This past winter, research firm Forrester released a report that linked overall U.S. economic productivity, which has been stagnant since 2011, with individual workplace productivity and argued that “insights-driven businesses” will be the companies that ultimately drive growth. This means companies that have data showing how their employees work are the ones that will thrive. And this past Wednesday, Microsoft made available to its enterprise Office 365 customers a new tool called Workplace Analytics. This tool, which relies on artificial intelligence for data analysis of worker behavior, joins WorkSnaps and BetterWorks as performance-monitoring software designed to log people’s computer activities to make sure every moment on the clock is a profitable one for their employers.
So what? Linking personal productivity to economic productivity is a stretch, as exactly how one defines and measures productivity is up for debate. As a fiscal metric, productivity is often defined as a higher economic output per hour. That definition is being challenged by scholars who argue that the definition of “productivity” is based on a manufacturing-dominated economy, not an information- or service-based one. Producing X number of widgets per hour does not map directly to writing Y number of apps per year.
However, when management talks about productivity in a white-collar context, they’re usually talking about it in the “How do I get my workers to spend less time on Reddit and more time churning out finished projects?” context of personal productivity. This is the kind of productivity that presumes one can achieve a state of distraction-free industry, whipping through to-do lists without the encumbrance of busywork and optimizing every moment for greater workplace benefit. Software has been selling the promise of that kind of productivity since VisiCalc laid out the electronic spreadsheet that streamlined accounting. Analytical tools that promise to improve how you use software are a logical next step.
Who cares? White collar workers are sure going to start. One of the perks of white-collar work used to be a fair degree of autonomy on the job. While a lot of customer-facing, manufacturing or service jobs incorporate continuous monitoring of employee performance against a set metric like “number of pieces processed per hour” or “number of sales voided per day,” many white-collar jobs don’t have the same degree of direct supervision and measurement against hard numbers.
Having artificial intelligence monitoring so many aspects of your working day, from your scheduled meetings to your email habits, then nudge you to change your job performance, is a significant change from how a lot of people work now. And it illustrates one of the fundamental limitations of artificial intelligence: An inability to consider why an outlying data point might not need to be corrected.
Not everyone works the same way—ask five different people how they handle email and you’ll get five different strategies. Some folks can’t check more than twice a day, others are the rare and lovely effective multitaskers. But what if a productivity tool is demanding that those five different people start producing the same metrics with regards to email engagement frequency? The result is not always going to be “five people improved their email habits.” The result is going to be “five people changed their email habits.” Change and improvement are not synonymous.
Artificial intelligence based on machine learning is only going to be as good as the data that’s being analyzed. And that data is only as good as the assumptions made by the people who decided what defined data and how to sort it for learning. So long as there is a built-in assumption that “productivity” is a discrete, measurable metric attainable only through very specific practices, the data identified and collected by artificial intelligence is going to reward very specific ways of getting things done.
Then again, so long as there is a built-in assumption that “productivity” is the greatest good we can strive for as workers, we’re closing off a lot of possibilities about our work and what we make of it at all.
Want more? There’s a whole archive of So What, Who Cares? newsletters at tinyletter.com/lschmeiser. In addition to the news analysis, there are also fun pop culture recommendations.