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The obsession for efficacy, and why it is dooming our companies

There is not a single day when we don’t talk about innovation, digitalization and the competition between startups and big companies. We might miss the point when we do so, because the fundamental shift occurring is not digitalization by itself but the complexity it generates. Complexity causes a reduction of our capability to predict and makes us need to reinvent the way we design and manage organizations. All our beliefs about business are rooted in a world of simplicity, where the causal links were understandable and the evolution of markets foreseeable. This environment led us to focus on one holy metric, that eventually became an obsession: efficacy.

June 2016
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Executive Summary

Paris Innovation Review – Let’s start with the distinction between startup and big companies. Is it just a question of size?

Willy Braun – Startups are small to mid-sized companies but one should not, though, consider startups as big companies in their early days.

Startups have at least three distinctive features. They grow for years before trying to become profitable, they are fuelled by third party money (equity), they are scalable and benefit from low marginal costs if they manage to grow enough.

Startups focus on the resolution of a specific problem; big companies manage numerous products, each of them with many declinations. They often manage several brands. Startups are improving their value proposition to address – and sometimes create – a market; big companies have found their market(s), operate at large scale and produce products with zero default. Startups have teams of polyvalent hard-working talents; big companies employ an army of experts. Startups avoid meetings; big companies spend a lot of time in meetings to make sure everyone is on the same page.

This list is not exhaustive for sure. We could sum it up with the following statement: startups are agile companies looking for a specific home run, building products as good as possible to ensure fast growth under uncertain market conditions; big companies are machines optimized to produce high quality products, most of the time at large scale for numerous customers.

Big companies’ evangelists assert that startups should learn to be as effective as the very well designed and processed big companies, that have plenty of experts. Startups’ evangelists assert that big companies shouldn’t be so risk averse and spend too much time on discussions and planning.

So ultimately it’s a question of culture?

Yes. And to me this is the main lesson we can learn from startups. Basically they deal with uncertainty – uncertainty about their environment, business model, and even about the design of the services and products they offer. And their ability to deal with uncertainty allows them to accept the idea and experience of imperfection. While this is probably the most difficult thing you could ask a big company. Big companies live in fear of imperfection.

They should not?

Of course they have very good reasons to be careful. But I’d like to point something else, that could be called the perfection trap.

Whatever the company, people are looking for the perfect tools, the perfect methods, the perfect organizations and processes. This is easily explained. The business world can be seen as a race: the fastest, the most nimble incumbent wins. There is someone, somewhere, who is training or looking to do things more effectively than you do : you’d better beware!

This is why our personal metrics most looked upon is efficacy. Efficacy is different from efficiency. Efficiency means maximizing output for a given input. It is concretized into a high productivity and very little waste. Efficacy, or effectiveness, means making the right actions towards a given goal. But when efficiency is easy to measure, efficacy reveals itself only in the future. And it is really hard to know what would have been if the choices would have been different. So efficacy, which lays on predictability, is often substantiated with the conjunction of efficiency (operational excellence) and planning (strategic excellence).

You can be very efficient but if you do things that shouldn’t be your priority, you won’t be effective. You can be effective not being the most efficient worker as well – you just need to make the right decisions.

The aim for efficacy is pursue in our everyday action, like managing things as fast as possible : our mailbox, our to-do list, our meals, our meetings, our readings, our chores…

We'd be tempted to conclude that our main challenge is to separate efficiency from efficacy, to achieve the maximum speed with the right course of action. But the story is not over yet: efficacy too presents a risk.

This obsession for efficacy has led us toward a new ethos of professionalism, that we find in every big companies. Even customer service and employees happiness are considered as variables of efficacy. This obsession is dooming numerous organizations.

Why is that so?

Because of a major feature of our world: complexity. Complexity comes from the latin word complexus, which means “what has been woven together.” Complexity occurs when different elements that constitute the whole are inseparable, due to interactions and interdependencies.

Connecting is not just making a connexion end-to-end. When things are connected, the whole is looping back to constrain its parts. Changing the whole will affect every elements. The elements communicate with each other and affect the whole; its parts are often creating emergent properties, that don’t exist when taken on their own. Connecting elements generates systems which are greater than the sum of their parts. That makes it impossible to separate and focus on one part alone.

The most important consequence of complexity is that the future doesn’t unfold as linearly as it used to do. We used to play chess: each player made one move sequentially, the set of possibilities were predictable. Nowadays, the prown can communicate with the queen while the knights are moving. Not the usual chess game.

And consequently, strategy is getting tougher, and the gap between effectiveness and efficiency is often widening in practice. People, focusing on being as productive as possible, make unwise choices given the uncertain environment.

