Blog

The first part of this book concerns an experienced CEO called Kathryn, who is brought in to fix a software company. Agonisingly, she turns around its dysfunctional leadership team, its decision-making, and its results. The second part unceremoniously picks apart the desiccated carcass of the tale, adding worksheets and teaching material.

Pyramids as diagrammatic aids are troublesome at the best of times, as it’s never clear from a glance what the illustrator wants to say about the hierarchy of information. Do we head from base to summit, as one might climb a real pyramid, or is our eye supposed to scan it from apex to base as a page of text, the tiny top broadening to weightier things? How do the middle tiers interrelate? The Five Dysfunctions themselves are depicted as a pyramid. Thus the only graphical aid in the book requires the whole text to explain it.

Never mind. The core message is that great teams argue, all the time. They argue to determine a company’s goals; they argue to decide how money and time is spent; ideally, they argue without ego. Teams that hold meetings without conflict hold meetings without consequence, where nothing is decided, and people zone out, dumbly acquiesce, or cower in fear. Lencioni says that the most fundamental dysfunction is an absence of trust. Everybody has a different perspective of what’s important and why, so meetings should be dramatic: full of tension, disagreement, fresh perspectives, and negotiation.

This being a bottom-up pyramid, trust is drawn at the base. This is the first step in fixing a team: it is on trust that everything else rests. Every member of the team has to trust the others. To invite disagreement, they must allow themselves to be vulnerable to criticism and counter-argument without their openness being abused by personal attacks or political plays, and this requires good facilitation. The antithesis of trust is invulnerability: if you don’t argue, you won’t ever lose, but you’ll have to sit through a lot of lousy meetings.

Absence of trust / Invulnerability

Without complete trust, there cannot be authentic, game-free argument. The next stage of the pyramid concerns unfiltered conflict. In a supportive and respectful environment, ideas are primal, truth wins, and it doesn’t matter who has volunteered a suggestion, only how appropriate and useful it is. Conversely, in places where a person can be shouted down for dissent without a real counter-argument, there will be people who feel they are being neutralised for political reasons. They will not buy into the eventual decision, and may decide to strike out alone.

Fear of conflict / Artificial harmony

The result of hours of conflict and resolution is a plan that the whole team can commit to. If there isn’t universal agreement, some people will not contribute sufficiently to the plan, so the overall mission of the company will not be coherent or complete. Argument airs the rationale behind a decision, which allows people to understand and commit.

Lack of commitment / Ambiguity

Commitment to a plan then leads to responsibility to deliver it. This means maintaining high standards throughout the team, and leads to other necessary conflicts: holding team members to account if they miss targets, ignore work, or get distracted by other goals.

Avoidance of accountability / Low standards

People may be members of many teams, while also attending to their personal ambitions. One team always comes first. If you’re on the leadership team, that’s your first team. Maintaining trust and confidentiality in your first team is paramount because it’s how a company succeeds. It’s important that you work towards the collective goal, that conflict will be handled with discretion, and that the team can call you out if you don’t perform.

While it’s easier to let people pursue individual, ego-led projects than holding them to account when they fail to deliver collective goals, it’s the wrong thing to do. You accept such attitudes at your peril.

Inattention to results / Status and ego

These problems are simple to state but, as we see in other books, it’s not always possible to dismantle political structures that preserve internal truces. Sometimes teams cannot be made to work like this without replacing parts of them, or forcing them to change in the face of an external crisis.

I didn’t find this book at ROLI, but Matthew Syed’s everywhere these days and this one looked like it might be worth a glance.

Matthew Syed is a tall man with no hair. His book is full of this kind of observation that never goes anywhere: the muscular build of a bereaved man; the hairstyle of a pilot who committed suicide. Here we are, forensically deconstructing the aftermath of a medical accident; suddenly, Syed jump-cuts to a widower’s eyes, welling as his tapering fingers tremble.

This gives the reader two problems. First, the central message of this book seems claustrophobic and insincere because he is constantly distracting us from it. Second, these soft-focus vignettes sit poorly with the tragic flavour of much of its material. Sometimes they feel voyeuristic. At other times, when you feel Syed is building up to a killer punch, he pulls it. This surgeon’s tyranny in an operating theatre nearly killed somebody. His obstruction of a subsequent investigation nearly leads to more deaths. But remember, he’s a hero. They’re all heroes. Atul Gawande trod this ground years before Syed, and did it authentically, as a driver rather than a back-seat passenger.

Syed’s book, then, is a poor recruiter for a great employer. At its core is a simple, powerful and universal message about the power of scientific inquiry. Here again, though, some flaws are unforgivable. Central to a writer’s integrity is a clear and honest use of words. You can’t tell your readers that science is what they need in their lives and then, in the next sentence, cut off its limbs to fit your bed.

Syed refers to ‘open-loop’ and ‘closed-loop’ thinking in a way that, for no good reason, inverts the established meaning of these terms. Hence, a ‘closed-loop’ system which, to the millions of us with technical training, is something that is ‘closed’ by a path that provides corrective feedback, is now ‘closed’ in the sense of ‘guarded against feedback and the influence of evidence’. Did anybody edit this book?

