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.
A recent report from the Organization for Economic Cooperation and Development finds that the number of jobs at risk of displacement due to automation in the coming years is probably smaller than previous forecasts have estimated. Nonetheless, the tens of millions of workers in developed countries are still at risk of having their jobs replaced or radically altered by AI and robotics. The Verge’s James Vincent summarizes the report’s findings:
The researchers found that only 14 percent of jobs in OECD countries … are “highly automatable,” meaning their probability of automation is 70 percent or higher. This forecast … is still significant, equating to around 66 million job losses.
In America alone, for example, the report suggests that 13 million jobs will be destroyed because of automation. “As job losses are unlikely to be distributed equally across the country, this would amount to several times the disruption in local economies caused by the 1950s decline of the car industry in Detroit where changes in technology and increased automation, among other factors, caused massive job losses,” the researchers write.
The analysis from the OECD, an inter-governmental organization representing the world’s 35 richest countries, is considerably less disconcerting than previous studies that have calculated the risk of automation at anywhere from 30 percent to fully half of all the work currently being performed globally. One difference between this study and previous ones, Vincent explains, is that it pays greater attention to details like whether a job can be fully or only partly automated and the variations among jobs that may have the same title but whose work differs substantially:
A recent Gartner survey of Chief Information Officers finds that while just four percent have already implemented AI in some form in their businesses, 46 percent have plans in place to do so. Although there are many obstacles to implementing this groundbreaking technology, soon companies that fail to take advantage will lag behind. To help ease the potential pains of diving into adoption, our colleagues who conduct IT management research at Gartner have four recommendations to ensure success in the early stages of AI implementation: start small; focus on helping, not replacing, people; plan for knowledge transfer; and choose transparent solutions.
“Don’t fall into the trap of primarily seeking hard outcomes, such as direct financial gains, with AI projects,” Gartner analyst Whit Andrews explains. “In general, it’s best to start AI projects with a small scope and aim for ‘soft’ outcomes, such as process improvements, customer satisfaction or financial benchmarking.”
Early forays into AI should be learning experiences rather than attempts at large-scale change that dramatically reshape a department or function. It’s important to set modest goals for AI initiatives, given that the most important outcome will be gaining the knowledge and expertise to successfully apply the technology to a work stream. Additionally, while many employees fear AI could replace them, the easiest way to assuage those concerns is to deploy AI solutions that make employees’ lives easier. As Gartner EVP Peter Sondergaard remarked in his observations from the recent World Economic Forum in Davos, Switzerland, AI is expected to create many more jobs than it destroys, while generating massive value and saving billions of hours of worker productivity.
That means there’s an opportunity to get employees engaged with AI adoption as a technology that will make their jobs easier, rather than obsolete.
Last October, Walmart announced that it was rolling out shelf-scanning robots at 50 stores throughout the US after piloting them at a smaller number of locations in Arkansas, Pennsylvania, and California. The robots are taking over some of the menial busywork that used to occupy employees on the store floor: checking shelves for out-of-stock items, incorrect prices, and wrong or missing labels.
At the MIT Technology Review, Erin Winick recently talked to Martin Hitch, chief business officer at Bossa Nova, the San Francisco-based robotics firm that created the machines, about how employees and customers were reacting to them. While you might expect employees to resent having their work automated or fear that the robots would put them out of a job, Hitch said employees “instantly become the advocates for the robot”:
One way they do that is by giving it a name—the robots all have Walmart name badges on. The employees have competitions to see what the right name is for each robot. They also advocate for the robot to the general public. It’s the store staff saying, “It’s helping me.” We see them now defending the robot.
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The 2018 World Economic Forum, recently concluded in Davos, Switzerland, brought together political, business, and cultural leaders from around the globe to discuss the future of the global economy and its foremost institutions. Gartner EVP Peter Sondergaard was on hand to take in the events and speak with influencers at the forum, where he observed a few key themes in discussions of the future of the workplace: The increasingly digital nature of business, the rise of artificial intelligence, and the impact technology can have on improving diversity and inclusion.
“It became abundantly clear that organizations have reached the point at which the digital workplace must be driven by both CIOs and heads of HR,” Sondergaard explained. This doesn’t mean technology will eliminate the need for people, just that employees will need to work in different ways and companies will need to offer guidance on how to do that. “Such changes will require new models of learning and development,” he continued, “as well as the creation of hybrid workplaces that combine technology and information to accommodate a mix of employees.”
Certainly, we have seen a wide range of technologies promise to reshape how the people and processes of the workplace operate, but artificial intelligence is the driving force behind the most groundbreaking offerings. It’s powering Google Jobs, wearable tech, analytical tools, and voice-activated tech such as Amazon’s Alexa, as well as the automation of processes from candidate sourcing to performance management. As a result, demand for AI talent has skyrocketed as technology providers are scrambling to keep up with the rapid rate of change.
While the rise of AI has fueled fears of the potential for a massive loss of jobs, Sondergaard is confident that AI should ultimately create jobs if deployed properly. “As was true of the Industrial Revolution,” he also pointed out, “technological advances as a result of AI will spur job creation. In 2020, AI will create 2.3 million jobs, while eliminating 1.8 million — a net growth of half a million new positions. Organizations will realize an added benefit as in 2021 AI augmentation will generate $2.9 trillion of business value and save 6.2 billion hours of worker productivity.”