Building cutting-edge technological capabilities within their existing workforce is among the most pressing business challenges organizations face today. The accountancy firm PwC is taking a notably aggressive approach to this upskilling project, giving employees as much as 18-24 months to devote to immersive learning of new skills, with half their time spent training in these skills and the other half working with clients to put them to use. Ron Miller recently profiled the PwC’s Digital Accelerator program at TechCrunch:
[Sarah McEneaney, digital talent leader at PwC] estimates if a majority of the company’s employees eventually opt in to this retraining regimen, it could cost some serious cash, around $100 million. That’s not an insignificant sum, even for a large company like PwC, but McEneaney believes it should pay for itself fairly quickly. As she put it, customers will respect the fact that the company is modernizing and looking at more efficient ways to do the work they are doing today. …
Members of the program are given a 3-day orientation. After that they follow a self-directed course work. They are encouraged to work together with other people in the program, and this is especially important since people will bring a range of skills to the subject matter from absolute beginners to those with more advanced understanding. People can meet in an office if they are in the same area or a coffee shop or in an online meeting as they prefer. Each member of the program participates in a Udacity nano-degree program, learning a new set of skills related to whatever technology speciality they have chosen.
The program focuses on a critical set of digital skills that are increasingly in-demand and where expertise is in short supply: data and analytics, automation and robotics, and AI and machine learning. McEneany and PwC’s Chief People Officer Mike Fenlon expanded on their philosophy in a recent piece at the Harvard Business Review, detailing the process through which the program was designed and touting its success at fostering innovation and a growth mindset throughout the organization:
The Future of Jobs 2018, a new report from the World Economic Forum, includes the organization’s latest forecast of how automation will reshape the future of work. As soon as 2025, the report predicts, more than half of “all current workplace tasks” will be performed by machines, up from 29 percent today. That doesn’t mean the world is facing the mass displacement of human workers by machines: The report predicts that automation will create 133 million new jobs by 2022 even as it destroys 75 million. It does mean, however, that employers and governments need to be proactive in readying the workforce to perform the higher-skill jobs AI, robotics, and other emerging technologies will create, according to a statement from the WEF:
Based on a survey of chief human resources officers and top strategy executives from companies across 12 industries and 20 developed and emerging economies (which collectively account for 70% of global GDP), the report finds that 54% of employees of large companies would need significant re- and up-skilling in order to fully harness the growth opportunities offered by the Fourth Industrial Revolution. At the same time, just over half of the companies surveyed said they planned to reskill only those employees that are in key roles while only one third planned to reskill at-risk workers.
While nearly 50% of all companies expect their full-time workforce to shrink by 2022 as a result of automation, almost 40% expect to extend their workforce generally and more than a quarter expect automation to create new roles in their enterprise.
The WEF reached its headline figures by extrapolating from the companies it surveyed, where executives predicted a decline of 984,000 jobs and a gain of 1.74 million jobs between now and 2022. The report also finds that all industries are facing significant skills gaps, with regard to both technical skills and “distinctly human skills, such as creativity, critical thinking and persuasion.” Reskilling and upskilling the workforce for this change is “the key challenge of our time,” WEF Founder and Executive Chairman Klaus Schwab said in the statement.
According to Gartner research, the adoption of AI is poised to grow rapidly in the coming years. This and other emerging technologies like robotics are bound to fundamentally change the way we work, largely or completely automating many of today’s jobs. While this technological upheaval is generally expected to create more jobs than it destroys, the transition will be disruptive and challenging for many professionals accustomed to working in a pre-AI world. The most dire projections anticipate widespread displacement or the radical transformation of current jobs due to AI and robotics, potentially affecting tens of millions of workers in developed countries.
The effects of automation will be challenging for the clients of many HR business partners, and HRBPs will be called to provide increasing support for those impacted, such as ensuring they have access to retraining opportunities. In addition, HRBPs see themselves as part of the population affected by automation: Ten years from now, HRBPs expect nearly half of their current day-to-day responsibilities to be automated. HRBPs are optimistic, however, about the impacts that technology and automation will have on their role. Our research at Gartner finds 68 percent of HRBPs agree that automation is an opportunity to prioritize strategic responsibilities. To capitalize on this opportunity, however, HRBPs need to anticipate what work will be automated and what work will be augmented.
At a recent meeting with 70 HRBPs in New York City, we discussed predictions for the future of their role and asked them how technology has changed or will change it. Several attendees mentioned employee data collection: Previously, this was an onerous monthly or quarterly process of manually pulling together data from various sources to populate dashboards for stakeholders. Technology has made this process easier and quicker, with the use of pulse surveys and other tools. It also creates opportunities to collect data in larger quantities or more precisely, and to use it in new ways, though HR still has a lot of work to do in convincing the C-suite of the value of talent analytics.
The US Department of Labor announced last week that it was making available $100 million in “Trade and Economic Transition National Dislocated Worker Grants,” which will fund training and career services programs for workers affected by “major economic dislocations.” These grants will be disbursed to states, outlying areas, local workforce development boards, and other entities, by the department’s Employment and Training Administration, and are meant to address a variety of workforce challenges, including:
- The economic and workforce impacts associated with job loss or employer/industrial reorganization due to trade or automation;
- The loss, significant decline, or major structural change/reorganization of a primary or legacy industry, such as a manufacturing downturn due to technological advances, including impacts on the agricultural industry due to trade or other economic trends;
- Other economic transition or stagnation that may disproportionately impact mature workers, putting them at risk for extended unemployment, lower wages, and underemployment.
