Future of work: where technology and work intersect


The future of the workplace stands to benefit from advances in technology. Technology can remove barriers to labour force participation, increase access to flexible working arrangements, enable workers to transition to new jobs or sector of employment, allow time for more complex tasks, and increase productivity and innovation.[1]

But the intersection of gender and technology also highlights workplace inequities. The under-representation of women in the education, jobs, and sectors that produce technology influences technology design and function. This can impact how society interacts with technology, entrench gender bias, and exacerbate existing workplace inequity.

Understanding these consequences will become increasingly important for the workplace as automation, robotics, and artificial intelligence (AI) feature more prominently in how business is conducted.


Women in STEM

Image is decorative and depicts a young man sitting cross-legged with a laptop

Women and girls are underrepresented in science, technology, engineering and mathematics (STEM). Globally:

  • less than one-third of female students take STEM courses in higher education
  • only 3% of students are women in information and communication technology (ICT) courses specifically[2] 
  • less than 30% of researchers are women[3] 
  • women have founded fewer start-ups.[4] 

Women working in STEM fields are paid less, published less, and are less likely to progress in their careers than men.[5] 

In Australia, there are fewer women and girls participating in STEM-related subjects at both secondary and tertiary education levels.[6] This has consequences for women’s workforce participation in STEM fields. In 2016, women accounted for only 17% of the STEM-qualified population.[7] To track and address women's under-representation in STEM, the Australian Government has established a STEM Equity Monitor. 

Even when women do enter STEM careers, retention and progression of female workers has been a challenge. Women often leave STEM careers due to lack of career progression.[8] This links to the type of employment available, in that STEM jobs are often grant dependent, based on short-term contracts, and less flexible, as well as social-cultural norms that see a lack of female role models, gender bias and stereotypes which influence career choices.[9] Career progression in STEM is also dependent on a continuous work history.[10] This disadvantages women who still usually take on the majority of unpaid care work.[11]

Gender bias by design

The lack of women in STEM influences technology design and function, creating the potential for biases that favour men. Research finds that certain technology is designed in a way that causes it to work differently and less effectively for women.[12] The same applies for people of colour,[13] people with disabilities,[14] and transgender people.[15] These inequities are likely to be exacerbated as the workplace becomes increasingly reliant on automated technologies.

For instance, technology products more compatible for use by men than women:
  • the average size of the smartphone is a better fit for men’s hands
  • voice recognition software more easily identifies men’s voices
  • cars and seat belts are better suited for men’s statures.[16] 

Working with ill-suited technology can impact women’s productivity, safety and health.[17]

AI technologies have also demonstrated gender bias. AI systems which are used during the job search and hiring processes can contribute to diversity.[18] However:

  • one study of advertisements on Google found that women received fewer advertisements encouraging the taking of high paying jobs than men, although it was unable to identify the reasons why[19]
  • machine learning algorithms have privileged male candidates’ resumes[20]
  • translation software has also used male pronouns in place of female terms, and has provided translations in line with gender stereotypes.[21]  

The functioning of AI technology relies on the labour of data labellers. Data labelling can involve a manual process whereby humans annotate and curate data,[22] which means that a person’s individual assumptions become incorporated into the data and how the technology functions.[23] The process of labelling data relies on an “invisible” global workforce and often includes workers in lower-income countries.[24]  

Technology in the workplace

Over the past several decades, the use of technology has increased in the workplace. Technology has enabled digital worksites[25] and offshoring of services,[26] replaced face-to-face contact in service and retail industries,[27] and and can assess applicants’ suitability for positions of employment.[28]

Over the past twenty years in Australia, the robot-to-worker ratio has tripled, predominantly in low-skill jobs.[29] However, Australia’s robot-to-worker ratio is lower when compared to other OECD economies and technology has not yet had dramatic impact on employment levels in Australia[30] due, in part, to a lack of business investment in new technologies.[31]

Technology will continue to feature in the workplace, particularly through automation. Estimates suggest that by 2030 automated technologies may displace up to one-third of work activities and require 3% to 14% of the global workforce to change occupations.[32] In Australia, recent estimates suggest that about 9% of existing jobs could be susceptible to automation,[33] while other projections suggest that 40% of Australian jobs are susceptible to automation.[34] These jobs losses are likely to have the most impact on workers with lower levels of educational attainment.[35]

The rate of job displacement by automation will depend on the pace of adoption of automation.[36] Technological improvements and use without complementary job creation will likely exacerbate and entrench long-term labour market trends. This includes decreasing labour demand, declining labour share, lower wages and rising inequality.[37]

Other more optimistic views suggest productivity gains and economic growth from technological improvements.[38] Technology can:

