Abstract
In the current debate over the Future of Work, there is little discussion about how firms anticipate the evolution of their demand for labor and the related mix of skills as they adopt Artificial Intelligence (AI) tools. This article contributes to this debate by leveraging a global survey of 3000 firms in 10 countries, covering the main sectors of the economy. Descriptive statistics from the survey are complemented by econometric analyses of corporate labor demand decisions. The findings are four-fold. First, those are still early days in the absorption of AI technologies, with less than 10% of companies investing in a majority of AI technologies and for multiple purposes. Second, if an aggregate portion of firms anticipates reducing employment as a result of adopting AI technologies, as many other companies anticipate labor growth or reorganizing employment. Third, this reallocation picture holds true when we examine further demand by labor functions and skills, with talent shifting toward more analytic, creative, and interaction skills, and away from administrative and routine-based functions, in line with past trends of skill- and routine-biased technological change. Fourth, a novel to the literature on Future of Work, econometric results on employment change highlight that employment dynamics are driven by related spillover effects to product markets. Higher competition, larger expectations of market (share) deployment may counterbalance negative automation effect on employment dynamics.
This article is based on a series of work launched at the McKinsey Global Institute where the author was a director until end of Dec 2019. This article is a personal contribution and does not engage any of the institutions mentioned. All errors and omissions are the author’s own.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Discussion with Mark Purdy from Accenture research at the G-20Y in Evian, Sept.
- 2.
See A future that works: Automation, employment, and productivity, McKinsey Global Institute, January 2017.
- 3.
As a case in point, let us consider the Associated Press news agency, which used to deliver reports on large corporations using 65 journalists in its newsroom. With AI technologies, the company quickly managed to automate the production of simple stories of quarterly earnings for 10 times as many small companies in the long-tail. This output gain was not done at the expenses of reporters; the in-house reporters did not lose their jobs, but were instead redirected to write longer research article on business trends as a major latent demand spotted by the company. See for detailed reference, the article by Ramaswamy, S (2017) at https://hbr.org/2017/04/how-companies-are-already-using-ai.
- 4.
Substitution may arise when, furthermore, the economics are attractive to replace human capital for example.
- 5.
- 6.
- 7.
We are rather keen to understand the expectations of firms as the current level of AI diffusion across all technologies is still relatively low.
- 8.
Here we show results in aggregate, but the same picture is also visible by industry.
- 9.
The same skill-biased tendency is also noticeable in the econometric analysis conducted by Arntz et al. (2016) linking occupations and tasks to the OECD PIACC skill database.
- 10.
Note that the sample used will concern only firms which are aware of, but not necessarily adopting, AI technologies. We sub-select those firms, and survey responses on the impact on AI are likely not to be largely noisy for those respondents with limited understanding of AI technologies
- 11.
With no loss of generality, we drop the suffix r hereafter.
- 12.
See also Ugur et al. (2016).
- 13.
The preponderance of empirical estimates on the substitution between labor and capital point to σ < 1, see Chirinko (2008).
- 14.
We do not have any mean to split this variable in terms of investment objectives, however.
- 15.
We do not include AILE as otherwise, we have perfect multicollinearity.
- 16.
There is a case for a selection bias in the sense that we only concentrate on firms aware of AI. However, firms not aware of AI have not given data, and if yes, noisy ones, so we can not control for them. We tried a Heckman correction where we try to predict awareness or not of AI in a first step. But it is rather difficult to have specific regressor for this first step.
- 17.
See however the asymmetry- in this present case, employment decline has still a positive probability; while employment increase in the previous case was nil. Everything being equal, it still suggests that employment pressure may be happening along automation.
- 18.
References
Acemoglu, D., & Resterpo, P. (2017). Robots and jobs – Evidence from US labor markets. NBER working paper 23285, March.
Acemoglu, D., & Restrepo, P. (2018). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542.
Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A comparative analysis. OECD Social, Employment and Migration Working Papers, No. 189.
Atkinson, R. (2013). Stop saying robots are destroying jobs – they are not, Technology Review, September.
Autor, D. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 7–30.
Autor, D., & Handel, M. (2013). Putting tasks to the test: Human capital, job tasks, and wages. Journal of Labor Economics, 31(2), S59–S96.
Autor, D., & Salomons, A. (2017). Does productivity growth threaten employment. Paper presented at Cintra at the EIB meeting, June 19.
Autor, D., & Salomons, A. (2018). Is automation labor-displacing? Productivity growth, employment, and the labor share. Brookings Papers on Economic Activity, 2018, 1–87.
Brynjolfsson, E., & Hitt, L. M. (2003). Computing productivity: Firm-level evidence. Review of Economics and Statistics, 85(4), 793–808.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress and prosperity in a time of brilliant technologies. New York: W.W. Norton & Company.
Brynjolfsson, E., Rock, D., & Syverson, C. (2017). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. NBER Working Paper 22401, National Bureau of Economic Research, Cambridge, MA.
Bughin, J. (2016). Reaping the benefits of big data in telecom. Journal of Big Data, 3(1), 14.60(2): 338–355.
Bughin, J., & van Zeebroeck, N. (2018). Why a digital base is critical for AI. McKinsey Quarterly, Issue 1. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/artificial-intelligence-why-a-digital-base-is-critical
Bughin, J., Staun, J., Andersen, R., Schultz-Nielsen, M., Aagaard, P., & Enggaard, T. (2017). Digitally-enabled automation and artificial intelligence: Shaping the future of work in Europe’s digital front-runners. McKinsey, October.
