[Podcast] Planning for AI and green skills shortage

AI and green skills: Closing the critical gap 

“We often discuss the green transition and digital transformation in isolation, without systematically mapping where these skill sets actually overlap, or more importantly, where they don’t.” says Siddhi Pal, Senior Policy Researcher at Interface. In this episode of the AI at Scale podcast Siddhi explains why the combination of AI and green skills—named “twin transition skills”—is emerging as one of the most valuable capabilities in today’s economy. 

Why should executives prioritize the convergence of AI and green skills as a strategic imperative?  

Siddhi’s research highlights a real challenge: green hiring is growing at 7.7% annually, yet green skills only at 4%. This mismatch results in 60% higher hiring premiums for workers combining AI with sustainability expertise. 

Taking example of Europe, where AI talent is concentrated in a few hubs, while 19 AI factories are announced across the continent, Siddhi observes that infrastructure alone will not attract talent. Companies must invest in internal reskilling and contribute to creating attractive opportunities for AI and green talents. 

Additionally, Siddhi highlights that women represent only 22% of AI talent globally—and they are a potential untapped group of sustainability and AI experts. Closing this gap requires moving away from traditional hiring approaches to skills-based hiring, as emphasized in the recent LinkedIn Green Skills Report. 

To ensure competitiveness in the AI-driven green economy, Siddhi proposes to focus on transparent reskilling pathways, inclusive work cultures, public-private partnerships, and updated taxonomies to measure these twin transition skills to develop accurate policies and corporate strategies. 

In this episode, you’ll learn: 

• why defining and measuring green skills is critical for future workforce planning, 
• which green skills are common among AI professionals, and which are still rare, 
• how companies can balance internal reskilling with strategic external hires, 
• what actions can help close the gender gap and expand talent pools. 

If you’re planning for growth in an era of climate action and AI acceleration, this episode delivers insights you can act on today. 

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Listen to Siddhi Pal: Planning for AI and green skills shortage episode. Subscribe to the show on the preferred streaming platform (Apple Podcasts, Spotify or play the episode on YouTube.

Transcript:

The idea of research about green and AI skills

Gosia Gorska: Welcome to the AI at Scale podcast by Schneider Electric. My name is Gosia Górska, and I am the host of this program. Our show invites AI practitioners and AI experts to discuss their experiences, challenges, and AI success stories. Today, we will discuss a very special combination of skills: AI and green skills. I have the pleasure of hosting Siddhi Pal, who is concluding research in this area. Welcome, Siddhi. 

Siddhi Pal: Thank you so much, Gosia. Thank you for that warm welcome. I am really excited for our conversation today. 

Gosia: Siddhi, you are a Senior Policy Researcher AI at Interface, a European tech policy think tank. Your work is focused on the intersection of artificial intelligence, labor markets, and talent flows with an emphasis on inclusion and equitable access. Previously, Siddhi served as a policy advisor to the UK government and led programs for the UN Foundation Girl Up initiative. She is an active voice in global forums like the UN General Assembly and the World Economic Forum. She holds a master’s degree in public policy from Oxford University and a degree in social psychology from the London School of Economics. So, Siddhi, please tell us a bit more about your work at Interface, and also, how did you come up with the idea of the research about green and AI skills? 

Siddhi: Thanks so much, Gosia, for that very warm welcome. As you have already mentioned, I lead AI and labor market research at Interface, and my work sits at a fascinating intersection of AI talent, innovation, and competitive policies. We have published quantitative reports in the past that analyze talent ecosystems in Europe and beyond and are also doing qualitative research where we are doing country-specific case studies that further examine the narratives behind these numbers that we are finding in our quantitative reports. So, we have published a case study on Italy. Next week we are publishing a case study on Finland, and then next, we are looking at Germany. But this paper on green and AI skills specifically is very special for us because it emerged out of a speaking engagement and a conversation behind it. So, I was invited to speak at UNDP’s unlocking green tech potential event, and I was having a conversation with my colleague Catherine, and we both realized that there is this massive blind spot in how we understand workforce development for climate action. Everyone talks about the green transition and digital transformation separately, but no one is mapping where these skill sets actually overlap or, more importantly, where they do not. What made this research urgent now is that we are seeing policy discussions around AI infrastructure and green investments, and they are both happening in complete isolation from talent realities. So, that was the inspiration behind this paper on green and AI skills together. 