  • create new tasks[39]
  • enable entrepreneurship[40] 
  • reinvent jobs.[41]

In addition, because of society’s preference that humans provide certain tasks and services, the effect on certain jobs will be less.[42]

Technology can also complement workers’ tasks, freeing up workers to spend time on complex problems.[43] As technology improves, workers can spend less time on repetitive manual tasks and more time employing their social, emotional, and cognitive skills on the job.[44] This is particularly the case for skilled work, an area that has seen an increase in Australia over the past two decades.[45]

Technology and women in the workplace 

While all workers will need to adapt to spending more hours working alongside automated technologies,[46] it is likely that more women’s jobs will be displaced by automation.[47] This could have a negative impact on the gains made in increasing women’s workforce participation.[48]

Research finds that there is a risk of displacement by automation for a higher proportion of the female workforce. [i][49] Another analysis[ii] suggests that roughly the same proportion of the female and male workforce may need to transition into other, often higher-skilled, jobs, due to automation. However, the analysis highlights the gendered impact of increased automation and transitioning to new jobs.[50] For instance, women face certain challenges, such as having less time to learn new skills and search for employment, because they are responsible for more care work and have less access to technology.[51] Therefore, there is a need for occupational adjustments, such as investments in women’s education and training, so that gender inequality in the workplace does not increase.[52] In Australia, the use of robots in mainly low-skilled jobs has more of an effect on women’s employment.[53]

The impact of automation on women in the workforce is due to the fact that women’s jobs include more routine tasks than men’s, making them more easily codifiable and more susceptible to automation.[54] This links to women’s educational attainment and under-representation in management positions.[55] However, even within management positions, women managers have more repetitive and less complex content than male managers.[56] The gendered impact of automation is also associated with job segregation that sees women and men more often employed in certain types of work.[57] The norms that influence job segregation effect individual-level choices about education, career,[58] and future career options, and have implications for how automation will impact women and men.[59]

Relatedly, women’s current under-representation in STEM will impact career mobility and transition amidst technological advancements in the workplace.[60] Research shows that globally, women’s economic participation and opportunity is already stalling due, in part, to their larger representation in employment that is being automated and their under-representation in growing fields of employment, including the STEM field.[61] This means that, without intervention, women stand to benefit less from new jobs created due to technology.[62]


[i] However, because women’s workforce participation rate is lower than men’s, this means that there is actually a greater number of men at risk of displacement due to automation.

[ii] These economies comprise half the world’s population and 60% of global GDP.

Towards more gender equitable tech

Importantly, advanced technologies are organisational technologies.[63] They involve active decision-making processes about how to develop, monitor and interact with technology. As the role of automated technologies increases, measures aimed at women’s education and skill-building, flexible working arrangements, and increased access to technology will benefit women in the workplace.[64]

Australia is focusing on STEM and education as one way to ensure the future of work remains accessible to women and men. The advancing women in STEM strategy encourages gender equity in STEM through girls’ and women’s inclusion in STEM education and employment and by making women in STEM more visible.[65] Internationally, UNESCO is collecting data on why women may or may not pursue STEM careers so as to better target policy aimed at women’s involvement in STEM.[66]

These measures are valuable. As many jobs will likely require the use of STEM skills, such skills may become an entry-level requirement for employment.[67] The ongoing underrepresentation of women in STEM risks reducing their workforce participation. Policy making and organisational priorities that emphasise gender equality and diversity are critical to the future of work.


Reference list

[1] Madgavkar, A, Krishnan, M & Ellingrud, K (2019), Will automation improve work for women – or make it worse? Harvard Business Review, viewed 11 May 2020, available: https://hbr.org/2019/07/will-automation-improve-work-for-women-or-make-it-worse.

[2] United Nations Educational, Scientific and Cultural Organisation (UNESCO) (2017), Cracking the code: Girls’ and women’s education in science, technology, engineering and mathematics (STEM), viewed 25 March 2020, available: https://unesdoc.unesco.org/ark:/48223/pf0000253479.

[3] UNESCO Institute of Statistics (2020), Women in science, viewed 25 March 2020, available: http://uis.unesco.org/en/topic/women-science.

[4] Stripe (2017), La #NouvelleVague de l’écosystème Français Des Startups, Technology, viewed 30 March 2020, available: https://www.slideshare.net/StripeonDeck/nouvellevague; Teare, G & Desmond, N (2017), Female Founders On An Upward Trend, According To CrunchBase TechCrunch (blog), viewed 30 March 2020, available: http://social.techcrunch.com/2015/05/26/female-founders-on-an-upward-trend-according-to-crunchbase/.

[5] UNESCO Institute of Statistics (2020).

