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Managers and the digital shift in employment

The article aims at giving overview information and raising questions on key aspects (labour markets, business, ethics) related to management in times of Artificial Intelligence.

From Amazon to virtual realtity: digital changes in business and the world of work seem to be underway everywhere we look at. While non-standard forms of employment are getting more widespread and digital leaders emerge, the scope and implications of changes are yet unclear. However, managers will play a key role in shaping the world of work of tomorrow: as employee representatives, as strategists and as responsible leaders.

1. Macro trends

  • Digital labour markets include online labour markets, where services are exchanged digitally, and mobile labour markets, where matching occurs online and where services are delivered locally. In countries like the US and the UK, 1-2% of the labour force is estimated to work regularly on these digital labour markets, whereas data for continental Europe is scarcer. Put roughly, the average worker on these platforms is younger, better educated (but only 10% are students) and majoritarily relies on the (low to modest) earnings as primary source of income, while being under- or self-employed. Main advantages of these digital labour markets may include: an increase of the pool of employers and workers by removing barriers and reducing transaction costs, improving matching, increasing human capital specialisation, with potential net welfare effects such as more efficient labour markets and increased employment, with potential productivity gains. Main risks may include biases (e.g. gender and ethnical), frictions and mismatches countering positive effects, precarious work increasing social risks, blockages to social mobility, wage penalties, being in a position of unfavourable information and power asymmetry, higher social protection costs as a result of an increase of nonstandard forms of work (see p. 3), as well as a lack of privacy. On average, 33% of total employment in OECD countries is in the form of non-standard forms of work (NSW) - a trend that started getting more widespread in the 90ies. > Read more in “the future of work in the ‘sharing economy’” (EC)
  • Artificial Intelligence (AI) business | The Economist called data the world’s most valuable resource, no longer oil. Alphabet (Google’s parent company), Amazon, Apple, Facebook and Microsoft, big investors in AI, collectively racked up over $25bn in net profit in the first quarter of 2017. According to McKinsey, companies invested around 26- 39bn USD on Artificial Intelligence in 2016 with an annual growth rate of 20%. The ICT, automotive and financial sectors were the strongest digital adopters. 20 percent of companies said they currently use any AI related technology at scale or in a core part of their businesses. Another estimate foresees that the European artificial intelligence market grows at a rate of 43.2% (compound annual growth rate) from 2016 to 2022. The data on the AI market should, however, be subject to caution. Compared to other continents, European managers are significantly more cautious about adopting artificial intelligence in their work and reluctant to regard intelligent machines as active partners in the workplace.
  • Changes in job structure | Instead of calculating net job gains or losses by job type, the OECD has analysed the potential for changes in job tasks and found that that just 9% of jobs are at a high risk of being automated (more than 70% of the job’s tasks are automatable). Besides routine jobs, AI is increasingly challenging (in the sense of modifying) mid- to high-skilled jobs with new high-skilled jobs emerging in most advanced economies. These jobs often involve tasks such as working with new information, interpersonal skills and solving unstructured problems. The polarisation of the occupational structure into high-skilled and low-skilled jobs and between open-ended and various atypical forms of employment may entail further polarisation of the wage structure into high-paying and low-paying jobs. > Read the OECD policy brief “Automation and Independent Work in a Digital Economy”
  • Industrial relations | Overall, there is only little data available on industrial relations in the digital economy. However, there is evidence that workers in the platform economy are organising into new employee associations and are also being brought into existing associations. Read CEPS’ “The Platform Economy and Industrial Relations”

2. Management in the digital age

  • Change of management tasks away from coordination and control to judgment work that focuses on strategy, human interaction and innovation. AI may enhance judgement and decision-making by informing about a more extensive set of organisational indicators including social interactions, information flows or outputs. For example, HR management systems can already preselect candidates based on evaluation criteria. The human ‘social’ factor is likely to remain important in employment relations, as well as giving meaning to unstructured knowledge. This includes being able to analyse data, contextualise information and extend existing frameworks of thinking (metalevel). With knowledge increasing ever more rapidly, job starters may have better knowledge than their supervisors, making horizontal relations a more adapted approach. Also social and emotional functions are increasingly entering the reign of artificial intelligence. For instance, the Jobaline Voice Analyzer assesses paralinguistic elements of speech, such as tone and inflection, and predicts which emotions a specific voice will elicit. The software processes that information to pinpoint a type of work at which an applicant might excel – irrespective of other personal factors. Furthermore, negotiation bots are starting to be tested. Experiments showed that today, these bots make their reasoning and procedure difficult to understand for humans, raising transparency and human agency questions. Read more in the CEC report “the leadership of the future” about leadership skills and organisational practices
  • Designing technology | Managers as designers bring together diverse ideas into integrated, workable, and appealing solutions and practices. In this effort, managers will need to reflect upon underlying assumptions (to avoid gender stereotyping for instance) as reflected in algorithms (e.g. cost-benefit analysis to maximise user’s utility).
  • Responsibility and ethics | With the emergence of (partly) automated decision-making in some domains, the question of responsibility and human agency is raised: who is responsible? The makers of the algorithm, its employers, its users or the technology itself? Furthermore, the question of unintended consequences of major technological innovations is to be considered by relevant actors (e.g. politicians, companies, civil society) and could become subject to the precautionary principle (e.g. bioethics).
  • A certain degree of transparency about the way the technology is conceived could prevent market concentration, incentivise responsible behaviour and inspire design innovation of the algorithms at the benefit of users.

3. Policy options

  • Democratic control | AI can be a major driver of economic growth and social progress if industry, civil society, government, and the public work together to support development of the technology and implement checks and balances to ensure accountability. Due to the often transnational nature of AI, international harmonisation of regulation is crucial. > Read more
  • Liability | European Parliament resolution on AI and robotics: In a February resolution, MEPs have asked the European Commission to propose clear liability rules (especially for self-driving cars), to consider robots legal status (shall robots be considered a legal entity similar to a company?), to enforce ethical standards and to consider creating a European agency for robotics and artificial intelligence.
  • A multidimensional framework similar to the SDGs and OECD’s well-being parameters has been developed by the Institute of Electrical and Electronics Engineers (IEEE). The framework aims at guiding the digital development by monitoring the changes brought by digital technologies in several domains. The framework overlaps partly with the Doughnut-model of planetary boundaries. > Read the CEC article on the Doughnut model here
  • Invest in AI to improve the benefits of AI, public policy makers can invest in research and development, in (public) cybersecurity to better protect intellectual property and cyberinfrastructure and to develop market competition to facilitate start up innovations. > Read more
  • Education and training | With the advent of AI, new kinds of skills (technical, digital, social skills and critical thinking) will be needed. Education needs to better accompany individuals in their learning process throughout life and professions. Thus, automation and the increased use of AI can become complementary to individual development rather than replacing jobs.
  • Adapting social safety nets to the new world of work by making them more inclusive and adapted to the changing needs of the working population, thereby facilitating labour market transitions.