation with HR because there is a people component, obviously, but really embracing data, like marketing, and really getting that strategic seat at the table, will only come if they help the business identify these critical skills. But on the other hand, that that will be done by embracing data. So it is a combination of those things.

David Green: So an exciting time for HR, but also a challenging time, I guess, because the whole conversation has been about skills and HR needs to improve its capability in certain skills area. 


As someone who has come from outside HR, what advice would you give to HR professionals on how they can learn some of these skills? Or, what resources would you suggest that they look at?

Mikael Wornoo: Well, myHRfuture academy, of course. It is always good to look outside of your bubble, that holds for us, people outside your bubble, outside of the tech world. I do continuously and I think that is also crucially important for HR to do. Talk to people in marketing and sales, even in technology and try to think what that means or will mean for your job, it gives you ideas but really embracing technology and having this technological awareness too.

I think Elon Musk once said that the topological map of technological awareness was very flat and had some peaks. What he meant by that was that essentially there are very few people that have a very good understanding of what technology will enable and there is just a lot of people that don't have a clue. And essentially you don't want to be the person that doesn't have a clue, you want to have a general understanding of how technology will impact your company, your industry, or job, and if you do that, I think then you are ready for the future.  


David Green: Yeah. And the good news is I think most HR professionals recognise that and want to be more data-driven, more data informed, and more digitally literate as well. As you said, there is so much that we can learn from outside of our bubble. So I think that is good advice.

Mikael, thanks very much for being a guest on The Digital HR Leaders Podcast, it has been a pleasure speaking to you. Can you let listeners know how they can stay in touch with you, find out more about TechWolf, and follow you and TechWolf on social media? 



Mikael Wornoo: Yes. You can follow me on LinkedIn, if you can spell my name, or you can just go to techwolf.ai and we will get it touch.

David, it was really great to be here. I really enjoyed our conversation and let's talk again soon.

David Green: Likewise Mikael, and hopefully we will get to see each other face to face again soon as well.  


Mikael Wornoo: I look forward to that.