analyzing automation

Several years ago I recommended one small change that could have a major impact would be to look at everyone’s work from the perspective of standardized versus customized (non-standardized) work. Every person in the company, with the help of some data and peer feedback, should be able to determine what percentage of their time is spent on standardized work.

If the percentage is over a certain threshold —perhaps 50% — then it becomes a management task to change that person’s job and add more customized work. The company can be constantly looking at ways to automate any standardized work in order to stay ahead of technology, the market, and the competition. While automation is pretty well inevitable, it does not have to decimate a workforce.

Looking at the overall company balance between standardized and customized work should be an indicator of its potential to succeed. By visualizing the Labour/Talent split, people in the company can take action and make plans before the inevitable shift. This also means that jobs and roles have to become more flexible and open to change.

Building ways to constantly change roles — perpetual beta — obsolesces the concept of the standardized job, which has no place in a creative economy. This one small change could have a major impact on any organization. It just requires a slightly new way of looking at work, collecting good data, engaging all workers in the process, and being transparent about it. Most of all, this change requires companies and managers who really care about people. After all, aren’t people every organization’s most important asset?

Here is how Unilever in the Nordic countries approaches automation.

Our mantra is: If we can develop an algorithm that incorporates a process flow, the process should be automated. Therefore, we continuously search for highly complex processes with many repetitions. The algorithms are changing the classical role of analytics: business people are no longer presented with data; they get pre-processed data and generated insights. It may not sound like much, but this unlocks capacity because we spend less time analysing data. —Rocking Robots 2022-07-12