Employees must follow the processes. Are you sure?

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Processes are reassuring for the business because they allow it to rely on the system to avoid individual deviations. Indeed, since humans are a priori fallible, locking them into a strict operating mode allows them to be certain that everything will be done mechanically, in the manner planned, and therefore that the expected result will be obtained.

In a business, the respect of processes by employees is a guarantee of performance and quality. But are we so sure?

At the beginning we find again and again the Taylorian heritage. At the beginning of the industrial era, Taylor revolutionized production methods in the way we know. In an industrial context where it was necessary to manufacture the same thing over and over again, without the slightest difference between two parts produced, more and more quickly and, let’s not forget, with a poorly qualified workforce, it was a question of leaving as little room as possible for reflection, for personal judgment.

This is how work was broken down into a number of micro-tasks that were easy to learn and perform by an individual so that he could execute them mechanically, without question, and without risk of quality deviation. This was widely caricatured by Charlie Chaplin in modern times.

In two words it was a question of “replicating perfection ad infinitum”.

A machine job you would say? Yes, it is. But at the time there were no machines, or rather, they were only in their infancy. So, if we could not entrust the work to machines, we designed the work for them but, for lack of anything better and while waiting for something better, we entrusted it to humans.

Then the machines came and we know what happened but that’s another story.

Let’s take a leap in history and look at the take-off of the tertiary sector and what is now called the service economy and knowledge workers, or even the experience economy, which is just an extension of it.

How did this economy organize itself and, above all, its production? By copying the existing, that is to say the industrial world inherited from Taylor. And too bad if the industry had become largely mechanized since then, because of the lack of machines, we would use humans to do a job that we had designed for machines.

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Waiting for the machines to arrive? We could talk at length about the fact that, yes, if we continue to give humans jobs that are designed for machines, the risk of seeing, one day, robots and artificial intelligences take their place is real, but that’s not our topic today.

However, what has not changed since then is the way in which work and its content are conceived in these sectors.

Surprising? Yes, insofar as the industrial world that we are trying to copy has not stopped questioning its efficiency and practices since Taylor and still does today.

As this New York Times article noted:

“Peter Drucker noted that during the twentieth century, the productivity of manual workers in the manufacturing sector increased by a factor of fifty as we got smarter about the best way to build products. He argued that the knowledge sector, by contrast, had hardly begun a similar process of self-examination and improvement, existing at the end of the twentieth century where manufacturing had been a hundred years earlier.“

If productivity and quality have increased in the manufacturing sector, it is not only because of machines, but because we have reinvented the way we work, the way we conceive the production chain, and we have rethought the role of people to make them do what machines could not do.

In the knowledge economy, we were content to add machines (computers….), thinking that this would allow us to work better without rethinking the way we work. Thus, in 1987, Robert Solow, who received the Nobel Prize in Economics the same year, stated what is now called Solow’s paradox:

“We see computers everywhere, except in productivity statistics.

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Yves Mulkers

Yves Mulkers is the founder of 7wData and a widely followed voice in the data and AI community. He curates the 7wData and AI Beat newsletters, reaching hundreds of thousands of data and AI professionals, and writes on data strategy, analytics, AI, and the evolving data ecosystem.