Jocelyn Cranefield looks at how digital platforms mining and presenting data on your work habits might not be serving up the whole picture
Open your email on Monday morning and you might spot an email from Microsoft breaking down your work for the past four weeks - 55 percent collaboration time, 45 percent of time free to focus, eight days without interrupted quiet hours, etc.
These automated messages are part of an IT system that combines artificial intelligence (AI), analytics, and nudging with the goal of helping us improve our productivity and wellbeing - a kind of work-life fitness coach. But how useful are such systems in practice and what’s involved in taking advice on your work from intelligent IT?
Today’s abundant communication tools and hyper-connectivity give great flexibility in where and how we work, but this comes at a price: there’s no longer a clear boundary between our work and our home life. It’s increasingly up to us to decide when to work and when to stop working.
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Yet it can be hard to disengage when there’s a torrent of messages arriving on your tablet, smart phone, and watch. Should you tackle a few more emails after dinner? What if your manager sends an urgent message at 10pm?
An always-on work culture can affect your wellbeing, productivity, and home life. Daytime work habits compound this. Checking email too frequently interrupts flow, while going to meetings all day leaves no time for focused work. According to a 2020 survey, work-life balance is a challenge in 70 percent of New Zealand organisations while stress is an issue in 69 percent.
Digital platforms such as Microsoft 365 and Google Workspace are seeking to address these problems by mining the data we create across their suites of software (when using email,
Microsoft’s Viva Insights, which sends those Monday messages to your inbox, builds a profile of your work habits based on when you read and send emails, who you communicate with when, how long you take to reply to colleagues, and even how often you send emails during meetings. It then serves you up a dashboard showing your high-level work patterns and you can drill down to see details and recommendations, such as scheduling “focus” time and booking shorter meetings.
But data and suggestions alone are unlikely to change your behaviour, so Viva also draws on “persuasive design”. Look closely and you’ll see this design used in reducing complex behaviour into simpler categories (work time is reduced to two categories: collaboration and focus), gain framing (you’re told how much focus time you kept, not how much you’ve lost), and nudges.
Nudges steer you towards making a beneficial change at minimal cost. Typically, you’ll see a prompt (“book focus time”) linked to a way of making the suggested change easy. With one click, you can have Viva book focus time in your calendar, delay email delivery, mute evening notifications, and send praise to the colleagues who’ve featured large in your work week.
So how do workers react to being profiled and nudged in this way? Our study of 28 users of My Analytics (the precursor of Viva Insights) found mixed results.
Many participants felt the summary of time use didn’t match reality. This reflects the system’s blindness to what we do outside the Microsoft 365 platform, such as having spontaneous in-person meetings and reading reports offline after-hours.
Participants also struggled with the two separate time categories (collaboration time and focus time). They were accustomed to personal, job-specific ways of categorising time, but the system didn’t support personalisation.
Nonetheless, some insights were seen as revelatory. People who become aware of the number of evening hours worked often undertook to make changes. The focus time booker was highly valued by those who used it and managers found it useful to see how much time they had spent with individual staff, so they could adjust this.
Perhaps surprisingly, privacy was not seen as an issue by most participants, who knew their data could not be seen by management (Viva Insights emails only go to you, dashboards are private, and aggregated data is anonymised).
Ironically, however, many participants in our study saw the productivity tool as creating a new time demand that was not worth their while.
In coming years, it’s likely we’ll be working with more AI-based tools designed to help us gain value from our jobs. Data about our use of IT was previously harnessed to improve products and for commercial gain, but with growing scrutiny of big tech companies, it’s in their interests to find ways for this data to benefit us more directly.
In order for us to feel that our time investment is worthwhile, it will be important to develop systems that are able to be configured to suit the user, transparent in how they reach decisions, and able to accept and respond to our feedback, as well as preserve our privacy.
