One of the most inescapable edicts when leading a team is the order to optimize the system towards the organization’s goals. It comes up across industries and at every conceivable stage of an organization, whether an early-stage startup optimizing for experimentation or a later-stage group optimizing for growth or an aged institution optimizing for preserving revenue. There’s an always-on assumption that there are still yet more efficiencies to be found, if we go looking for them, still yet more ways to hone the team’s focus, to turn laser-eyed onto whatever it is the executive team has deemed most necessary and then light that thing up.
But what happens when those optimization efforts collide with an unpredictable environment?
But you can’t optimize systems in a context that’s changing, especially if it’s changing in unpredictable ways. Removing inefficiencies when circumstances are as anticipated means that there isn’t much slack in the system to respond when the unanticipated happens. Optimization is intrinsically brittle, because it’s about closely matching the output to the conditions, which means it’s vulnerable if those conditions change. What we’ll need from our infrastructural systems, more and more, is for them to be resilient, able to absorb uncertainty and changing circumstances either without failing or by failing gracefully and reversibly, rather than unexpectedly or catastrophically.
Chachra, How Infrastructure Works, page 249
(Emphasis mine.) Chachra is talking about our collective, public infrastructure here—think of the systems that bring water and electricity to our homes—but I will take the message more generally and argue that this conceptualization of optimization applies also to the evergreen calls for managers to optimize their team’s output and to the likewise frequent orders for us to all optimize our own lives. The problem (or at least one of the problems) is that the twin edicts to simultaneously optimize your team and life and to be flexible in light of an uncertain future are in opposition to each other. Optimization presumes a kind of certainty about the circumstances one is optimizing for, but that certainty is, more often than not, illusory. Here’s Chachra, again:
Making systems resilient is fundamentally at odds with optimization, because optimizing a system means taking out any slack. A truly optimized, and thus efficient, system is only possible with near-perfect knowledge about the system, together with the ability to observe and implement a response. For a system to be reliable, on the other hand, there have to be some unused resources to draw on when the unexpected happens, which, well, happens predictably.
Chachra, How Infrastructure Works, page 209
Another way to look at this is that you cannot optimize for resilience. Resilience requires a kind of elasticity, an ability to stretch and reach but then to return, to spring back into a former shape—or perhaps to shapeshift into something new if the circumstances require it. Resilience is stretchy where optimization is brittle; resilience invites change where optimization demands continuity. But whether we’re talking about our public infrastructure or our workplaces, our streets or our lives, it’s change we need to be ready for. Whatever is ahead for us, it’s not more of the same.