The friendly surface-level rationale behind any mass data collection via surveillance is improved efficiency through metrics. With the right amount of the data, and the right analysts working through it, you can optimize pretty much any process. From a business perspective, this could potentially present new ways to work smarter, instead of working harder — increasing profits and productivity through better decision-making, which ultimately makes everyone happier.
In that context, it makes sense why a chain restaurant like Outback Steakhouse might be interested in implementing its own mini surveillance state. So far it’s only limited to a single franchise in Portland, Oregon which is operated by Evergreen Restaurant Group. But Evergreen also owns some 40-other Outback Steakhouses throughout the country, which means this small pilot program could seen be expanded, if the suits think the metrics work out in their favor.
According to Presto CEO Rajat Suri, Presto Vision takes advantage of preexisting surveillance cameras that many restaurants already have installed. The system uses machine learning to analyze footage of restaurant staff at work and interacting with guests. It aims to track metrics like how often a server tends to their tables or how long it takes for food to come out. At the end of a shift, managers receive an email of the compiled statistics, which they can then use to identify problems and infer whether servers, hostesses, and kitchen staff are adequately doing their jobs.
“It’s not that different from a Fitbit or something like that,” says Suri. “It’s basically the same, we would just present the metrics to the managers after the shift.”
Again: from a corporate board room standpoint, this makes sense.
In practice, however, the pressure from this kind of surveillance tends to have a massively negative impact on the workers. People act differently when they know they’re being watched, and when they know they’re being actively judged on that. It might discourage them from acting outside of their routine, even when it might potentially benefit the customers — for example, using jokes to establish a personal connection with the customers. And if the customers are unsatisfied, for any reason — even if it’s unrelated to the server’s performance — then that could reflect poorly in the metrics. What could have just been one bad tip now goes directly to your boss, who might decide to cut back on your shifts because of that one bad tipper — who, for all they know, may have been an asshole and a lousy tipper anyway. Now, that server is in the hole for more than a single lousy tip; their income is down overall, and their stress is up as a result, increasing the chance that they might actually screw up on the job, thus providing the justification that the employer needs to cut their hours back even more, which is exactly what exacerbated the problem in the first place.
Don’t get me wrong: a reasonable amount of data metrics could help a restaurant to optimize for improved efficiency. That’s why sometimes small, busy urban restaurants can be faster at seating and serving than a physically larger space in a more low-key area. Different restaurants in different places have a different gauge for what constitutes as "busy," or how many tables they expect or hope to flip in any given night. There are valuable things that a restauranteur can learn and use. But once you start treating human employees like a canning machine on a production line, you’re going to start losing out on the humanizing part of eating out. And that ultimately leaves you with a cold dining experience topped with 2,000 calories of fried battered onion.
No one wants that.
At an Outback Steakhouse Franchise, Surveillance Blooms [Lousie Matsakis / Wired]
Image via Paul Young / Flickr
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