Artificial intelligence and skills in the workplace: An integrative research agenda

Introduction

On 11 January 2023, a marketing agency Codeword announced the recruitment of ‘the world's first AI interns’. ¹ Named Aiden and Aiko, the artificial intelligence (AI) ‘interns’ will be full members of the company's 106 staff: Aiko will join the design team, and Aiden will work in the editorial team. Their tasks will include creating slides, writing entries for the company's blog and social media and analysing trends and news. The performance of the AI ‘interns’ will be regularly evaluated as they progress towards a potential full-time role. The vendors’ promotion of AI tools under the guise of ‘digital employees’ or ‘colleagues’ is well-documented in the literature. Scholars have critiqued the destabilising effect of such nomenclature on human dignity in the workplace, evidencing the non-trivial amount of human input that goes into training, validating and calibrating such tools to keep them running (Newlands, 2021). The Codeword's announcement is mute on what would be required of Aiden's and Aiko's human co-workers, managers and clients to work with these AI ‘interns’.

This example is one of a myriad of recent transformations in work practices brought about by the development and diffusion of AI in organisations. Economists have shown that technological advancements require specialised, ‘higher-level’ knowledge and abilities leading to new skill demands, a pattern termed ‘skill-based technological change’ (e.g. Autor and Dorn, 2013). AI technologies fit this pattern generating skill gaps and potentially requiring new, AI-specific skills. This technological transformation challenges workers, organisations and societies. At the individual level, workers are faced with the need to enhance their skills to succeed in today's AI-mediated workplaces and the labour market of the future, where AI is set to play an ever more central role (McKinsey, 2021). At the organisational level, the embedding of AI has been uneven, with the availability of skills to develop, implement and use AI recognised as a key barrier to their uptake (Brynjolfsson and Mitchell, 2017; Frank et al., 2019). At the societal level, educational institutions and policymakers have been unable to adequately keep up with these transformations generating knee-jerk, piecemeal, technologically determinist responses poorly substantiated by evidence (Cedefop, 2018).

Whilst the literature has recognised the importance of skills as a key factor in the development and adoption of AI, there has been paucity of empirical research on the nature of skill requirements in AI-mediated workplaces. Recent publications, including in this journal, have highlighted the skill gaps that arise when humans interact with AI, for example, when workers seek to make sense of AI-generated predictions (Borch and Min, 2022) or when they validate AI's decisions without understanding the basis of the decisions (Anthony, 2021). The lack of understanding of skill requirements, particularly the scarcity of micro-level data such as AI-focused skill taxonomies, has been recognised as a key barrier to advancing our understanding and measurement of the impact of AI on the future of work more broadly (Frank et al., 2019). Furthermore, the absence of an empirically grounded understanding of exactly what skills are required in AI-mediated workplaces hinders the development of effective interventions to enhance workforce's requisite skills.

Conceptually, the present discourse on AI and skills has at least two limitations. First, the term ‘skill’ is often used too loosely. For example, sometimes ‘skill’ is used as a catch-all term to denote occupations/fields such as ‘data science’ or ‘programming’, which are aggregates of skills, knowledge and qualifications rather than a single skill (e.g. Stephany, 2021). Furthermore, the discussion tends to be limited to technical skills required to develop and implement AI solutions such as machine learning [sic], NLP or data visualisation techniques (e.g. Marr, 2022). Often, the term ‘skill’ is used interchangeably with ‘competencies’ or ‘capabilities’, which are broader concepts comprising of but not limited to skills (Long et al., 2020). The variation in and inconsistency of use is partly due to the notion of skill having been historically studied within diverse disciplines including economics, anthropology, psychology, education and sociology, each of which has produced their own conceptualisations, definitions and typologies of skills, often in a non-generative manner. The conceptual imprecision and inconsistency, far from being purely of academic concern, are a practical hindrance, for they can lead to ineffectual solutions and poor policy. Whilst a detailed discussion of definitions of skills is beyond the scope of this commentary, to clarify, here I use the term ‘skills’ to mean the ability to apply knowledge to work tasks (Cedefop, 2014), comprising core skills (the technical know-how of a particular occupation, e.g. for a data scientist the ability to identify and apply correct techniques and software to analyse big data), transversal skills (such as communication, teamwork and problem-solving) and dispositions (such as self-efficacy, creativity, and emotional intelligence). The more nuanced way of thinking about skills afforded by a broader typology such as this one helps expand the current policy and practice discourse beyond the limited – and limiting – focus on technical skills, by emphasising also the uniquely human, interactional, creative and emotional capabilities that will be relevant in AI-mediated workplaces.

Second, the present discourse on AI and skills is focused on generic ‘digital skills’, a notion which originated in the earlier waves of technological development (e.g. Vuorikari et al., 2022). However, generic digital skills are only partially relevant because AI technologies are hypothesised to necessitate new, AI-specific skills such as ‘intelligent interrogation’ (the ability to correctly formulate questions to AI) or ‘fusion skills’ (the integration of human skills and machine capabilities within a business process to generate results superior to what humans or machines could produce alone) (Daugherty et al., 2019).

This commentary argues that to address these limitations and to develop an empirically grounded understanding of the transformation of skills brought about by the development and diffusion of AI technologies, an integrative approach is required. By ‘integrative’, I mean an approach that combines the perspectives of different actors/stakeholders and scientific disciplines using multimethod research designs. In doing so, the commentary responds to Jarrahi et al. (2022) who in this journal called for more research on the complex ways in which AI and humans mutually augment across contexts and who highlighted AI skills as an important aspect to be investigated.

Research agenda

I propose that at least four dimensions of integration are central to future research in this area: (a) integrative exploration of work practices and associated skill requirements across the AI production chain, comprising curation of big data underpinning AI, development of AI, and end-use of AI; (b) integrating insights from social sciences, humanities and engineering/computer science; (c) integrating quantitative and qualitative methodologies; and (d) integrating perspectives of different stakeholders within a participatory co-design framework.

Conclusions

This commentary highlighted and problematised a societally important but under-researched topic: advancing the empirical understanding of skill requirements in emergent AI-mediated work practices. The commentary proposed an integrative research agenda to guide future studies in this area, specifying four dimensions of integration. The potential outputs from such integrative research could include empirical typologies and micro-level taxonomies of skills for AI-mediated workplaces differentiated by type of key frontline actor within the AI production chain. The outputs could include rich descriptions of opportunities and challenges of AI for work and skill development that draw on a nuanced, contextualised understanding of frontline actors’ perspectives and experiences. Finally, future integrative research would produce higher-level policy and practice recommendations and guidelines on the design of skill-enhancing AI technologies and skill development interventions, differentially addressing organisational leaders, government bodies and regulators, as well as educational and vocational training institutions.