Estimated reading time: 8 minutes

This essay was v1, published on October 27^th, 2021.
Following great additional feedback, a second version has been published on December 1^st, 2021 under the title “Fewer Managers, More Leaders: a Template for the Future of Work“.

I have been thinking about how we measure value creation in the context of managing and leading teams for over two decades now, and I want to explain why Autonomy is the most critical characteristic of successful teams.

We use three primary metrics to measure the value of work:

1. Input, or time spent. It doesn’t compound and creates the worst possible incentives.
2. Output, or production volume. Opening to improvement but potentially tracking vanity metrics.
3. Outcomes, or value created. Leading to compounding learnings, thus accelerating value creation.

These represent the past, the present, and the future of performance evaluation. Some teams already experience this future today, while others are stuck in the present, or worse, in the past.

Input is the worst metric.

Many companies and administrations still pay their staff by the hour, and countless liberal professionals use the same metric to bill their clients.

Input, or “time spent working”, has been the reference metric for the longest time. For most of our history, it made sense. Labour was manual, and while productivity could vary across individuals, production remained proportional to time spent working.

The industrial revolution only confirmed time as the determining factor to evaluate performance and compensation. Machines took a growing place in the production process. The assembly line and specialised tooling allowed for the hiring of relatively unskilled workers by breaking work down into simple basic steps.

Hourly wages have been so pervasive that we find them everywhere, including across service industries where not only are they misguided but are counterproductive.

It is confounding to think that in most countries today, lawyers, doctors, or accountants, to just name a few, bill their clients by the hour. But, unfortunately, doing so creates a conflict of interest between their need to bill as many hours as possible and their clients’ wish for the fastest resolution possible. As a consequence, we try to translate the value of experience into ridiculously high hourly rates.

Input is a race to the bottom. Employees paid by the hour progressively converge towards the lowest level of productivity that doesn’t get them fired.

It is also terrible for onboarding new generations of employees. When they base wages on time, employers ask for the proverbial “X years of experience” to guarantee a minimum level of effectiveness instead of focusing on the ability to learn. Experience requirements get in the way of new generations learning a job.

So, why do we still see input based performance evaluation, a.k.a. Hourly wages, everywhere? I believe that is because bad managers love it. It’s the simplest to measure. At what time did people get in, when did they leave? These are elementary questions to answer, not requiring any understanding of the work in progress.

Input is a first-order type of metric. Therefore, it doesn’t generate any additional value.

If you think about it from an economic perspective, trading one’s time for money is akin to using your primary capital to live off it. As you go forward, you have less and less of it until it eventually runs out.

You need to put the same time into work every day to earn your salary.

Employees barely benefit from the experience, and incentives to learn are low.

Output is a vanity metric.

Enters output based value measure. We do not care about time spent working but only about the number of things done, items produced, clients served.

It does represent a step forward by providing an incentive to improve. For example, a person whose compensation is based on output can learn to work faster. Then they can choose between making more money or working shorter hours.

Measuring output also lifts the onboarding barrier: junior employees have to invest more time in their work. But, at the same time, they learn their craft and progressively reduce that investment over time as they progress.

The problem with measuring value by counting products is that it leads, in many cases, to focus on the wrong metrics.

Producing a lot of something, whatever that is, doesn’t automatically create value. Products don’t have intrinsic value, and they are only as valuable as the problems they solve for people, leading to their willingness to pay for them.

There are times when not doing anything is what will create the most value. Product teams repeatedly ruin great products because they need to keep adding new features to show productivity.

I have been there and done that; I will be first to admit it. Have you ever added a feature to a product just because you could?

More often than not, measuring output results in almost comical excess. I remember witnessing a very cringey moment at an ICANN conference a few years ago. A person presenting the results of their working group said: “We are very proud of our work. Our report is more than 100 pages long and has over 200 footnotes.”

Managers who have realised that counting heads in a room is a poor way to assess performance but still lack understanding of the teams they manage generally love output based evaluation.

You don’t need to believe me. Here it is demonstrated by the worst manager in the history of management 😉

Still, output is better than input. It is a second-order type of metric. It does generate extra value over time as by learning to work faster, a person can improve their life.

Back to the parallel with economics, it is like living off the interests generated by your primary capital. Still not ideal as inflation will eventually get you, but it already gives you more runway.

Outcomes, a.k.a. Impact

Here we try measuring the value created by our work.

Tracking outcomes means that a team’s measure of success is not the amount of “stuff” produced but the impact they have by providing their users with solutions to problems they need to solve.

It is harder to measure. However, when focusing on outcomes, it appears that “what” we work on is more important than how much or how fast we work.

It is also much more valuable to track because focusing on value created incentivises fast iteration. We can never be 100% sure of being right. We know we’ll be wrong regularly.

Acknowledging this fact leads to reducing the size of every single investment to minimise losses and maximise learnings. In other words, the fastest a team will ship a succession of small projects, the more successful it will become.

It’s pretty easy to imagine that a team shipping an improvement every two weeks for a year will learn a lot more and be right more often than a team shipping two large projects lasting six months each during the same time.

Resistance against faster iteration generally comes from a perception that significant changes are needed to make big improvements, that rewards are proportional to risks, inspired mainly by romantic literature and fantasy.

To grasp the power of compounding small bets, I like to use an entirely different frame: think about casinos, and more specifically, the game of Black Jack.

Everyone knows that the house always wins. Yet, the Casino advantage in a BlackJack game played by a proper (and sober) player is about 0.5%.

It means that on average, the Casino has a probability of winning of 50.25% and the player of 49.75%

The number of card counting systems conceived by professional players is only matched by the investment Casinos make to catch them. That is a brilliant demonstration of how gathering bits of information while iterating fast can profoundly change the outcome of a game by shifting the odds just a little bit.

Tracking outcomes leads to accelerating the pace of iteration, which in turn leads to compounding learnings.

Using the parallel with economics one last time, it corresponds to living off the interests generated by the interests of your primary capital, which is the best definition I ever heard of what being wealthy means.

Managers can’t do it. So this is where leaders enter into play.

Autonomy is the key to success.

Peter Drucker shares Alfred Sloan’s thought on Authority and Responsibility in Adventures of a Bystander:

“Authority without responsibility is illegitimate; but so is responsibility without authority. Both lead to tyranny. Sloan wanted a great deal of authority for his professional manager, and was ready to take high responsibility. But for that reason he insisted on limiting authority to the areas of professional competence, and refused to assert or admit responsibility in areas outside them.”

My interpretation is that one can evaluate a team’s success based on the outcomes of their actions if, and only if, one first put the team in a position to decide their course of action.

I recently read a great article about How Big Tech Runs Tech Projects and the Curious Absence of Scrum. It highlights how “Empowered and autonomous teams are the building blocks of all these companies. They are also the key differentiator between many companies in the tech industry.” as well as “Teams with clear ownership is another building block of Big Tech. Each team of 5-15 people has a clear vision and mission, and the skills and autonomy to execute on it.”

Not randomly, Autonomy is the first of the three principles that, according to Daniel Pink, drive us: autonomy, mastery, and purpose.

To allow teams to have ownership and autonomy requires setting objectives, and goals that the teams understand, on which they agree upon and to which they commit.

More importantly, though, it requires a shared system of values and principles that allows a leader to trust that each team, given unexpected and changing conditions, will know how to make the right decisions.

In conclusion, my thesis is that autonomy is the essential characteristic of successful teams because measuring outcomes is the most effective way to lead teams to excellence.

And that is why I claim that managers can only succeed by becoming leaders.

Props to Trisha Reddy and Luca Sartoni for their valuable feedback.