Workers today are being urged to constantly reskill. Artificial intelligence favours certain new skills while making others redundant, but it’s not that simple to choose what to invest in. Fabian Stephany and Ole Teutloff reveal that complementarity – the degree to which skills can be combined – is key to understanding the economic value of a particular skill.
Can we keep up with AI? The question of whether we will be “racing against or with machines” has been a prominent one in recent years. AI and other digital technologies have started to reshape industries, rendering some jobs obsolete while creating entirely new ones. This constant transformation demands that workers and organisations adapt to the changing landscape of work. Technological advancements, especially in fields like AI (think ChatGPT or Dall-E), bring forth new opportunities, but they also create a pressing need for specific skills.
The pace of technological and social change has quickly outstripped the ability of national training systems and employers to keep up. Workers therefore find themselves at a crossroads, uncertain about which skills will secure their future. This dilemma is further complicated for those who have already invested in their current skill sets and who have limited resources to embark on entirely new career paths. The global workforce is urged to constantly reskill, as technological change favours particular new skills while making others redundant. But which skills should workers and firms be investing in?
Complementarity: the key to a skill’s value
To address this question, we analysed a decade’s-worth of skill profiles of around 25,000 knowledge workers, summarised in our recent article and found that complementarity is the key to determining the value of a skill. Complementarity refers to how well a skill complements and enhances other skills. Here’s why it matters:
- Skill sets: Rarely do we apply a skill in isolation. Most jobs require a combination of skills. Therefore, the value of a skill can only be assessed in the context of its complementary skills.
- Reskilling efficiency: As workers adapt to new technologies, they incrementally add new skills to their existing skill sets. Maximising complementarity between old and new skills is crucial for economic efficiency in this process.
- Strategic value: As a particular skill’s set of complementary skills becomes more diverse, the more strategic options a worker has for reskilling. This increases their resilience against unforeseen technological changes in the future.
AI skills: a valuable resource
To put our concept into practice, we focus on the skills related to AI. AI is at the forefront of technological innovation, creating new opportunities and demands for specific skills. Indeed, in our model, AI skills, such as programming languages and data analytics, have proven to be particularly valuable – increasing worker wages by 21 per cent on average – as shown in Figure 1 below. But why?
Figure 1. The value of AI skills
(Top) Working with AI pays off: The value of AI skills – increasing worker wages by 21% – is significantly higher than for average skills. (Bottom) When examining the value of AI skills individually, we see that skills around machine learning – that is, ML (40%), Tensor Flow (38%), Deep Learning (27%), NLP (19%) – are more valuable than skills around Data Analysis (14%) and Data Science (17%), followed by the most prominent programming languages used to build AI, like Python (8%), C++ (7%) and Java (5%). Source: Stephany and Teutloff (2023) .
AI skills exhibit strong complementarity with various other skills, both in terms of number and diversity. This makes them highly adaptable and valuable in a variety of contexts. They have entered various fields of knowledge work, from graphic design to translation work to software development. In addition, the demand for AI skills has been on the rise in recent years. As industries across the board embrace AI, workers with AI skills are in high demand, leading to increased wages.
Reskilling: empowering workers and firms
Our findings have profound implications for individuals, businesses, and policymakers. By recognizing the value of complementarity, we can better guide workers on their reskilling journeys. Imagine personalised skill recommendations tailored to each individual’s existing skills, ensuring they are well-equipped for the future. For organisations, investing in the development of AI skills among their workforce is an investment in the future. It’s a strategic move that not only boosts productivity but also future-proofs the company against technological disruptions.
In conclusion, the world of work is evolving, and adaptability is the key to success. Understanding the complementarity of skills is essential in making informed decisions about where to invest in your skill development: Learning one and the same skill might pay differently, depending on which skills you already have. Our research supports policy recommendations that advocate for personalised learning strategies – ideally carried out within firms – and more flexible certification options for competencies acquired through vocational training, short courses, or training programs. These flexible certifications, often referred to as “micro-credentials,” are in high demand to ensure that both workers and employers are well-equipped to navigate the ever-evolving landscape of the future of work.
- This blog post is based on What is the price of a skill? The value of complementarity. Research Policy, 53(1).
- The post represents the views of its author(s), not the position of LSE Business Review or the London School of Economics.
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