The importance of combining AI and green skills

Gosia: I see. And why do you think it is important for talents to combine both the AI and green skills? Why does it matter for businesses and for society? 

Siddhi: Definitely. So, there has been a lot of recent research that is showing that green hiring is growing at 7.7% annually, but when you look at green skills growth, it is only at 4% growth annually. This is not just a workforce challenge, right, because it is telling us where the most valuable skill combinations are emerging. What has changed very recently, and has been looked at for the first time in 2025, is that 53% of these green hires are people with green skills but are not in green job titles. So this is not just about isolated sustainability departments anymore. We are looking at procurement specialists that understand sustainable supply chains, manufacturers optimizing for circularity, and technologists managing AI’s energy footprint. Companies are recognizing that these skills deliver what they have always prioritized, which is efficiency, resilience, and competitiveness. And the market is putting real value on this. We are seeing that workers with green skills globally are hired at rates that are 46% higher than the overall workforce. In India specifically, that advantage jumps to nearly 60%. So now workers who combine this AI and green expertise—this is what a LinkedIn report recently called twin transition skills—are positioned at a very critical economic advantage, right? Sixty percent higher than the overall workforce is fairly high. And we are seeing that in 2025, 3% of the green talent in G7 countries has started adding AI engineering skills that are also growing at nearly 40% year over year. So, we are now realizing that the AI and sustainability conversation is no longer isolated, and we need people who can actually bridge this in practice. So, these are people who can both deploy AI solutions and optimize their energy efficiency. They are managing sustainable data center operations or applying machine learning to reduce emissions in manufacturing and logistics. And this is a combination that is becoming extraordinarily valuable for us right now, which makes it more important for us to work on this workforce development. 

Least and most popular green skills

Gosia: Yes, those are really interesting findings. And I am super curious to know, among the AI experts, the AI population on the job market, which are the most popular green skills, and which of them are still very rare or emerging? What did you find in the study? 

Siddhi: This was one of the most interesting findings for us. So, we mapped Europe’s 1.6 million AI professionals against the European skills classification system, which is the ESCO, and that revealed patterns around where the AI expertise is essentially concentrated. So, the most common green skills that we found are lean manufacturing and developing biocatalytic processes. These are skills that are related to pharmaceuticals, agriculture, and consumer products. The third most common was renewable energy, which had about 34,000 professionals. And then, when we see research capabilities, this is about conducting flora research, analyzing environmental data, and ecological research—basically, fields of study like biology, geology, and earth science. Now, what is largely absent tells you where the AI talent is not going. And the least common skills relate to waste management systems, that is food waste monitoring, recycling program management, disposal systems, and critically, the development and enforcement of environmental standards and regulation. So, this is about road transport legislation, airport environmental regulation, and urban planning law. We are seeing that technical knowledge is not shaping the development in these crucial industries. If Europe wants to lead in areas like circular economy, sustainable urban planning, or smart waste systems—which are sectors where we feel we could have genuine competitive advantage—we do not have AI professionals working in those spaces. Our AI talent is concentrated in manufacturing and pharmaceuticals, but these are sectors where we are already competing head-to-head with some of the most established global leaders. 

Workforce of AI and green skills

Gosia: Yeah, that is really fascinating. So, you mentioned the skills. Now let us focus a bit on people. Where is Europe’s talent base in terms of AI and green skills? Where are these people coming from? 

Siddhi: That is a great question, and I love how you framed it as, “let us look at people,” because that is what we have been pushing for the whole time. Let us actually look at the people that are the workforce behind AI, behind green tech. In our research, we looked at the geographic distribution of AI professionals. We are seeing that the bigger economies dominate in terms of absolute numbers. So, the UK has about 16,000 green AI professionals, Germany has 13,000, and France 7,000. But then, when you look at major AI talent hubs, the picture is a little different. You look at Ireland, Switzerland, and Luxembourg. These are top destinations for AI talent overall, but they have very modest green talent pools. Ireland has fewer than 2,000, and Luxembourg just has 200 green and AI tech professionals. We can say that at the city level, London is leading with over 4,000 individuals. Paris has about 2,000. There is a steep drop-off after that because the majority of the cities that we have analyzed—that is Munich, Amsterdam, Dublin, Cambridge—all of them have fewer than a thousand green AI professionals. This matters because we are seeing talent flow towards opportunity and not just infrastructure. The research shows that workers in these combined skills command significant hiring premiums, as we have mentioned, and they are strategic about where they locate. Right now, talent is concentrated in a handful of cities while the rest of European regions are struggling to build critical mass essentially. 

