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1. Exposure to generative artificial intelligence in the European labour market

Author:
Laura Nurski and Nina Ruer
Publication Date:
03-2024
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
We apply two sets of generative artificial intelligence (GenAI) occupational exposure scores – one task-based, one ability-based – to the European Labour Force Survey. While using different methodologies, our findings reveal consistent demographic patterns across the two approaches: jobs held by women, highly educated and younger workers are more exposed to GenAI technology in Europe. We also review the literature on the recent productivity impact of GenAI. Within the same occupations, less-experienced or less-skilled workers consistently get the largest productivity gains from GenAI support. We argue that a task-based analysis is more fruitful than an ability-based one, both for guiding GenAI adoption in organisations and their workplaces, and for assessing the employment and job quality impact on workers. Finally, we provide policy recommendations that can help workers (ie the labour supply) adjust to technological disruption, such as providing training and social safety nets. But we go further by also suggesting policy interventions that could redirect future labour demand towards better jobs, by promoting job redesign and organisational agility. Monitoring GenAI’s employment effects and researching the ‘jagged technological frontier’ is necessary to further build our understanding of the employment impact of this transformational technology.
Topic:
European Union, Artificial Intelligence, Labor Market, and Supply and Demand
Political Geography:
Europe

2. Artificial intelligence adoption in the public sector: a case study

Author:
Laura Nurski
Publication Date:
03-2023
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
This case study illustrates the drivers of and barriers to artificial intelligence adoption by organisations, and acceptance of AI by workers in the public sector. Several factors were crucial in the successful adoption of a human-centred approach to AI, including a fast discovery phase that involved workers (or end users) in the development early on, and aligning human resources, information technology and business processes. Subsidy support mechanisms were also specifically targeted and acquired to support the adoption. However, making AI support available to workers proved insufficient to ensure its widespread usage throughout the organisation. The slow adaptation of existing work processes and legacy IT systems was a barrier to the optimal usage of the technology. Moreover, the usefulness of the technology depended on both the task routineness and worker experience, thereby necessitating a rethinking of the work division between technology and workers, and between junior and senior workers. Successful human-centred roll-out of AI in Europe will therefore depend on the availability of, or investments in, complementary intangible organisational capital. Very little is currently known about these investments.
Topic:
Digital Economy, Economic Growth, Public Sector, Innovation, Artificial Intelligence, Inclusion, and Work
Political Geography:
Global Focus

3. The impact of artificial intelligence on the nature and quality of jobs

Author:
Laura Nurski and Mia Hoffmann
Publication Date:
07-2022
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
Artificial intelligence (AI), like any workplace technology, changes the division of labour in an organisation and the resulting design of jobs. When used as an automation technology, AI changes the bundle of tasks that make up an occupation. In this case, implications for job quality depend on the (re)composition of those tasks. When AI automates management tasks, known as algorithmic management, the consequences extend into workers’ control over their work, with impacts on their autonomy, skill use and workload. We identify four use cases of algorithmic management that impact the design and quality of jobs: algorithmic work-method instructions; algorithmic scheduling of shifts and tasks; algorithmic surveillance, evaluation and discipline; and algorithmic coordination across tasks. Reviewing the existing empirical evidence on automation and algorithmic management shows significant impact on job quality across a wide range of jobs and employment settings. While each AI use case has its own particular effects on job demands and resources, the effects tend to be more negative for the more prescriptive (as opposed to supportive) use cases. These changes in job design demonstrably affect the social and physical environment of work and put pressure on contractual employment conditions as well. As technology development is a product of power in organisations, it replicates existing power dynamics in society. Consequently, disadvantaged groups suffer more of the negative consequences of AI, risking further job-quality polarisation across socioeconomic groups. Meaningful worker participation in the adoption of workplace AI is critical to mitigate the potentially negative effects of AI adoption on workers, and can help achieve fair and transparent AI systems with human oversight. Policymakers should strengthen the role of social partners in the adoption of AI technology to protect workers’ bargaining power.
Topic:
Science and Technology, Economy, Artificial Intelligence, Inclusion, and Work
Political Geography:
Global Focus

4. What is holding back artificial intelligence adoption in Europe?

Author:
Mia Hoffmann and Laura Nurski
Publication Date:
11-2021
Content Type:
Policy Brief
Institution:
Bruegel
Abstract:
Artificial intelligence (AI) is considered a key driver of future economic development, expected to increase labour productivity and economic growth worldwide. To realise these gains, AI technologies need to be adopted by companies and integrated into their operations. However, it is unclear what the current level of AI adoption by European firms actually is. Estimates vary widely because of uneven data collection and lack of a standard definition and taxonomy of AI. What is clear is that AI adoption in Europe is low and likely running behind other parts of the world. Discussions on the barriers to AI advancement often mix up different stages of innovation – research, development and adoption. Each stage is constrained by the availability of skills, data and financing in the European market, but there are nuances in how these barriers arise in each of the three stages. This policy contribution focuses on the final stage, AI adoption. We discuss theoretical and empirical evidence of the drivers of AI adoption. We outline the relevant barriers to adoption for European firms in terms of human capital, data availability and funding, and make international comparisons where possible. To accelerate the roll-out of AI technology across the European Union, policymakers should alleviate constraints to adoption faced by firms, both in the environmental context – labour market, financial market and regulation – and in the technological context – data availability, basic digitisation of businesses and technological uncertainty.
Topic:
Science and Technology, European Union, Economic Development, and Artificial Intelligence
Political Geography:
Europe