Prior to COVID-19, leadership at organisations in Asia have shown hesitation about allowing remote working for its staff. As evidenced by Hang Seng Bank management trainees that flour work-from-home rules to go hiking, there is valid concern by management as to how to ensure productivity of employees approved to work remotely.
But nearly a year on into the work from home movement, organisations have discovered that employees are willing to put serious hours into working from home perhaps even as much as they would if they were in the office. In its third year, the Buffer Survey noted that 98% of surveyed respondents want to continue working remotely (at least for some time) for the rest of their careers.
For many organisations in Asia the quest to track employee productivity in remote work conditions continues. It is as if having visibility of your employees guarantees productivity or quality work.
Gartner predicts that by 2023, more than 10% of workers will seek to trick artificial intelligence (AI) systems used to measure employee behaviour and productivity. Such systems have seen a significant uptick in use in the wake of the COVID-19 pandemic.
“Many businesses are making a permanent shift to full- or part-time remote work, which can be both costly and require cultural changes,” said Whit Andrews, distinguished research vice president at Gartner. “For management cultures that are accustomed to relying on direct observation of employee behaviour, remote work strengthens the mandate to digitally monitor worker activity, in some cases via AI.
“Just as we’ve seen with every technology aimed at restricting its users, workers will quickly discover the gaps in AI-based surveillance strategies. They may do so for a variety of reasons, such as in the interest of lower workloads, better pay or simply spite. Some may even see tricking AI-based monitoring tools as more of a game to be won than disrespecting a metric that management has a right to know.”
Tools for tracking productivity
Organisations are using AI-enabled systems to analyse worker behaviour in the same way that AI is used to understand shoppers, customers and members of the public. These tools provide basic activity logging with alerts, or in more sophisticated versions, can attempt to detect positive actions or misbehaviour through multivariable analysis.
Many employers use productivity monitoring systems despite a high percentage of workers finding such tools unappealing. Even prior to the pandemic, Gartner research showed that workers feared new technologies used to track and monitor work habits. As these tools become more prevalent, Gartner predicts that organisations will increasingly face workers who seek to evade and overwhelm them.
Workers may seek out gaps where metrics do not capture activity, accountability is unclear, or the AI can be fooled by generating false or confusing data. Such activities have already been observed in digital-first organisations; for example, ride-share drivers sometimes work for two different services simultaneously as a way of maximizing personal earnings.
Andrews suggested that IT leaders who are considering deploying AI-enabled productivity monitoring tools should take a close look at the data sources, user experience design and the initial use case intended for these tools before investing. “Determine whether the purpose and scope of data collection support employees doing their best work. For those that do decide to invest, ensure that the technology is being implemented ethically by testing it against a key set of human-centric design principles,” he concluded.