The increasing use of productivity monitoring tools has raised a series of questions concerning the balance between workplace (or remote work) efficiency and employee privacy and trust.
When applied carefully, these tools can be used to improve management practices: By understanding employee activity better, organizations can see where systemic factors are making the work more challenging for employees.
Following this thinking, they might learn that 90% of the times that employees start to use a given tool to accomplish a task, that they immediately move on to a different tool to get some of the information they need to complete the task.
“This could highlight an opportunity for technology changes to make moving to the second system unnecessary and give them a reliable basis for quantifying the benefits of making the changes,” explained David K. Johnson, Forrester principal analyst, employee experience.
He said organizations must effectively communicate productivity monitoring programs to employees to foster trust transparency and fairness.
“Companies must specify exactly which activities are included and which are not,” Johnson said. “Second, it’s essential to outline how the information collected is being analyzed and how it will be used in decision-making.”
He noted employees need to understand the purpose behind the data collection and how it impacts them.
Lastly, Johnson stressed the importance of addressing disputes fairly.
“Organizations must provide a workable resolution process for any disagreements about the data or its use in employee-related decisions,” he said. “This fairness is key to building trust and reducing concerns about over-surveillance.”
Ethical concerns surrounding the use of employee productivity tools include privacy violations, potential bias in algorithms, and a culture of distrust.
To address these issues, organizations must prioritize data privacy by implementing strong security measures, anonymizing data, and restricting access to sensitive information.
They can ensure fairness and transparency by utilizing unbiased algorithms, conducting regular bias audits, and granting employees access to their own data.
Additionally, organizations should foster a culture of trust through open communication and emphasize that data is used for improvement, not punishment.
Eric Schwake, director of cybersecurity strategy at Salt Security, said productivity monitoring data should enhance, rather than replace, effective management.
“Use this data to identify bottlenecks, streamline workflows, and offer targeted support,” he said.
Encourage open communication between managers and employees to address concerns and promote collaboration.
He also recommended focusing on improving overall team performance and organizational efficiency rather than concentrating solely on individual metrics.
John Bambenek, president at Bambenek Consulting, said productivity monitoring is based on the premise that you can manage knowledge workers the same way you manage an assembly line, by X output per hour.
“You can’t, and in environments where metrics are the mission, you always end up catering to the employees that work to manage the metrics instead of maximizing value, excellence, and profitability,” he said.
He added if your front-line managers can’t assess whether your employees are actually creating value, then your problem is your front-line managers.
“When productivity monitoring tools, where used, to try to prevent cheating in higher education, the results were poor, the false positives were high, and everyone was dissatisfied,” he said.
Schwake said when implementing productivity monitoring tools, organizations should prioritize transparency and trust, and clearly communicate the program's goals, the data collected, and its intended use.
“It is essential to clearly communicate the purpose, scope, and data collection practices to employees,” he said.
He said it is important to obtain their consent and provide options for opting out.
“Focus on analyzing aggregate data and trends to enhance workflows and support employee development rather than using the data for punitive measures,” Schwake said.
Another important part of the program is to offer opportunities for employees to ask questions and provide feedback.
“Emphasize that the focus is on improving workflows and supporting employee development rather than monitoring individuals,” he said. “Regularly review and update policies based on input and the latest best practices.”
AI can improve productivity monitoring by automating data analysis, identifying patterns, and offering personalized insights, and can enhance privacy by anonymizing or aggregating data.
“However, using AI tools ethically and transparently and establishing clear guidelines for data usage and privacy protection is essential,” Schwake cautioned.
Johnson said AI could potentially derive interesting new insights from a combination of employee activity data and relevant context.
For example, imagine that a company experiences a significant drop in employee activity in the weeks following a major reorganization or even just following a handful of manger changes.
“Tracking these patterns with their context can teach leaders a lot about managing change more effectively,” he said.
He added AI could also potentially help by deriving insights from large volumes of conversational data between employees.
For example, imagine that a change in employee activity coincides with lots of communication between employees that reveals anxiety or uncertainty.
“This could give leaders much more insight into where employees are struggling and need help or clarity,” Johnson said.