[6] Australian Government Department of Industry, Science, Energy and Resources (2019), Snapshot of disparity in STEM, viewed 18 March 2020, available: https://www.industry.gov.au/data-and-publications/advancing-women-in-stem-strategy/snapshot-of-disparity-in-stem.

[7] Australian Government Department of Industry, Science, Energy and Resources (2019).

[8] Australian Academy of Science (2019), Women in STEM Decadal Plan, viewed 18 March 2020, available: https://www.science.org.au/files/userfiles/support/reports-and-plans/2019/gender-diversity-stem/women-in-STEM-decadal-plan-final.pdf.

[9] Australian Government Department of Industry, Science, Energy and Resources (2019).

[10] Australian Government Department of Industry, Science, Energy and Resources (2019).

[11] Workplace Gender Equality Agency (WGEA) (2019), Australia’s gender equality scorecard: Key findings from the Workplace Gender Equality Agency’s 2018-19 reporting data, viewed 6 March 2020, available: https://www.wgea.gov.au/sites/default/files/documents/2018-19-Gender-Equality-Scorecard.pdf.

[12] Magnet, S (2011), When biometrics fail: Gender, race, and the technology of identity. Duke University Press; Perez, C.C. (2020), Invisible women: Exposing data bias in a world designed for men. Vintage.

[13] Hankerson, D, Marshall, AR, Booker, J, El Mimouni, H, Walker, I & Rode, JA (2016), Does technology have race? In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 473–486. CHI EA ’16, New York, NY: ACM. https://doi.org/10.1145/2851581.2892578; Magnet (2011).

[14] Magnet (2011).

[15] Hamidi, F, Scheuerman, MK, & Branham, SM (2018), Gender recognition or gender reductionism?: The social implications of embedded gender recognition systems, In CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Paper No 8, pp. 1–13, https://doi.org/10.1145/3173574.3173582.

[16] Perez (2020).

[17] Perez (2020).

[18]Koh, Y (2017) How language in job listings could widen silicon valley’s gender divide, Wall Street Journal, viewed 30 March 2020, available: https://www.wsj.com/articles/how-language-in-job-listings-could-widen-silicon-valleys-gender-divide-1513189821; Silverberg, D (2018), Why do some job adverts put women off applying? BBC, viewed 30 March 2020, available: https://www.bbc.com/news/business-44399028.

[19] Datta, A, Tschantz, MC & Datta, A (2015), Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination, Proceedings on Privacy Enhancing Technologies, issue 1, pp. 92–112. https://doi.org/10.1515/popets-2015-0007.

[20] Reuters (2018), Amazon ditched AI recruiting tool that favoured men for technical jobs. The Guardian, viewed 30 March 2020, available: https://www.theguardian.com/technology/2018/oct/10/amazon-hiring-ai-gender-bias-recruiting-engine.

[21] Perez (2020).

[22] See Kolog, EA, Owusu, A, Devine, SNO & Entee, E (2020), Data avalanche: Harnessing for mobile payment fraud detection using machine learning, In Boateng, R (ed.), Handbook of Research on Managing Information Systems in Developing Economies, IGI Global.

[23] Hagendorff, T & Wezel, K (2019), 15 challenges for AI: or what AI (currently) can’t do, AI & Society, 1-11, DOI:10.1007/s00146-019-00886-y.

[24] Hagendorff, T & Wezel, K (2019); Madhumita, M (2019), AI’s new workforce: the data-labelling industry spreads globally, Financial Times, viewed 11 May 2020, available: https://www.ft.com/content/56dde36c-aa40-11e9-984c-fac8325aaa04.

[25] Schwartz, J, Hatfield, S, Jones, R & Anderson, S (2019), What is the future of work? Redefining work, workforces, and workplaces, Deloitte, viewed 8 May 2020, available: https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work/redefining-work-workforces-workplaces.html.

[26] Hajkowicz, S, Reeson, A, Rudd, L, Bratanova, A, Hodgers, L, Mason, C & Boughen, N (2016), Tomorrow’s digitally enabled workforce: Megatrends and scenarios for jobs and employment in Australia over the coming twenty years, CSIRO, Brisbane.

[27] Chui, M, Manyika, J & Miremadi, M (2015), Four fundamentals of workplace automation, McKinsey Digital, viewed 6 March 2020, available: https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/four-fundamentals-of-workplace-automation.

[28] Marr, B (2019), Artificial Intelligence in the workplace: How AI is transforming your employee experience, Forbes, viewed 6 March 2020, available: https://www.forbes.com/sites/bernardmarr/2019/05/29/artificial-intelligence-in-the-workplace-how-ai-is-transforming-your-employee-experience/#16a8db3353ce.

