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.
The digital age has its pros and cons for the workforce. Technology provides employees with faster, easier access to information and data. It also allows for greater personalization and more interaction between employee and employer. Yet the digitalization of the workplace does have its downsides. Consider smartphones, for example: They can be alternately distracting and distressing; they can create barriers to action like information overload and decision fatigue, as well as work-life balance issues stemming from an “always-on” mentality.
Some managers, frustrated with the ubiquity of these devices and their ability to distract employees, are banning phones from meetings or otherwise limiting their use in the workplace, the Wall Street Journal’s John Simons wrote in a feature last week. Simons points to studies indicating that executives and managers consider smartphones “the leading productivity killers in the workplace” and that the presence of a phone can harm people’s cognitive performance, even when they are not using or holding it. He also notes Google’s recent announcement that the next version of its Android operating system will introduce a feature enabling users to see how much time they spend on their phones, which apps they use the most, and how often the phone gets unlocked.
Our recent research at CEB, now Gartner, also underscores these downsides of technology at work. While solutions to help employees minimize time wasted on tech, like Google’s forthcoming Android time tracker, might be helpful, our research suggests that no technological intervention can have a meaningful impact on employee performance or the employee experience by itself. The limitations are striking, given the large investments organizations (and HR functions in particular) are making in technology to support employees. But the challenges employers face are human and organizational, not just technological—and the same must be true of any solution.
LinkedIn is developing a major new training program to teach its employees about artificial intelligence, which it predicts will be a part of everything they do in the near future, GeekWire’s Nat Levy reported last week:
The AI Academy program will start with classes for engineers, product managers and executives, but the company hopes to expand it so every employee can gain some degree of AI expertise. The first cohort from LinkedIn engineering just started going through the program, but the company is already looking at making the AI Academy part of its onboarding process for all new employees. There are four levels of classes, each one a deeper dive than the last. When participants are done, LinkedIn wants them to have an understanding one of the most important issues in the field: which problems AI can solve and which ones it can’t.
LinkedIn’s Head of Science and Engineering Craig Martell writes in a blog post about the program that AI is “like oxygen—it’s present in every product that we build and in every experience on our platform.” There it has common ground with parent company Microsoft. Like LinkedIn, Microsoft has infused AI into many of its major initiatives, and it offers an online training program for developers.
Microsoft has made huge bets on AI and sought to position itself as a leader in the field, hiring thousands of scarce and expensive AI experts and building AI functionality into its suite of enterprise products—with which LinkedIn is also becoming increasingly integrated. In that context, it’s no surprise to see LinkedIn make AI knowledge a priority for its own workforce: They’re going to need it.
In a recent overview of gamification technologies at Employee Benefit News, John Soat looked at the growing number of ways in which organizations are gamifying HR processes. Soat highlighted three areas in which gamification is most promising: pre-hire assessments for recruiting, training programs for current employees, and encouraging participation in wellbeing initiatives and other benefits programs. Game-like tools are popular and effective because they are fun and engaging, so employees are more likely to use them voluntarily, even outside working hours. This impact on engagement, Soat hears from vendors, is part of the often intangible ROI their clients see from gamification.
This is a trend we’ve been following both here at Talent Daily and in our research at CEB, now Gartner, for several years now. Looking at how various organizations have gamified their processes, we’ve discovered some surprising use cases for this approach and developed a robust understanding of what makes gamification initiatives most likely to succeed.
In the training space, it’s interesting to note that companies aren’t just using gamification for entry level or technical skills. In 2014, we profiled GE’s Experienced Leaders Challenge: a week-long, immersive development session for experienced GE leaders designed to help them develop a leadership mindset aligned to today’s inherently unpredictable business environment. A key part of the program is a simulation that lets leaders practice navigating common challenges and observe the unexpected consequences of their decisions or actions. (CEB Corporate Leadership Council members can check out the full case study here.)
The explosion of technology available to people outside the workplace is forcing employers to keep up with their employees’ digital expectations within it. In our 2018 Digital Employee Experience Survey, CEB, now Gartner, found that 74 percent of employees say they expect more access to state-of-the-art technology at work today than three years ago. However, these technologies can take different forms: Motorola’s modular phones, for instance, offer a multitude of features ranging from camera styles to gaming platforms, while the Light Phone markets itself as a “dumb phone” with features limited to calling and texting.
Both of these phones are at the leading edge of mobile technology, but they offer their users dramatically different experiences and are marketed to different sets of consumers with different preferences. HR leaders face a similar challenge in choosing from the growing range of options available to them how to most effectively deliver technology to their own consumers: employees.
When deploying new digital technologies, most HR functions focus on making as many digital solutions available to employees as possible. Many accomplish this by putting their HR resources into an app, providing “on-demand access” where all information is available anytime and anywhere. Just like we as consumers are used to having access to most types of information on demand outside of work, replicating that experience internally for employees has a certain appeal for organizations.
However, the results of an on-demand approach don’t live up to employers’ expectations: Our latest research on digitalization has found that deploying HR solutions through on-demand access only generates a 4 percent impact on employee performance, at most. This is better than no promotion of digital tools at all, but despite its intuitive appeal, in practice the on-demand approach overwhelms employees, confronting them with too much information and too many choices about how to use HR solutions.
Fortunately, there’s a better way.
In today’s digital organizations, HR departments are increasingly using algorithms to aid in their decision-making, by predicting who is a retention risk, who is ready for a promotion, and whom to hire. For the employees and candidates subjected to these decisions, these are important, even life-changing, events, and so we would would expect the people making them to be closely supervised and held to a set of known performance criteria. Does anyone supervise the algorithms in the same way?
Algorithms don’t monitor themselves. Replacing a portion of your recruiting team with AI doesn’t obviate the need to manage the performance of that AI in the same way you would have managed the performance of the recruiter. To ensure that the decisions of an AI-enhanced HR function are fair, accurate, and right for the business, organizations must establish performance criteria for algorithms and a process to review them periodically.
A recent special report in The Economist illustrates the significant extent to which AI is already changing the way HR works. The report covers eight major companies that are now using algorithms in human resource management, which they either developed internally or bought from a growing field of vendors for use cases including recruiting, internal mobility, retention risk, and pay equity. These practices are increasingly mainstream; 2018 may mark the year of transition between “early adopters” and “early majority” in the life cycle of this technology.
At this point in time, it is essential that leaders ask themselves whether their organizations have management practices in place to supervise the decisions of these algorithms. The Economist concludes their piece with a reminder about transparency, supervision, and bias, noting that companies “will need to ensure that algorithms are being constantly monitored,” particularly when it comes to the prevention of bias.
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: