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Will Algorithms Ultimately Write Coaching Scripts for Managers?

There's a lot of talk about AI, robots and algorithms ultimately replacing people in some, if not all, of the jobs we know and love.  While I believe that's going to happen (it already has and will continue), there's a couple of myths we can dispel about the displacement of people out of jobs.  

Those myths include the following:

  1. The revolution will happen fast and we'll all be out of jobs.  I've got good news and bad news.  The bad news first - you're all going to be impacted by the move to automation and AI.  The good news? Mr robot This stuff happens in waves, which means that generations have time to retrain and refocus where the jobs are moving to - for people.
  2. The art of managing people will never be lost to automation, because machines and AI can't manage emotions.  Actually, the art of managing people will be stripped away gradually in waves, just like everything else.  Sorry Mr./Mrs. Manager - you're not special in this regard.

Need proof related to both of those myths and what reality is? My friend Steve Boese just shared the following snippet from the Financial Times, which is interesting and shows the current wave of management being stripped away and - I think - offers up a snapshot of what will be stripped away next:

The next frontier for algorithmic management is the traditional service sector, tackling retailers and restaurants.

Percolata is one of the Silicon Valley companies trying to make this happen. The technology business has about 40 retail chains as clients, including Uniqlo and 7-Eleven. It installs sensors in shops that measure the volume and type of customers flowing in and out, combines that with data on the amount of sales per employee, and calculates what it describes as the “true productivity” of a shop worker: a measure it calls “shopper yield”, or sales divided by traffic.

Percolata provides management with a list of employees ranked from lowest to highest by shopper yield. Its algorithm builds profiles on each employee — when do they perform well? When do they perform badly? It learns whether some people do better when paired with certain colleagues, and worse when paired with others. It uses weather, online traffic and other signals to forecast customer footfall in advance. Then it creates a schedule with the optimal mix of workers to maximise sales for every 15-minute slot of the day. Managers press a button and the schedule publishes to employees’ personal smartphones. People with the highest shopper yields are usually given more hours. Some store managers print out the leaderboard and post it in the break room. “It creates this competitive spirit — if I want more hours, I need to step it up a bit,” explains Greg Tanaka, Percolata’s 42-year-old founder.

The company runs “twin study” tests where it takes two very similar stores and only implements the system in one of them. The data so far suggest the algorithm can boost sales by 10-30 per cent, Tanaka says. “What’s ironic is we’re not automating the sales associates’ jobs per se, but we’re automating the manager’s job, and [our algorithm] can actually do it better than them.”

What this snippet shows is that the more data points available, the more current algorithms can replace the need for managers to evaluate performance.

Here's what could be next - it's hard right now to imagine a machine/algorithm providing coaching to the middle range or low performing employee.  After all they can't connect emotionally, right?

Of course, what that algorithm could do is provide a coaching script for the manager to follow based on what the data says that's impossible to mess up.

In the past, I've offered up this gold standard for a manager to use when writing performance review items . If an employee is "meeting" expectations in any area of performance, you use this basic formula:

    [<Statement +2> + <1 Stretch> = Gold] The Only Formula You Need

    That’s it. This formula is all you need when you’re writing any type of written performance- review item. Let’s break down what each part of this formula means:

  • Starting with Statement +2: Once you've arrived at a rating, you're going to make a statement that describes why you're giving the rating in question.  Then, you're going to back up the statement/rating with two specific performance/behavioral examples that you can cite from the review period.  The specific examples you give should be representative of the trend you see, and should help you illustrate why someone is at the rating you’re giving and not the next highest point on the rating scale.

After reading what's happening with sales performance in places like 7-Eleven, it's clear that AI and algorithms are cutting into the role of management at companies with access to data.  

It's not a big jump to think that those same algorithms and AI could create performance statements via the formula I provided above.  At some point, either humans aren't in the jobs or the tech advances to the place where AI can deliver the coaching via that formula to the human talent.  After all, you avoided it, right?

That's how it goes, right?  One day you love technology because it's making your job easier.  The next day the tech advances and suddenly, you're not needed.  

Brave new world...

 

 

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