When we talk about the future of work, I always insist that we talk primarily about work and its content, not about what is peripheral to it. Continuously improving onboarding is good, rethinking the way work is designed and executed is better for both the employee experience and the organization’s performance.
But we can only improve what we measure, and that’s the topic of the day. A topic that we will try to understand through the major trends that shape tomorrow’s work.
By forcing widespread remote work on businesses and employees who were completely unprepared, the pandemic did not really create a new problem but highlighted an existing one.
Indeed, as I said in my last article on the subject, most managers are unable to monitor the work of their employees without seeing them, which is all the more serious since seeing someone busy is not a sign that he is working and moving forward, and allows even less to understand what is doing and how the work is done individually and collectively.
The pandemic has therefore brought attention to a subject without any valid answers.
Today, data is everywhere in our private lives, for better or for worse. Everything we do, the way we behave on a website, our online behaviors, everything is tracked to improve our experience…and also to harass us with messages if we don’t complete the purchase.
And there’s the quantified self. Scales, smart watches are constantly collecting data on us, our activity, our health.
In the end, we are supposed to know ourselves better, to understand how we work. Well, it also allows brands to know us better, which can be a good thing when it’s just to improve an online experience, less if it’s to practice aggressive marketing. But that’s not the point.
But let’s get back to work. I challenge most managers to explain to me how their teams individually and collectively achieve what is expected from them.
There are operating procedures, formalized processes, but they are by definition incomplete, since they are most often used for activities requiring ad hoc processes. There is a framework, but within this framework, employees have room to adapt and no one is able to monitor, explain and improve it.
No one knows employees less well than their employer. No one knows better than them what their daily life is made of and we don’t do much to achieve this except questioning them. It’s a good start but it lacks precision and objectivity.
But if the employee can say that nobody knows or understands him, the manager who would like to get out of visual control and presenteeism can also say that nothing is done to help him while in his personal use he is flooded with data on everything and anything.
A gap that is less and less understandable and that creates expectations.
The evolution of society and the economy
Today, an essential part of our activity is based on “knowledge work”. A rather pompous term which, contrary to popular belief, does not only designate highly qualified employees but rather activities consisting in the manipulation and transformation of information through intangible production flows.
Unlike manufacturing activities, this part of the economy has never been the object of a thorough quantitative understanding, so much so that we can say that the activities that create the most value today do not really know how they create it nor how to improve themselves through a logical and rational approach.
It is the long-standing transformation of our economies in this direction that makes this approach indispensable and leads to the next point.
The transformation of service activities and knowledge work
I promise this is the last time I’ll tell you that as this New York Times article said:
“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. “
The reason for this shortcoming is quite easy to explain. Talking about intangible workflows we don’t see them, so it’s easy to pretend they don’t exist, to say that we can’t measure anything and therefore can’t improve anything. An approach that reaches its limits.
We never really improved the productivity of knowledge workers that much, we just increased the amount of work. Work more to produce more, but never work as much or less to produce more. Working more instead of smarter. This works for a while but there comes a day when not only can’t we ask more from people (burnout etc) but we can’t find people to add (talent shortage) or they cost too much.
Two levers have been identified: collaboration and technology.
Collaboration remains a pipe dream. We do not have enough objective data to understand how to improve the work of an individual, so to do it at the level of a collective is an impossible task. Let’s say that we have mostly put in place cooperation (division of tasks without a common goal, the goal of some being only the realization of a task) than collaboration (collective work towards a common goal).
As for technology, as powerful as it is (and it is today, at least in terms of potential), it is poorly mastered collectively and has been deployed without transforming the way people work (read above), imagining that it would be self-supporting, whereas it only delivers its full potential to those who want to change the way they work. This is a good justification of Solow’s paradox: “We see computers everywhere, except in the productivity statistics”.
In short, the way in which these activities are carried out must undergo a process of questioning and improvement so that the activities that drive most of the growth and generate most of the jobs in countries like ours are not excluded from operational excellence.
What has changed is that we can measure all or part of these activities and understand qualitatively and quantitatively what is happening at the individual and collective level through feedback mechanisms and secondary data.
I advise you to read the excellent Qualtrics use case mentioned in this post:
“It does this by automatically finding the intersection points between employee experience and operations, enabling organizations to explore their overall impact on business KPIs much more effectively.”
In short, businesses are no longer blind and can therefore undertake a process of understanding and transforming work based on data rather than on the intuition of some guru.
Businesses now have the data to finally understand and improve knowledge work activities, an essential step before entering into a logic of continuous improvement. Indeed, it is inconceivable that this part of the economy remains the only one not understanding or improving in a structured way the way it operates!
Indeed it would be nice, when we talk about the future of work, to finally talk about what people do and how they do it, not only about what happens around when they don’t produce.
Then will follow actions of change/transformation/improvement whose success will depend precisely on the quality of the data collected and the interpretation that will be made. Sometimes it will be necessary to act only on the edge, other times more deeply.
Finally, it will be necessary to make these subjects exist and move them forward while the weight of the daily business monopolizes all the staff’s attention and postpones any desire for improvement to an indefinite date. Perhaps by taking inspiration from agile methods to move forward incrementally but surely instead of waiting to have the time to make a big bang?
The last point is that this data should be used to measure processes, procedures and the way work is done, not to measure people. Making such a mistake would indeed have a catastrophic impact in terms of trust, but a very popular digital workplace tool almost did it.