Debates about the disruptive impact of technology, the skills gap and the need to improve the representation of women in the information and communications technology (ICT) sector are not new. Nor are the gaps between the hype and the reality. Instead, what is needed is an evidence-based strategy that prepares us for whatever the future may hold. This requires challenging assumptions and being clear about what we do know and don’t know.
First, we should recognize that our conceptualization of innovation is often limited and incomplete. We equate it with the creation of new technologies, without adequate regard for their adoption. Innovation is about doing something different. But without adoption, innovation is pointless. History has shown that predictions concerning technology are generally fraught. We might be able to anticipate technology trends, but we are very bad at predicting the pace, and overestimate initial impacts while underestimating long-term effects. The Gartner Group’s hype cycle has proven to be surprisingly accurate in anticipating trends. In the 1990s, pundits predicted imminent changes that have yet to materialize: teleconferencing would replace business travel; technology-enabled learning would disrupt universities; electronic records would transform health care. Impediments to such changes had little to do with technology but related to policy or regulation, organizational culture and practices, or human preferences and behaviour. Innovation strategies often focus on investing in science, technology, engineering and math (STEM) to fuel creation of new technologies, but overlook factors that will influence their adoption – often, the domain of the social sciences and humanities (SSH).
A second issue is the echo chamber that amplifies some perspectives and excludes others, such as the near-obsession of government, business and academia with the impact of disruptive technologies. Those can include artificial intelligence, analytics, the “Internet of things” and robotics. In 2013, two Oxford academics, Carl Benedikt Frey and Michael Osborne, assessed what those impacts might be on sectors and classes of jobs, concluding that up to 47 per cent were vulnerable to automation (research cited more than 5,000 times). Others, from the World Economic Forum to Canada’s Brookfield Institute, took the methodology and applied it in a different context. In 2016, Brookfield estimated that as many as 42 per cent of jobs could be lost. “Could” be automated soon became “would” be automated, ignoring the realities surrounding technology adoption and conjuring an apocalyptic vision of entire industries being obliterated amid urgent calls to equip for the new world.
The preoccupation with disruptive technologies has also eclipsed other issues with potentially transformative effects – climate change and political responses, demographic shifts that threaten to cripple health care systems, changing social norms and values, patterns of migration. These all have implications for the future of work and skills. Tapping into more sophisticated approaches to envisaging possible futures and scenarios would better inform adaptive, agile responses.
Planning when you cannot predict is fraught. In the ’90s, John Roth, president of Nortel Networks, insisted that Canada’s burgeoning ICT sector was threatened by skills shortages and we needed to “double the pipeline” for engineers. Ontario complied, diverting resources from other disciplines to gear up for the explosive demand. Then, Nortel and the dot-com market collapsed, leaving us with under-employed engineers. Organizations like the Canadian Advanced Technology Association (CATA) pronounced that “Technological skills are not the only need….Marketers are harder to find than engineers.” While deep technology skills are essential, we need to avoid creating reductive binaries that position choices as either STEM or SSH.
The preoccupation with teaching coding earlier has been embraced by many – an irony, given that a major impact of disruptive technologies is the emergence of no-code or low-code platforms where an understanding of user needs and goals is key. Even Microsoft has recently insisted that the humanities are essential to the adoption of AI. While Canada’s Essential Skills Framework might need a refresh, its relevance is reinforced by the industry discussion of in-demand skills, which includes complex problem solving, critical thinking, creativity, people management, emotional intelligence, judgment and decision making. This could be why growing evidence suggests the employment and earnings gap between STEM and SSH graduates erodes over time, and that most science grads (unless they pursue professions like nursing, medicine or dentistry) actually earn less than SSH grads. Increasingly, “soft” skills are actually hard.
If skills are the currency in the new economy, we need clearer definitions, better assessment and more effective approaches to developing them. Research shows, for example, significant gaps in perceptions when we compare those of graduates to employers’. In one survey, over 90 per cent of students believed they were highly proficient in writing and oral communication, while employers perceived that 39.4 per cent of graduates were highly proficient in writing and 47 per cent in oral communication. Students might be good writers and producers of essays but lack an understanding of the communication tools and techniques of business, how to adapt their skills to different genres – to memos, briefs, reports and blogs. Better definitions and assessments of essential skills are key to bridging supply and demand.
We must be careful about assuming a university degree is a surrogate measure for the possession of skills. Recent research has suggested that as many as 25 percent of university graduates do not meet expected grades on standardized tests for English, mathematics and critical thinking – recognizing, of course, that some challenge the validity of such testing.
Current framing often ignores growing demands for “hybrids” – often SSH grads with knowledge of the applications of technology. For example, some companies have hired data scientists only to realize they actually needed people who can translate results into actionable strategies. And let us not ignore the fact that 46 per cent of Canadian small and medium enterprises do not have a webpage and are far from being motivated or equipped to benefit from the potential of more advanced technologies. This is, in part, because we have ignored adoption.
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Another unintended consequence of the preoccupation with STEM is the exclusion of university educated women, persons with disabilities, Indigenous Peoples and some ethnic groups more likely to graduate in SSH. While we should strive to increase diversity in STEM, we should also admit that well-intentioned approaches over the last three decades have mostly failed. There are fewer women in computer science than in 1989 and only marginally more in engineering despite massive investments in promotional campaigns. We need to set clear targets, ensure accountability with rewards (or sanctions) but also consider alternative pathways. Many female CEOs of tech companies are not STEM graduates.
Finally, employer-centred perspectives are key. We want to prepare people for jobs that actually exist. Stories of engineers and doctors driving cabs and working as security guards are not urban legends. Forty-four percent of internationally educated engineers were underemployed in 2015 compared with 21 percent of those with Canadian engineering degrees. The underemployment rate for newcomer women was more than 50 percent. University graduates with severe disabilities have worse employment outcomes than those with only high school (even though they have demonstrated the grit and determination succeed, often working much harder than other students). The challenges faced by Indigenous and LGBTQ2+ youth, while different, are equally discouraging. Research by my colleagues, led by Rupa Banerjee, showed that when identical resumes with different names were sent to prospective employers, applicants with “foreign-sounding” names were 30 percent less likely to be called for interviews in large corporations and 60 percent less likely in small and medium enterprises. Let’s be clear that the so-called skills gap is partly an artifact of employers seeking skills in all the wrong places, relying on informal networks, hiring in their own image, and being plagued by stereotypes and bias.
So what is to be done?
- Recognize what we know and do not know: the challenge of planning when you cannot predict. Consider a range of possible futures and ways to make the skills and employment system more responsive, agile and adaptable to whatever the future holds.
- Develop more common language around skills, definitions and assessments.
- Focus on what job seekers bring to the table instead of what they lack. Consider alternative pathways, upskilling and reskilling.
- Understand the importance of employer-centred approaches and stop training people for imaginary jobs.
- Ensure employers commit to more inclusive and transparent processes to take advantage of the skills that are available. Look at diverse pools of talent.
- Diversity strategies require more than rainbow posters and good intentions. Use intentional approaches that address macro-level policies, stereotypes and infrastructure. Embed diversity through the value chain; in leadership and governance, culture, procurement, product design and marketing, and in accountability frameworks.
- Harness the potential of trends in technology to bridge the skills gap. Create a more accessible and inclusive skills and employment ecosystem.
The future of work is not preordained. Governments, organizations and individuals all have choices to make. Seizing this opportunity requires us to gain a better understanding of what we already know about work and skills and, even more importantly, what we don’t know.
This article is part of the Ensuring inclusive prosperity when all boats aren’t being lifted special feature.
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