Gosia: Yes. And then we have a topic, like you mentioned, about circularity, that is important for every city, any council, any village, for every country as well. And if we look from the AI perspective, recently there was the announcement about AI factories being built in Europe in countries like Poland, Germany, but also Slovakia and Greece, to just name a few. Are these locations already attracting talents? Are they going to tap into the potential of local talents? What is going to happen? What do you think? 

Siddhi: So, we have done research on this exact topic as well because we were very curious when the AI factories were announced what the locations might be. As you have already mentioned, different countries were bidding for AI factories as well. As of now, there are 19 AI factories that have been announced across Europe, and we are seeing only two of them are located near major existing AI talent hubs. AI skills in Europe are heavily concentrated in specific metropolitan areas and innovation ecosystems that have digital sector expertise. They are far better positioned to leverage compute infrastructure. This is not just intuitive; this is something that has been confirmed by data. We have also recently completed the case study on Finland, where we examined exactly this because they have the Lumi AI factory. We wanted to learn more about infrastructure’s role in attracting AI talent. And we concluded that there is a need to decouple talent attraction strategies and infrastructure development. Because what is happening right now is that we are setting up and focusing on infrastructure, and we assume that talent is going to come as soon as we have the world-class infrastructure essentially. This is something that we are seeing in the Finland case study specifically because Finland has always been ahead of the curve when it came to infrastructure. They also have the Lumi AI factory, and we are seeing that while Finland has a decent AI talent base, it is not necessarily around the AI factory or only because of the AI factory. A lot of people also remotely access this infrastructure and are not situated next to this infrastructure. Essentially, that is where our recommendation of looking at talent pipelines as a separate policy initiative comes from. I think this extrapolates beyond Finland: regions that do not have existing talent pools are facing significant challenges in growing their AI capabilities. Smaller talent pools limit the network effects, and then they drive adoption and utilization. And innovation does not require just infrastructure. It also requires people who know how to use it and critically, the business leaders and researchers who can identify valuable use cases. 

International inflow of talents

Gosia: So we are talking a bit about the exchange of talents between European countries, especially as we will see those AI factories emerging. How about the international inflow of talents? Did you observe anything in the research on this topic? 

Siddhi: Definitely. We are seeing when it comes to AI talent, a lot of the workforce is international talent, and there is a high dependence on the international workforce. Specifically, when it comes to AI, we have seen in countries such as Ireland over 30% of their AI workforce comes just from India. Around 30% is the average across European nations regarding where the AI workforce is coming from. So, again, there needs to be a dual focus here: not just on developing homegrown talent, but also ensuring that the countries remain competitive in terms of attracting talent. And this high dependence on international talent is not unique only to Europe; this is a global challenge. The US’s AI workforce is heavily dependent on international talent as well, and they do focus on remaining competitive in terms of attracting this talent. I think it is slightly different when we talk about Europe here because we are always talking about the US, China, and Europe when we discuss the AI debates, but our talent policies are different at member state levels in the EU. So, maybe there is also a need for conversation around how that leads to internal competition for talent among European member states as well, and if there is a way to move beyond that. 

The advice for businesses

Gosia: As you mentioned, the AI infrastructure alone will not be enough. We also need to have a policy that follows, that creates attractive conditions of working and living for those AI and green skilled talents. Now, if we look at businesses, I know that you usually advise on policy to governments and different organizations. But do you have any advice for businesses on what they can do in the face of this kind of skill gap? Because when I reflect on what we do at Schneider Electric, definitely AI and sustainable skills are super important for us. For example, we have a mandatory course this year for all employees to get upskilled, to get a basic understanding of AI. And also, we were mentioning the recent report by LinkedIn about green skills. But what else can we do? Should we support the transition of careers more towards AI and sustainability, or do you think we should work more with the young talents, the people who are coming to the job market basically? 

