Designing with People in Mind
The surprising effect of Machine Learning on the workforce
Arguably, Machine Learning (ML), and Artificial Intelligence (AI) more broadly, have moved from “early adopters” to “early majority” along the Innovation Adoption Lifecycle (i.e. Rogers’s bell curve).
This means that your company has probably already started tinkering with it or, better yet, invested in it. Actually, 89% of clients surveyed by Slalom are discussing, planning or already have AI initiatives. Overall, 48% have it on their roadmap or are building solutions.
Some very publicized examples of companies investing in ML/AI come to mind … as recently as late April 2019, Tesla (TSLA) organized an ‘Autonomy Investor Day’ solely dedicated to advertising their ‘Full Self-Driving’ capability. Based on artificial neural networks, an advanced type of Machine Learning model, this technology empowers the image recognition ability of their self-driving vehicle.
As with any new technology before it, this is merely a tool to gain a competitive advantage in the pursuit of new business opportunities. In this example, managing a self-driving fleet of “robotaxi” vehicles (i.e. driverless) with the potential to significantly undercut the competition and grab the lion share of the ride-hailing market.
While more and more companies are joining the ML bandwagon to enter new markets or gain a competitive advantage in an existing one, these technologies are getting a bad reputation in the press as a job killer due to automation and efficiency gains. But, are the numbers confirming or refuting this assumption? Are we finding more evidence pointing to a slow-down or an uptake of job demand due to those technological advances?
New technologies with disruptive potential are often considered a threat to employment as a single person can suddenly perform tasks which would have previously required several individuals or remove the need for humans all together. But studies on employment and the associated effect that those new digital capabilities will bring are surprisingly pointing to the opposite. To this point, the 2018 World Economic Forum report on ‘The Future of Jobs’ indicates that:
“[…] 75 million jobs may be displaced by a shift in the division of labour between humans and machines, while 133 million new roles may emerge that are more adapted to the new division of labour between humans, machines and algorithms”.
While the report mentions a sizeable shift in the trend toward more task hours being performed by machine rather than humans, it also highlights the creation of “Emerging in-demand roles”. Indeed, while on one side there will be a large-scale decline in jobs due to automation, on the other side there will be an even larger scale demand of new products and services created through the adoption of new technologies and other socio-economic developments, and therefore will foster associated tasks and roles.
Another study from PwC estimates that AI, robotics and other forms of smart automation could contribute up to $15 trillion to the global GDP by 2030. While those predictions could be dismissed in favour of the status quo, it is crucial to remember that History has never been kind to companies unable to adapt fast enough to changing market forces. Of course, employees need to embrace the inevitable future where automation continues to grow and focus on a host of new roles and skill sets birthed by the transformation, while companies must recognise that it isn’t only about going to the market and recruiting new talents.
The bulk of the workforce required to strive in an ML / AI proficient world will come from retrained existing employees. Not unlike mechanics of fossil fuel powered cars are converted into electric car specialists through re-tooling and re-training, traditional business analysts and software developers ought to be upskilled and re-tooled to become the motor of an AI infused data-driven future. Great examples can be found at Facebook and others who have hired top academics to not just work on core products but to run Center Of Excellence like functions to help AI propagate throughout the company. Furthermore, Microsoft is actually working hard at lowering the barrier to entry into the AI arena by developing simpler software tools to aid the development of AI-powered products.
While key roles should undeniably be filled by visionary Subject Matter Experts and Machine Learning authoritative figures, the market’s winners and losers will very much be determined through the ability of a business to upskill its existing workforce rather than acquiring a brand-new one. Striking the right balance of newly hired experts / leaders and retrained existing staff can lead to a rapid expansion of ML and AI business capabilities, allowing a company to stay relevant in the market and to secure a bright and exciting future.