Today’s digitalized world is increasingly organized around networks. Human networks have always existed. We are social animals and we love to gather in tribes. But we made digital tools to make communication within and between tribes less costly and faster.

The consequence? Tribes can be spread around the world (so the limits of local network effects are diminishing), they can be bigger, talk more and faster, which improve their ability to act upon the world.

Remember that a century ago, you needed to send hand-written letters to people to communicate important matters.

Remember that half a century ago, you couldn’t reach someone on the phone if he or she wasn’t at the office or at home.

When everything moves faster and at a larger scale, the world is less and less predictable. Centralized systems, built around reductionist procedures, don’t work anymore. In a centralized system, the limiting factor is the time to have everything planned by the top of the hierarchy, then go down to the chain of decision (the descending flow) and, the needed adaptations suggested by the bottom (because, as Helmuth von Moltke said, “no plan survives contact with the enemy”) that eventually needs to be validated by the top (the ascending-then-descending flow).

This endless up and down flows add a lot of delay to any decision and make the relevance of exhaustive planning for efficacy very disputable when the need for adaptation increases.

Let me sum up: an ever more complex world is ever less predictable, so efficacy is less and less… effective?

More precisely: when things are prone to change faster and in more uncertain ways, we need to find the right trade-offs between efficacy and adaptability.

I’m not saying that you don’t need to search for efficacy. Our world is not ruled by pure randomness. There are things that can be predicted. The sole exercise of planning makes us understand interconnections and the set of the possible scenarios that can happen.

Chaotic systems don’t last long compared to systems where there is a constant dialog between order and disorder. The key is to find the right position between efficacy and adaptability.

If we try to reframe the initial debate between startups and big companies, that led to the fallacy of the ethos of professionalism centered around efficacy, we would say:

A big company is run by the force of cosmos (order): designing a machine and a set of strict procedures (removing chaos) to have the maximum efficacy and the biggest impact possible, under a set of predicted conditions. It is efficacy on steroid.

A startup is run by the force of chaos (disorder): adapting the product and organization to be able to fit its product with a market and grow as fast as possible, always reorganizing itself. It is adaptability on steroid.

A growing startup needs to increase its efficacy without losing its capacity to adapt. And a big company facing disruptions needs to increase its adaptability without diminishing its efficacy. In other words: startups need to become more robust; big brands needs to become more resilient.

Why are so many companies in a bad shape nowadays? Because they were designed in a simpler word and are too much focused on the efficacy side of the trade-off.

Then the question becomes: how to find the right position in the efficacy/adaptability tradeoff. Is it even possible?

Many authors are trying to reply to this question. Since the 2000s, after the .com bubble burst, and even more after the 2008 financial crisis, there has been an abundant literature addressing complexity issues, especially for the business word.

Nassim Taleb is a good example of this trend: his first book, Fooled by Randomness, reinstituted the importance of randomness in our today life, his second book, Black Swan, highlighted that reductionism can’t deal with statistical anomalies, which are epistemologically unforeseeable, his third book, Antifragile, proposed a new paradigm, where reductionism is forgotten and the goal is to design systems that get tougher when exposed to unexpected phenomena.

In the meantime, lots of talented authors have tried to help us decide better (see the Heath brothers’ book, Decisive) and forecast better (see Tetlock’s book, Superforecasting). But the most impactful book on the topic may be Team of Teams, a first hand thinking by General Stanley McChrystal, a US commander in Iraq and Afghanistan.

McChrystal’s troop were losing against Al Qaeda, while their ennemies had less resources (money, communication, weapons, intel gathering devices, technologies, etc.), were less trained and in fewer number. This experience of war against Al Qaeda, which is nothing else than a decentralized network of fighters, made him witness all the limits of a very efficient system: the rivalry between the specialization and departments which obstruct the information flow, the delay induced by the classical chain of command, the lack of the big picture by soldiers & intelligence specialists and correlatively the lack of understanding of the real issues on the ground by the commanders, etc.

The primary lesson detailed in the book is the need to reinvent organization and the necessity to create team of teams with empowered team members : “Creating a team of teams [enables] the insights and actions of many team and individuals [to] be harnessed across the organization. […] Doing this requires increasing transparency to ensure common understanding and awareness.

Why a team of teams? Because teams are the most efficient AND adaptable gathering possible. But they can’t scale. A team is often composed of three to twenty members. Afterwards, you lose what makes the team: everyone knows each other deeply (habits, personality, strengths and weaknesses) and trusts each other fully.

The classic answer is to create teams with a classical chain of commands. It makes it possible to benefit both from efficiency and adaptability on the ground (within the team) but still induces many delays in the decisions and suffers from the lack of trust between the teams.