Siddhi: Very interesting question. I would say that Schneider specifically is a very interesting case study, and as has been shown in the LinkedIn report as well, you are already proving a lot of things with your approach. We have already discussed how the market supply cannot meet the demands of the green AI workforce at the moment. Companies do need to focus on building it internally as well. Your approach has shown remarkable results as well, right? You have trained around 44,000 employees in eco-design. You have expanded beyond R&D to sales, and you are also, like you just mentioned, focusing on AI training. I think one of the very striking aspects of your approach is a skills-first approach to roles and capabilities. It is this internal reskilling at massive scale, and it is delivering results. You have seen 12% revenue growth. You have seen a massive reduction in customer emissions. This is also matching a broader pattern in the data that we are seeing. We are seeing that 53% of the green hires are now people with green skills but in non-green job titles, as I had mentioned. So, we are looking beyond the mainstream roles and looking to layer up capabilities and skills. I think specifically for whether to focus on internal upskilling and reskilling within the organization or to look at hiring new experts within the organization, there needs to be a balance there. Your employees already understand energy systems. They understand building automation and industrial control. Now, adding that additional AI literacy to that foundation is much faster than teaching computer neuroscientists about HVAC systems or manufacturing operations. So, I would say that this domain knowledge is not easy, and your employees already have that, and then the technical skills can be layered on top systematically. Schneider’s systematic approach is building a competitive advantage because you are already three to five years ahead of the curve when it comes to developing this talent pool internally. We have already seen that the market is not meeting the demands, and there are a lot more vacancies in this AI and green space than there is available talent. And policy makers also agree that governments by themselves cannot meet this upskilling and reskilling gap. It is one thing to focus on educational pipelines right now. By the time our current talent at universities graduates and then enters the workforce, the workforce might look fairly different by then as well. The workforce is changing every one or two years at this point; new skills are being added and needed. So, there needs to be a combination of public-private partnership when it comes to upskilling talent itself. There needs to be a combination of not just relying on the younger talent that is entering the workforce, but also continuously upskilling and reskilling the existing workforce so that we can meet the demands of these evolving workforce needs essentially. 

Gender balance

Gosia: Yes. So, there is definitely some challenge here for policy makers, for businesses as well. And I would like to discuss one more challenge with you since I know that you are also researching this space: the gender balance. So, is there a gap in terms of AI and green skills in terms of gender representation? 

Siddhi: There is a big gap there that is not spoken about enough. So, there is a gender gap in this space that is both very stark and also strategically damaging. Globally, the AI workforce or talent pool has only 22% women, but this is only at the entry level. As soon as you start looking at senior executive levels, it falls down to below 15%. In Europe, specifically, it ranges between 20 to 40% across countries, even where general workforce gender parity is much better. When you look at green AI talent specifically, northern Europe leads. So, Latvia has 52% women, Iceland has 49%, and Finland 48%. But when you look at bigger economies, they show much worse numbers. Germany, which has one of the largest green AI talent pools, has just 21% women in this pool. France has 29%, UK 28%, Netherlands 26%, and Malta is the lowest in our rankings with 18% women in the green AI talent pool. This goes beyond just fairness, right? Workforces that are not diverse risk failing to fully consider climate change impacts on different communities or even to evaluate the needs of different interventions. But then there is also the brutal economic reality: we are systematically excluding half the population from the fastest-growing, highest-premium skill combination precisely where we can least afford to. We have seen in the LinkedIn report and also in the case study on Schneider that skills-based hiring that is focusing on capabilities rather than traditional credentials can actually increase women’s representation in green talent pools by 26% in, say, US construction, and 22% in India’s utility sector. We know this works, and data is showing that 60% of Gen Z women express preference for climate action jobs. So, the demand is also there. But we are not doing it. We are hiring based on traditional credentials in male-dominated fields, artificially shrinking our available talent pool. We have seen green hiring is growing annually, and we cannot meet demand. So, systematically excluding half of the potential workforce is strategic malpractice. We know what we need to do. We need to implement skills-based hiring and promotion. We need to create transparent reskilling pathways that do not require traditional engineering credentials. We need to mandate inclusion hiring targets with accountability. And most importantly, we need to address the hostile environments in male-dominated industries that drive women out even when they do enter. This is something we have seen in all the case studies we have done so far. The fact that women are now entering more and more STEM careers but are choosing to leave two to three years in is a broader systematic question that goes beyond just STEM trainings. We feel that companies and countries that can figure this out can significantly expand their talent pools to 3.5 times globally, and those that do not will lose this competitive race. And it will not be because the talent does not exist; it will be just because they refuse to access it. 

Gosia: Okay. So, you explained about the gender gap. Do you have any advice about how to close it? 

