Services, the knowledge worker and the 21st century management challenge
long time no see 😬
Today I’ll skip the intro, but I’ll start with a personal story to smooth our way into today’s topic — the knowledge worker and 21st-century management challenge.
We met a long time ago. I was around 18, and he was three years older (still is!). At that stage, it was enough for me to look at him as a reference, and that continues till this day.
We were both involved in a Young Scientists Association, and he was one of the leaders of a multidisciplinary research group with people from Biology, Physics, and Engineering. They had a broad research scope, from biology field-work with deers to ecosystem modeling. With that group, I was introduced to the Complexity Theory that still fascinates me to this day. It was the heydays of Standa Fé Institute, cellular automata, and Artificial Life. Fascinating!
I still remember a university project I did with a friend, where he served as our advisor. We developed a cellular automata simulation of predator-prey dynamics. It was fascinating that the system showed emergent dynamics comparable to the ones described by the Lotka–Volterra equations — the classical nonlinear differential equations that describe the dynamics of predator-prey biological systems.
After college, we took different paths, and we lost contact. He pursued a research career that he reconcile with being an entrepreneur in sustainable agriculture. I moved into a business career, first in the corporate world and then in startups.
Three years ago, we got together for lunch. We engaged in those free flow conversations with no particular agenda, just talking about our projects, making connections, and being curious about the other’s world. Since the early days and to this day, he always played in the interception between disciplines — Physics, Ecology, Energy, Economy, and AI, to name a few — something that I find particularly exciting.
From time to time, we still meet for lunch and let the conversation flow. In one of those conversations, he told me about a study he conducted on long time-series relationships between Energy and GDP, exploring the intersection of thermodynamics and economy. One of the study's striking conclusions is that energy to GDP efficiency grows till 1980 but then stagnates. Actually, the last big jump in energy productivity was the adoption of the electric engine! Moreover, all the frenzy around the adoption of computers and information technology didn’t have an impact in terms of energy to GDP efficiency! (for a more precise explanation, you can deep dive into the paper, or for a less hard-core reading, look at chapter 2 of this report).
In a follow-up study, they desegregated the energy and GDP by economic sector — agriculture, industry, and services. What they found was even more insightful and was the result of 3 trends:
Agriculture and Industry have seen massive efficiency gains in the last 100 years.
But their GDP contribution, as we know, fall sharply and gave space to a service-based economy.
Services, on the other hand, have shown mostly stagnant energy to GDP efficiency.
These 3 trends combined mean that even if Agriculture and Industry became much more efficient because their contribution is much lower now, total energy-to-GDP efficiency does not grow significantly.
This means, Service sector's lack of efficiency is to blame for a stagnant economy.
Services the next frontier
This made me wonder: if we want to see a growing service-based economy, we need to find ways for services to be more efficient.
I see two possibilities here:
A step-change in technology that will drive more process automation
A significant improvement in how we organize knowledge workers that are the base of the service economy.
It was the introduction of technology across agriculture and industry that replaced humans led to massive improvements in their efficiency. But this technology relied on precise metrics and control models. The physical and tangible world of agriculture and industry provides much more precise control variables that could be automated. Replacing a human with a machine in these sectors is much straight forward than in the service sector.
Service automation requires a new set of technologies that can replace human judgment in activities with many nuanced variables. Services business require more interaction with humans, judgment, and empathy.
Today, new technology based on Artificial Intelligence and Machine Learning enables more automation in this sector — RPA technology is a prime example of how business processes are being automated and replacing humans in the back-office. Customer service bots are providing a faster way to support customers without human intervention.
But beyond the adoption of this new technology, I also see a lot of room to improve how we organize and produce work within a service company.
Did you ever wonder if people on agriculture, manufactory and construction also complain about of having too many meetings, too many emails and lack of focus time?
The knowledge worker
If service companies leverage the knowledge worker's value, their output optimization relies on getting the most out of the worker's potential.
Peter Drucker puts it clear that this is the productivity challenge for the 21st century:
“The most important, and indeed the truly unique, contribution of management in the 20th century was the fifty-fold increase in the productivity of the manual worker in manufacturing. The most important contribution management needs to make in the 21st century is similarly to increase the productivity of knowledge work and knowledge workers. The most valuable assets of a 20th-century company was its production equipment. The most valuable asset of a 21st-century institution (whether business or nonbusiness) will be its knowledge workers and their productivity.”
Peter Drucker, “Knowledge-Worker Productivity: The Biggest Challenge” — California Review of Management, 41-2, 1999
Service companies have an organization deficit because they typically require more people, and the outputs are much fuzzier and much less “dramatic”.
Let’s take the industry sector as a base. On the one hand, the outputs are quite tangible — a car, a 200ml bottle of shampoo, or a cereal pack. You can’t argue with this. Measuring, tracking, and optimizing is linear and straightforward. While in services, the output is fuzzier — how do you define customer satisfaction and service quality?
On the other hand, in manufacturing, the cost of errors can be dramatic. If a plane is ill-constructed, people will die. If a product is sold with defects, people will get hurt, and companies will face massive recall and reputation costs.
When serious costs are at play and the outputs are tangible, companies will devote time and resources to build robust processes that ensure quality and become efficient. It’s no surprise that major management innovations were originated within the industry sector: scientific management, total quality management, lean, six-sigma,... They have all the incentives and conditions to become more efficient and ensure quality outputs. It’s then no surprise that they achieved a 50x increase in productivity.
Conversely, service companies struggle with measuring output, and when things go wrong, it usually affects a few customers, can be easily contained or reversed.
How many service companies can actually provide a six-sigma quality standard (99.99966% quality)?
What can we do about it?
Should we run service companies as a factory, or are there other models we could get inspiration from?
I’ll explore a hypothesis in the next issue. Stay tuned.
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Thanks for reading,