The rise of digital bosses:
They can hire you — and fire you
The use of artificial intelligence and machine learning (AI/ML) has exploded just as managers and executives are adapting to hybrid work environments and struggling to oversee remote employees.
A recent report from research firm IDC predicts that by 2024, 80% of Global 2000 companies will use AI/ML-enabled "Digital Managers" to hire, fire, and train workers in jobs measured by continuous improvement — but only one in five companies will get any real value from the move without human engagement.
The ongoing worldwide COVID-19 pandemic has forced many companies to adopt new work models, ranging from fully remote to "hybrid" approaches where individuals and teams work at or across multiple digital and physical locations.
This shift to more distributed workforces has required companies to devise new methods for managing, leading, and organizing enterprises, according to IDC.
In fact, about 41% of companies see the ability to manage a remote and hybrid workforce as a critical skill to hire or develop in-house, according to IDC's April 2021 Future Enterprise Resiliency and Spending Survey.
Today, digital-management software based on AI/ML is used to scan resumes and cull applicants, determine daily employee performance, recommend additional training, and determine when and how many employees are needed for a job – especially for shift-type work.
Amy Loomis, a research director for IDC's worldwide Future of Work market research service, said that while the use of AI/ML to hire and fire workers might seem remarkable, “it’s rather widely used in [human resources] circles today to greater or lesser degree.
“Algorithms are often used to stack-rank employees offering recommendations on who would be best fit to hire or targeted to fire,” Loomis said.
Stack-ranking, also known as forced ranking or forced distribution, uses a statistics-based approach to rate employees on job performance in comparison to other team members.
Stack-ranking software can be used to suggest that employees take additional training, push managers to perform employee remediation, or in some cases prompt the firing of a percentage of employees who fall below performance thresholds. A company, for instance, could choose to fire all employees who fall into the bottom 10% of performers.
Case example: Amazon
For example, widespread media reports during the past year claimed Amazon uses software algorithms or “bots” to hire and rate employees, “firing millions of people with little or no human oversight.”
Overall, a large percentage of the Amazon workers are terminated for job abandonment. Only a small percentage are terminated for performance issues, according to Kelly Nantel, an Amazon spokesperson.
The company, which employs more than 1.4 million workers, denied its algorithms are used solely to fire workers. The company's workforce management technology supports and enhances the experience of job candidates and employees. It’s not meant to replace managers, but to aid their decision-making with data and information, according to Nantel.
"There’s a distinct difference between a personnel management system flagging someone who has abandoned their jobs — and as a result they’re automatically terminated — versus our performance systems that help give feedback to our managers on where and how our employees are performing and stacking up against one another and giving recommendations and feedback to those who may be struggling," Nantel said.
"Contextually, it’s easy to say thousands or hundreds of thousands are fired by robots. Well, in some cases that’s true in job abandonment cases, but they’re not fired for performance issues ever," Nantel continued. "They’re not coached, fire,d or disciplined through any technology."
Shannon Kalvar, research manager for IDC's IT Service Management and Client Virtualization Program, said that while companies may not rely entirely on software bots to fire employees, recommendations based on AI/ML weigh heavily in decision making.
“We are human beings who are overworked and over stressed. What is the likelihood you’re going to disagree with a suggestion when it comes through — especially if you’re remote managing somebody?” Kalvar said.
Digital management software was already in use before the pandemic, when it mainly helped manage trucking fleets, retail workers, service workers, and other “task oriented” jobs. For example, the gig economy enabled flexible hours for delivery services, which enabled same-day delivery for retail products and groceries. Delivery trucks were no longer pre-packed days in advance.
In 2015, for example, Amazon started its gig-style Flex delivery service using contract drivers instead of full-time employees. Contract workers’ performance is closely monitored by software algorithms that track their routes and delivery times.
“A frighteningly large number of organizations have digital managers,” Kalvar said. “We’ve seen a huge uptake in interest in that and it’s already starting to roll out for office workers in addition to everyone else. Today, it’s really a problem in task-oriented jobs, but you have to realize we’re all moving into task-oriented jobs.
“There’s plenty of software that detects problems with process, which is another way of saying, 'Where are people screwing up and do they need to be remediated?'” Kalvar said.
The issue has become a hot-button one in Europe, where the European Commission is eyeing rules that could force companies to be more transparent about their use of algorithmic management.
One major flaw with algorithmic employee management is the disparate nature of applications. Some tools are embedded in ERP system software, others are standalone applications and services. In a large enterprise, there can be many different personnel management and training applications, and many of them do not talk with each other.
That's a problem at Amazon, which uses various types of software and algorithms. Some track employee time and attendance, others oversee worker performance, while still others keep a record of employee disability leave.
A manual patch the company deployed to enable communications between its time and attendance monitoring algorithm and its employee-leave system failed to integrate the two systems.
"In some cases there have been issues where an individual might have been out on leave and two systems were not talking to each other and the system generated a form email or letter being sent out to an employee saying they’d abandoned their job when, in fact, they were out on leave," Nantel said. "We’re in the process right now of fully implementing a patch that connects those two systems together.
Source: Computer World