So the answer is to create a team of teams, a network of relationships, where one team member knows at least someone in the other teams. This enables a good communication flow and creates a shared consciousness across the organization.

To manage to do so, you need to be transparent within the organization and between the teams (to ensure understanding and awareness) and to build trust and purpose.

But it is not enough.

Building a shared consciousness across the organization fixes the lack of trust and makes everyone onboard but we still suffer from the delays of decision, due to the chain of command. You also need to empower every team member.

This shared consciousness has to be tied with an empowered execution: McChrystal’s rule of thumb was : “If something supports our effort, as long as it is not immoral or illegal, a soldier could do it.” This is very close to the US NAVY’s practice of command-and-control, called “Command by Negation,” which stated that any subordinate commander have the freedom to operate as he/she thinks best, keeping authorities informed of decisions, until the senior overrides a decision. They use the acronym UNODIR (Unless Otherwise DIRected).

Empower execution is grounded in the design of shared consciousness: without the later you can’t get the former. “Creating a team of teams also often involves changing the physical space and personal behaviors to establish trust and foster collaboration. This can develop the ability to share context so that the teams can decentralize and empower individual to act. Decisions are pushed downward, allowing the members to act quickly.

Thus, with the right culture and infrastructures, decisions came more quickly. This is critical in a fight where speed was essential to capture enemies and prevent attacks. This is also critical in today’s business world.

And they discovered something unexpected: even as speed increased and authority went further down, the quality of decisions increased. “We had decentralized on the belief that the 70 percent solution today would be better than the 90 percent solution tomorrow. But we found our estimates were backward – we were getting the 90 percent solution today instead of the 70 percent tomorrow. A piece of this is the psychology of decision-making. An individual who makes a decision becomes more invested in its outcome. Another factor was that, for all our technology, our leadership simply did not understand what was happening on the ground as thoroughly as the people who were there. The ability to see the video footage and hear gunfire from an operation as it unfolded was a tremendous asset, but a commander on the ground can comprehend the complexity of a situation in ways that defy the visual and audible: everything from temperature and fatigue to personalities.

Eventually, these changes, which induce that information and decisions are no longer exclusive to leaders, raise the question of the role of the senior leadership in this new organization.

“I began to view effective leadership in the environment as more akin to gardening than chess, writes McChrystal. The move-by-move control that seemed natural to military operations proved less effective than nurturing the organization – its structure, processes and culture – to enable the subordinate components to function with ‘smart autonomy.’ It wasn’t total autonomy because the efforts of every part of the team were tightly linked to a common concept for the fight, but it allowed those forces to be enabled with a constant flow of ‘shared consciousness’ from across the force, and it freed them to execute actions in pursuit of the overall strategy as best they saw fit. Within our Task Force, as in a garden, the outcome was less depending on the initial planting than on consistent maintenance.

The leader’s role is to nurture the organization instead of planning and controlling.

And as expected, the mental transition from heroic leader to humble gardener is not a comfortable one, yet it has led to great successes.

Quite impressive. And this leads us to the very last question: what is the adequate degree of “smart autonomy” to provide?

This is probably the greatest question in today’s business world… but it was also the greatest question in yesterday’s business world! Taylor’s factory was a brilliant answer, without doubt the optimal solution in its time.

The point is that there is probably no such thing as an optimal solution today. Markets are way too diverse. The right level of autonomy to provide depends on the level of complexity of the environment where you operate, and is found in the crossing of the adaptability and predictability functions.

Startups and big companies face the same challenge: understanding the relationships between complexity, predictability and adaptability. As the complexity of our world increase, our ability to predict it diminishes and the need to be more adaptable increases, which implies giving more autonomy to workers.

This understanding is vital to ensure effectiveness over time and not the lure of a bad use of efficiency: being productive in the things that don’t matter now or worse, being productive in the things that matters now but keeping doing them when you have to adapt.

This is the incredible discovery of Christensen in his magnum opus The Innovator’s Dilemma: great firm fails not because they are not efficient enough (“poorly managed”), they fail because they lose their ability to adapt to changing conditions.

Why are tech giants still succeeding? Because they have been designed in an environment where the complexity was already high (an innovation-driven market), so they’ve granted their workforce with sufficient autonomy and build the right structure, process and culture, whereas most of the big companies used to work in an environment that was less complex that today’s world. This is the story told by Eric Schmidt and Jonathan Rosenberg in How Google Works.

We used to say “What gets measured gets managed.” But a more reality-matching aphorism would be “What gets foreseen gets managed”… and for everything else you just need to have the right organization, designed to adapt continuously and with proper right level of efficacy.

Willy Braun
Co-founder, daphni