Applications for grants are due by September 7, and the administration plans to begin awarding funds by September 30. It will continue to fund qualifying applications in the order they are received until all of the allocated funds are spent.
This is the first major initiative from the Trump administration focused on protecting the workforce from automation-related displacement. Treasury Secretary Steven Mnuchin took criticism last year when he downplayed the potential impact of automation on job loss, arguing that technological displacement would not be an issue for another 50 years or more.
The rapid growth of e-commerce, a strengthening economy, and a rebounding in consumer spending habits have caused a spike in demand in the US trucking industry over the past few years. At the same time as the need for their services is growing, however, the country is facing a shortage of truck drivers, Kirsten Korosec reports at Fortune, with an aging population of drivers exiting the workforce and fewer young Americans willing to sign up for long, lonely hours on the road:
The pain point is specific. The industry calls them “full-truckload, over-the-road nonlocal drivers,” jargon for drivers who haul goods over long distances, often days, if not weeks, before returning home. That lifestyle just isn’t attracting millennials and the incoming Gen Z cohort who place a greater emphasis on work/life balance.
The long-haul sector, which employs around 500,000, was in need of nearly 51,000 truck drivers by the end of 2017, the worst shortage it had ever seen. The lack of qualified drivers—some trucking companies have complained only 1% to 2% of applicants meet their requirements—has businesses competing for the same pool of workers.
The shortage is creating a ripple effect. Companies vying for qualified workers are offering higher pay and signing bonuses. The median pay for drivers in this category is $59,000, according to the ATA. Experienced drivers who work for private fleets can make as much as $86,000 a year.
The truck driver shortage is not new: At CEB, now Gartner, our State of the Labor Market report for the US late last year showed that heavy and tractor-trailer truck drivers had the highest demand of all occupations, followed by registered nurses. Demand for trucking skills has been growing rapidly, but with experienced drivers retiring and not being replaced by new talent, the segment of the labor market with this skill is very small. (CEB Recruiting Leadership Council members can read the full report here.)
As machine learning algorithms are called upon to make more decisions for organizations, including talent decisions like recruiting and assessment, it’s becoming even more crucial to make sure that the performance of these algorithms is regularly monitored and reviewed just like the performance of an employee. While automation has been held up as a way to eliminate errors of human judgment from bias-prone processes like hiring, in reality, algorithms are only as good as the data from which they learn, and if that data contains biases, the algorithm will learn to emulate those biases.
The risk of algorithmic bias is a matter of pressing concern for organizations taking the leap into AI- and machine learning-enhanced HR processes. The most straightforward solution to algorithmic bias is to rigorously scrutinize the data you are feeding your algorithm and develop checks against biases that might arise based on past practices. Diversifying the teams that design and deploy these algorithms can help ensure that the organization is sensitive to the biases that might arise. As large technology companies make massive investments in these emerging technologies, they are also becoming aware of these challenges and looking for technological solutions to the problem as well. At Fast Company last week, Adele Peters took a look at Accenture’s new Fairness Tool, a program “designed to quickly identify and then help fix problems in algorithms”:
The tool uses statistical methods to identify when groups of people are treated unfairly by an algorithm–defining unfairness as predictive parity, meaning that the algorithm is equally likely to be correct or incorrect for each group. “In the past, we have found models that are highly accurate overall, but when you look at how that error breaks down over subgroups, you’ll see a huge difference between how correct the model is for, say, a white man versus a black woman,” [Rumman Chowdhury, Accenture’s global responsible AI lead,] says.
When it comes to making judgments based on large data sets, machines are often superior to humans, but many business leaders remain skeptical of the guidance produced by their organizations’ data analytics programs, particularly when it comes to talent analytics. That skepticism derives largely from doubts about the quality of the data the organization is collecting, but there is also a natural tendency among people who make strategic decisions for a living to reject the notion that an algorithm could do parts of their job as well as or better than they can.
While this may be true of executives and high-level professionals, some recent research suggests that most people are actually comfortable with the decisions algorithms make and even more trusting of them than of judgments made by humans. A new study from the Harvard Business School, led by post-doctoral fellow Jennifer M. Logg, finds that “lay people adhere more to advice when they think it comes from an algorithm than from a person”:
People showed this sort of algorithm appreciation when making numeric estimates about a visual stimulus (Experiment 1A) and forecasts about the popularity of songs and romantic matches (Experiments 1B and 1C). Yet, researchers predicted the opposite result (Experiment 1D). Algorithm appreciation persisted when advice appeared jointly or separately (Experiment 2). However, algorithm appreciation waned when people chose between an algorithm’s estimate and their own (versus an external advisor’s—Experiment 3) and they had expertise in forecasting (Experiment 4). Paradoxically, experienced professionals, who make forecasts on a regular basis, relied less on algorithmic advice than lay people did, which hurt their accuracy.
Our colleagues here at Gartner have also investigated consumers’ attitudes toward AI and found that these attitudes are more welcoming than conventional wisdom might lead you to believe. The 2018 Gartner Consumer AI Perceptions Study found that overall, consumers are not skeptical of the potential usefulness of AI, though they do have some concerns about its impact on their skills, social relationships, and privacy. The study was conducted online during January and February 2018 among 4,019 respondents in the US and UK. Respondents ranged in age from 18 through 74 years old, with quotas and weighting applied for age, gender, region, and income.