Siddhi: Definitely, there are a few pathways to bridge this gender gap. The first would be, of course, encouraging more women in STEM careers. And that does not start at the university level; that starts right in primary school. This is something we have seen in the Finland case study, where when we spoke with people about what encourages more women to enter the workforce, it is not just a push towards STEM that starts when people start going to university. It is more societal. It starts as soon as someone is born. There is this constant push for equity in how people are raised. So, we need to fundamentally change how we look at STEM careers, how we look at the gender’s role when it comes to bringing up individuals. So, education right from the beginning. Nowadays, there are a lot of books that encourage young people to explore different types of careers that are beyond the traditional careers that we had known when we were growing up. So, education, but starting right at the primary level. Then, of course, looking at what existing hiring practices look like and if they sometimes unintentionally are keeping out certain parts of the talent pool and providing an advantage to certain genders over others. This includes looking at traditional workforce experience, and what type of credentials we value versus others. Are we giving more weight to traditional university degrees than reskilling/upskilling bootcamps that are happening now? A lot more people are reskilling and upskilling themselves through micro-credentials that they are stacking rather than university degrees because of how fast the workforce and skills are evolving. So, I would say those would be two education pathways, looking at hiring practices, and also work cultures, because we are seeing even the people entering the workforce are choosing to leave because of workforce culture. So, there needs to be a bigger emphasis on what kind of impact that has on people’s careers. And again, when we look at attracting talent, also focusing on inclusion and diversity there when we look at international talent. This is another thing that we are noticing more and more: most of the international talent that countries are attracting tend to be men because of the ease of moving overseas and various factors there. So, having an inclusion focus there as well. 

Future of Europe in the green AI race

Gosia: I see. So, Siddhi, when you look at this landscape: the skills gap, the gender imbalance, the centralization of skills in certain locations. Do you worry, or is there some hope for Europe’s future in the green AI race? 

Siddhi: I would say there is definitely hope, but worry here is fairly fundamental because if you ask different people what AI and green skills mean, there will be different answers. We do not have one standardized definition that is EU-wide. Say, for example, when you look at the ESCO taxonomy, there are definitions of what green skills are and what AI skills are, and somewhat what green tech skills are, but these definitions are fairly outdated at this point. There are a lot more skills that have been added in the last few years. If we want to know what our talent pool looks like and what interventions we need, where do we need to upskill or reskill, where is it that we are lacking, we first need to be able to measure what our existing workforce snapshot is like—where are we at right now and where do we need to develop. So, I would say let us start with the basics. We need to look beyond the traditional taxonomy, which is very heavily focused on engineering and natural sciences, and go more towards tech-forward capabilities. We need to look more at AI-specific taxonomy here. Based on what we have seen in the existing taxonomy, only one-third of the AI workforce has even just one green skill. It can be the case, but maybe we are also not measuring for the right type of skills in our existing taxonomy, and maybe there are more, maybe there are less—that would be a definitional challenge to look at. This again would help us be more explicit and look at what green AI talent means in practical terms, right? Are we talking about data center energy optimization? Are we talking about AI-driven emissions reduction, sustainable supply chain modeling, or predictive maintenance for renewable infrastructure? There are a lot of these new skills that are needed and need to be measured, and we need to know about this in order to build the workforce pipeline accordingly. 

Last message and wrap up

Gosia: Thank you so much, Siddhi, for this conversation. It was super informative. Do you maybe want to share some last message to our audience who is listening? 

Siddhi: I think my last message to the listeners would be to look at AI and green skills from a multi-sectoral perspective. Look at this challenge not as a responsibility for one sector, or only governments, or only the private sector to solve, but to look at where our strengths lie and where collaboration will help us solve these problems in a way that can be sustainable. For example, reskilling and upskilling—all these challenges can be better understood and met if there is a joint understanding of what skills are needed and what pathways work best. So, I would leave you all with the message, which would be to look at the problem in a multi-sectoral way and explore more public-private partnerships when it comes to building the workforce for the future. 

Gosia: Great. Thank you so much, Siddhi. It was a pleasure hosting you. 

Siddhi: Thank you so much for having me, and I look forward to keeping this conversation going. 

Gosia: Yes, sure. And of course, we will share the link to the study, and to all who are listening and watching us, thank you so much for staying with us. I invite you to follow us on podcast platforms and on YouTube to discover next episodes. Thank you. 

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