[29] Cassells, R, Duncan, A, Mavisakalya, A, Phillimore, J, Seymour, R & Tarverdi, Y (2018), Future of work in Australia: Preparing for tomorrow’s world, Bankwest Curtin Economics Centre, viewed 25 March 2020, available: https://bcec.edu.au/assets/BCEC-Future-of-Work-in-Australia-Report.pdf.

[30] Chester, K (2018), The future of work: Is it something completely different? The future of work series, Luncheon Address, Productivity Commission, viewed 6 March 2020, available: https://www.pc.gov.au/news-media/speeches/future-work/future-work.pdf.

[31] Stanford, J (2018), The future of work is what we make it, Submission to Senate Select Committee on the Future of Work and Workers, The Australia Institute, Centre for Future Work.

[32] Manyika, J, Lund, S, Chui, M, Bughin, J, Woetzel, J, Batra, P, Ko, R & Saurabh, S (2017), Jobs lost, Jobs gained: Worforce transitions in a time of automation, McKinsey Global Institute, viewed 18 March 2020, available: https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Future%20of%20Organizations/What%20the%20future%20of%20work%20will%20mean%20for%20jobs%20skills%20and%20wages/MGI-Jobs-Lost-Jobs-Gained-Report-December-6-2017.ashx.

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[34] Cassells et al. (2018).

[35] Borland, A & Coelli, M (2017), Are robots taking our jobs? Australian Economic Review, vol. 50, no. 4, pp.377-397.

[36] Madgavkar, A, Manyika, J, Krishnan, M, Ellingrud, K, Yee, L, Woetzel, J, Chui, M, Hunt, V & Balakrishnan, S (2019), The future of women at work: Transitions in the age of automation, McKinsey Global Institute.

[37] Acemoglu, D & Restrepo, P (2019a) The wrong kind of AI? Artificial Intelligence and the Future of Labour Demand, working paper no. 25682, Cambridge, MA, National Bureau of Economic Research. https://doi.org/10.3386/w25682.n

[38] Manyika, J & Sneader, K (2018), AI, automation, and the future of work: Ten things to solve for, McKinsey Global Institute, Executive Briefing, viewed 18 March 2020, available: https://www.mckinsey.com/featured-insights/future-of-work/ai-automation-and-the-future-of-work-ten-things-to-solve-for.

[39] Acemoglu, D & Restrepo, P (2019b), Automation and new tasks: How technology displaces and reinstates labour, working paper no. 25684, Cambridge, MA, National Bureau of Economic Research, https://doi.org/10.3386/w25684.

[40] Hajkowicz et al. (2016).

[41] Chui, M, Manyika, J & Miremadi, M (2015), Four fundamentals of workplace automation, McKinsey Quarterly, viewed 18 March 2020, available: https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/four-fundamentals-of-workplace-automation.

[42] 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 Paper 189. https://doi.org/10.1787/5jlz9h56dvq7-en; Madgavkar et al (2019).

[43] Hajkowicz et al. (2016).

[44] Madgavkar et al (2019).

[45] Hajkowicz et al. (2016).

[46] Manyika, et al. (2017).

[47] Brussevich, M, Dabla-Norris, E, Kamunge, C, Karnane, P, Khalid, S & Kochhar, K (2018) Gender, technology and the future of work, IMF Staff Discussion Note 18/07.

[48] Brussevich et al. (2018).

[49] Brussevich et al. (2018).

[50] Madgavkar et al. (2019).

[51] Madgavkar et al. (2019).

[52] Madgavkar et al. (2019).

[53] Cassells et al. (2018).

[54] Brussevich et al. (2018).

[55] Brussevich et al. (2018).

[56] Cassells et al. (2018).

[57] Brussevich et al. (2018).

[58] World Economic Forum (2020), Global gender gap report 2020, viewed 30 March 2020, available: http://www3.weforum.org/docs/WEF_GGGR_2020.pdf.

[59] Madgavkar et al. (2019).

[60] Madgavkar et al. (2019).

[61] World Economic Forum (2020).

[62] Madgavkar et al. (2019)

[63] See Fleming, P (2019), Robots and organisation studies: Why robots might not want to steal your job, Organisation Studies, vol. 40, no. 1, pp. 23–38.

[64] Madgavkar et al. (2019).

[65] Australian Government Department of Industry, Science, Energy and Resources (2020), Advancing women in STEM strategy: 2020 Action Plan, viewed 3 April 2020, available: https://www.industry.gov.au/data-and-publications/advancing-women-in-stem-strategy/2020-action-plan; Australian Government Department of Industry, Innovation and Science (2019), viewed 3 April 2020, available: https://www.industry.gov.au/sites/default/files/2019-04/advancing-women-in-stem.pdf.

[66] UNESCO Institute of Statistics (2020).

[67] Hajkowicz et al. (2016).