Chirinko, R. S. (2008). Sigma: The long and short of it. Journal of Macroeconomics, 30, 671–686. The CES Production Function in the Theory and Empirics of Economic Growth.
Comin, D., & Hobijn, B. (2010). An exploration of technology diffusion. The American Economic Review, 100(5), 2031–2059.
Deming, J. (2017). The growing importance of social skills in the labor market. The Quarterly Journal of Economics, 132(4), 1593–1640.
Deming, D., & Kahn, L. B. (2018). Skill requirements across firms and labor markets: Evidence from job postings for professionals. J Labor Economics, 36, S337–S369.
Dinlersoz, E., & Wolf, Z. (2018). Automation, labor share, and productivity: Plant-level evidence from US manufacturing No. 18–39.
Dupuy, A., & de Grip, A. (2003). Do large firms have more opportunities to substitute labour than small firms. Center for Labour Market and Social Research Working Paper.
Frey, C., & Osborne, M. (2013). The future of employment: How susceptible are jobs to computerisation?. Oxford Martin School Working Paper.
Garcia, A., Jaumandreu, J., & Rodriguez, C. (2002). Innovation and jobs- evidence from manufacturing industries. Discussion paper, Universidad Carlos III de Madrid, June.
Goos, M., Konings, J., & Vandeweyer, M. (2015). Employment growth in Europe: The roles of innovation, local job multipliers and institutions. Utrecht School of Economics Discussion Paper Series, Vol. 15, No. 10.
Gregory, T., Salomons, A., & Zierahn, U. (2016). Racing with or against the machine, Evidence from Europe, ZEW. Discussion paper 16-053.
Handel, M. (2016). Dynamics of occupational change: Implications for the occupational requirements survey. Research paper prepared for the US Bureau of Labor Statistics, July, bls.gov
Holtgrewe, U. (2014). New new technologies: The future and the present of work in information and communication technology. New Technology, Work and Employment, 29(1), 9–24.
Keynes, J.M. (1931), “The Economic Possibilities for our Grandchildren” in J.M. Keynes, Essays in Persuasion, Macmillan, London.
Machin, S. (2001). The changing nature of labour demand in the new economy and skill-biased technology change. Oxford Bulletin of Economics and Statistics, 63(s 1), 753–776.
Martinez, J. (2018) Automation, growth and factor shares. No. 736. Society for Economic Dynamics.
McKinsey Global Institute. (2017a). A future that works: Automation, employment, and productivity. January: McKinsey Global Institute.
McKinsey Global Institute. (2017b). Artificial Intelligence – The next digital frontier? McKinsey Global Institute, May.
McKinsey Global Institute. (2018a). Skill shift: Automation and the future of work. McKinsey Global Institute, May.
McKinsey Global Institute. (2018b). Notes from the AI frontier: Modelling the impact of AI on the world economy. McKinsey Global Institute, September.
Milkman, R., & Pullman, C. (1991). Technological change in an auto assembly plant: The impact on workers’ tasks and skills. Work and Occupations, 18(2), 123–147.
Nubler, I. (2016), New technologies: A jobless future or golden age of job creation? ILO, Working paper 13, November.
OECD. (2017). Future of work and skills. Paper presented at the 2nd meeting of the G20 Employment Working Group, 15–17 February 2017.
Peters, B. (2004). Employment effects of different innovation activities- microeconomic evidence, ZEW discussion paper 4–73., Mannheim, Zentrum fur Europäische Wirtschaftsforschung.
Pissarides, Ch., & Bughin, J. (2018). Embracing the new age of automation, Project syndicate, January. https://www.project-syndicate.org/commentary/automation-jobs-policy-imperatives-by-christopher-pissarides-and-jacques-bughin-2018-01
Spiezia, V., & Vivarelli, M. (2000). The analysis of technological change and employment. In M. Pianta & M. Vivarelli (Eds.), The employment impact of innovation. London: Routledge.
Susskind, D. (2020). A world without work: Technology, automation, and how we should respond. London: Allen Lane.
Ugur, M., Awaworyi, S., & Solomon, E. (2016). Technology innovation and employment in derived labour demand models: A hierarchical meta-regression analysis. MPRA paper, Munich, paper 73557, September.
Vivarelli, M. (2014). Innovation, employment, and skills in advanced and developing countries: A survey of the economic literature. Journal of Economic Issues, 48, 123–154.
Walvei, U. (2016). Digitization and structural labor market problems. ILO research paper, 17.
Whitehead, J. C., Groothuis, P. A., & Blomquist, G. C. (1993). Testing for non-response and sample selection bias in contingent valuation: Analysis of a combination phone/mail survey. Economics Letters, 41(2), 215–220.
Zeira, J. (2018). Workers, machines, and economic growth. The Quarterly Journal of Economics, 113, 10911117.
Acknowledgements
We thank Eric Hazan and Peter Gumbel for comments and editing, as well as Dr. Chris Pissarides, Nobel Prize winner, for suggestions. All remaining mistakes are mine.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bughin, J. (2020). Artificial Intelligence, Its Corporate Use and How It Will Affect the Future of Work. In: Paganetto, L. (eds) Capitalism, Global Change and Sustainable Development. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-46143-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-46143-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46142-3
Online ISBN: 978-3-030-46143-0
eBook Packages: Economics and FinanceEconomics and Finance (R0)