Lesson one: collect data about everything you’re doing. The title ‘Black Box Thinking’ refers to the two data recorders that capture the cockpit voice and telemetry in aircraft, so that crashes and near-misses can be better understood. Dispassionate forensic analysis of this data provides vital information about what went wrong. If you don’t have data, you’re reliant on lucky guesses to prevent disaster, and highly susceptible to the interference of people with their own agendas.

Lesson two: depersonalise this information, and don’t use it to shame people. The fear of shame leads to the deliberate concealment of errors, so everybody loses opportunities to learn. Humans are fallible under stress, and the first duty of a crash investigator is to improve flight safety. Before critical failures, there are near-misses, and people must be allowed to report and challenge these without fear. The exemplary attitude in aviation allows mechanical problems to be caught at an early stage. Best practice is also improved in the cockpit. In-flight checklists control the narrowing of a pilot’s concentration under stress; improved human factors fix problems with the flying controls; Crew Resource Management addresses the psychological difficulties of cockpit hierarchy. This is why, even as aircraft become more complicated, and the skies more congested, civil aviation gets safer.

Lesson three: learn by building, make marginal gains, iterate often, create theories and try to falsify them. Syed summarises with unusual concision, ‘If I want to be a great musician, I must first play a lot of bad music. If I want to become a great tennis player, I must first lose lots of tennis games. If I want to become a top commercial architect known for energy-efficient, minimalist designs, I must first design inefficient, clunky buildings.’ Nobody gets great without a lot of practice. If you’re a product company, solicit feedback from customers at a really early stage, while you’re a bit embarrassed by your offering: you’ll learn if you’re doing a really great job designing the wrong thing.

On the subject of iteration, there is another use of the term ‘Black Box’ that is more commonly employed by engineers. A Black Box model is one in which a system is characterised merely by measuring and relating its inputs to its outputs, without attempting to understand the internal process that connect them. This might have been woven into the central chapters on evolution and marginal gains. Here, in many places, it would have bolstered the book, but it doesn’t. The dual meaning is dismissed in a footnote on Page 33 and never mentioned again.

In a central chapter, Syed notes that Unilever employed physicists and biologists to approach a difficult optimisation problem from two directions. Detergent granules are produced by firing a hot, pressurised liquid through a nozzle into air, where it rapidly solidifies and lands as a powder. The powder has to have the right grain size and consistency, and be adequately mixed, so the nozzle design is critical.

First, as Syed narrates it, physicists tried to characterise how the nozzle worked by modelling the flow of fluid through it. Their failure to build a decent nozzle highlighted the intractability of the problem. A team of biologists then successfully optimised the nozzle with a typical ‘black box’ approach: starting with an existing, poorly-functioning prototype; measuring the powder it produced, tweaking its design, and favouring the best-performing candidates over dozens of generations; finishing when it was as good as it seemed it was going to get. Hundreds of prototypes later, the ‘black box’ approach produced a great nozzle.

Syed narrates this as a victory for the empirical, evolutionary approach. Dyson, who created thousands of prototype vacuum cleaners in order to arrive at the DC01, is press-ganged into Syed’s war. Take that, theorists!

Had Syed been a scientist — had he taken his own advice — he would have seen this story as more than a battle between schools of philosophy. Both teams’ methods are in alignment with best practice: each collected data, analysed it dispassionately, and thus approached the truth. One school attempted to find a theory to solve the general case, realised that their best guesses were false, and conceded defeat. The other school set out to attack a specific case — a smaller problem — and succeeded. My conclusions would be:

1. Failure informed the approach that led to success, as it often does. Failure’s a great teacher, but a slow and expensive one.
2. Reducing the scope of the problem allowed a different tool to be used, which succeeded. The price of success was the abandonment of a general understanding of the problem.

Unilever have a brilliant nozzle, and the method that produced it, but they’ll never know why, or whether there’s one they missed that produces three times as much powder. Every time they want to increase flow through the nozzle or reformulate their detergent, they’ll have to make a hundred more prototypes.

Lesson four: understand and eliminate cognitive dissonance. Resist the temptation to spin failure as a success, or deny that something went wrong. Accept such failures as an opportunity to learn and improve.

If you’re involved in a technical discipline, you’re already a servant of hard physical truths. No amount of post-event rationalisation will excuse a prototype that is not fit for sale. (Although, if you’re building Mars landers for the European Space Agency, it seems you can crash-land at every attempt, act as though you succeeded, and continue to get funding, but I’m talking about real jobs.)

An external perspective of scientific method may help a wider audience to discover it. There are certainly pickings in this book for technical readers, but it’s principally for an audience who don’t get, or even seek, the same class of feedback from their work that the technologist must. Recommend it to your boss. Next time you have a corridor conversation, though, remember that ‘closed-loop’ is open-loop, ‘open-loop’ is closed-loop, and ‘black-box’ means collecting and responding to data. Or ‘science’.