Understanding changes in the graduate employment landscape
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“Tomorrow’s workers will need to be able to collaborate with AI” (Financial Times, September 2023)
“As AI advances, will human workers disappear?” (Forbes, June 2022)
“AI anxiety: the workers who fear losing their jobs to artificial intelligence” (BBC, April 2023)
“Companies that replace people with AI will get left behind” (Harvard Business Review, June 2023)
In fact, we can draw on a long-standing strand of the labour economics and political economy literature on the effects of automation and technological change on labour markets and how that reflects on education systems . This literature has provided a sound conceptual framework which allows current researchers to analyse the most recent developments and how they affect different occupations and professions. What we want to do in this step is to review some of the key concepts and current approaches together and build some foundational knowledge that will enhance your capacity to critically analyse the different perspectives that are being voiced in the public debate.
Description: Transformations and questions being asked: AI as one of the mega-trends that are transforming the labour market: comparing job growth and decline; Uncertainty about their trajectory for the future; Rapid transformations in AI technology increase that uncertainty; How can governments, employers, trade unions, educational organisations and individuals prepare?
Many organisations refer to the transformations we are facing and the questions that they raise as the ‘future of work’. For example, the World Economic Forum has been releasing bi-annual ‘Future of Jobs’ reports since 2016 . The ‘future of work’ refers to a variety of trends in the economy which are affecting how occupations change within the labour market, what kind of jobs are growing and which ones are in decline. In 2023, the most important forces that affect job creation and destruction are not only technological (AI amongst other transformations) but also significantly environmental (green transition investments) and economic (eg supply chain disruptions). Two notable features that are different with AI are:
- The future of AI technology is highly uncertain as the technology itself is developing rapidly; and
- The scope of automated tasks has changed from manual and physical tasks to tasks involving reasoning and communicating, generally associated with white-collar occupations.
That’s why, where automation used to be mainly a worry for low-skilled occupations in the recent past, it now increasingly looks like a possible scenario for many graduate jobs in the future. In light of this uncertainty, employer and employee organisations alike are therefore calling on governments to adapt their skill formation and labour market policies to get ready to respond to these trends and soundly balance their economic and social objectives.
In the newspaper quotes above, you can see there is a lot of emphasis on the fear of losing one’s job and on the idea that jobs are disappearing, but this is of course only part of the story. Current analyses expect more job creation than displacement overall and emphasise the importance of jobs that collaborate ‘with’ AI, hence the idea that firms need to be careful about decisions which replace workers with AI. Instead, to remain competitive, they should upskill them to be able to collaborate with AI technologies.
To assess how AI shapes an occupation, it is helpful to think of AI systems as task centres and then ask what tasks of a particular occupation can be automated with the current technologies. Therefore, when you read or hear about a kind of occupation or sector being ‘exposed’ to AI, this does not necessarily mean that AI is replacing the occupations within that sector. According to a recent report by the International Labour Organisation , it is more useful to compare the extent to which automation may affect certain tasks within jobs or occupations more broadly, which includes both ‘automation’ and ‘augmentation’. Automation means that the AI replaces a worker. Augmentation means that humans collaborate with AI systems, where some tasks are better addressed by the AI system (eg crunching numbers) whilst others are better solved by humans (eg non-routine reasoning and interpersonal skills).
What are the different ways in which AI might affect jobs?
- Variation in exposure: automation – occupations where many tasks can be automated (eg clerks). Augmentation – occupations where some tasks can be automated and others not (eg managers).
- Job quantity: is it good enough to substitute workers but not good enough to boost productivity to create new jobs? Does it create new tasks related to its deployment?
- Job quality: does it substitute pleasant or unpleasant tasks? Could the use of AI result in the deskilling of workers as it replaces complex tasks?
- Unequal distributions of exposure: low versus high-income countries, gender inequalities, educational inequalities.
Finally, to move away from speculation and come back to the realities on the ground, you can review some data from a survey of employers in different sectors in a selection of high-income countries in the table below.
Employers’ responses to changing needs due to AI
|Finance sector||Manufacturing sector|
|Retraining or upskilling workers||64%||71%|
|Buying services from external companies||53%||53%|
|Hiring new workers||35%||48%|
|Attrition or redundancies||17%||14%|
Source: Figure 6.2 
The data seem to indicate that retraining and upskilling workers is the number one solution that employers are turning to now. At the same time, it is important to bear in mind that such survey data might come with some issues. For example, do respondents accurately capture what their firm does and the extent to which these actions result from changing needs related to AI? What might be special about the sectors represented in this survey that does not apply to other sectors?
This conceptual overview of how AI drives economic and political actors’ responses will hopefully give you a handy starter kit to put together your own box of conceptual tools and questions to have in mind when continuing to hear or read about AI and the future of work, and to critically assess the evolution of the debate on this topic and how it applies to the development of skills and occupations in your own field.
Now that you have completed this step, you have gained an understanding of the key concepts with which experts assess the relationship between technology and employment changes in the current context. In the next step, you will discover what these broad trends mean for the specific skill requirements of the new jobs that are being created as well as for traditional graduate jobs.
- Autor, David, David A. Mindell, and Elisabeth B. Reynolds. Why ‘the Future of AI Is the Future of Work’. MIT Sloan Management Review, January 31, 2022.
- World Economic Forum. Future of Jobs Report 2023. Insight Report. 2023.
- Gmyrek P, Berg J, Bescond D. Generative AI and jobs: a global analysis of potential effects on job quantity and quality. International Labour Organisation; 2023.
- Lane M, Williams M, Broecke S. The impact of AI on the workplace: main finding from the OECD AI surveys of employers and workers. OECD Social, Employment and Migration Working Papers, No. 288. Paris: OECD Publishing, Paris;2023:67.
If you would like to explore this topic further, here are some additional resources.
Lane M, Williams M. Defining and classifying AI in the workplace. OECD Social, Employment and Migration Working Papers, No. 290, OECD Publishing, Paris.
Autor D, Mindell D Reynolds E. The work of the future: building better jobs in an age of intelligent machines. MIT Task Force on the Work of the Future. 2020.
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Generative AI in Higher Education
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