Learning is Expensive, but So Is the Status Quo

On Friday, I spent the day working with our operations team on parking spaces, signs outside of bathrooms, and organizing the school kitchen. I kept thinking: this is definitely a diverse job. Also, being certified as a food safety manager came in handy, which—noting for my wife—totally justifies my argument that attending culinary school was legitimately for work.

All of those tasks are part of our effort to get ready for the start of the school year, but their diversity and bite-sized nature create problems. Thousands of things happen each day, but every time I look at the to-do list, there seem to be thousands more to go. It’s sometimes hard to feel the progress when that’s the case, and every day seems chaotic. 

But in moments of rest, here’s the question I’ve been asking the team: If we’re doing the same things every year, why does it feel so hectic?

It’s not just the back-to-school effort. I’d guess that well over 70% of what we do—sending report cards, ordering supplies, painting the buildings, doing preventative maintenance—is mostly the same each year. For example, we have new students and staff every year, but we know months in advance when they’ll need their computers. So if a repeated process like that feels hectic, what choices are we making in how we approach the work that makes it harder than it needs to be—both technically and emotionally?  

This isn’t a school-specific problem. Swap in your own organization’s version—budget season, hiring cycles, product launches—and I’d guess the same dynamics show up. Repeated projects somehow still feel like uncharted territory, spark the same “last-minute” requests, or become a rush to the finish line every time.

On one level, we should give ourselves grace, since we’re fighting human nature. Most people don’t pay their taxes in January—they wait until the April 15 deadline provides the urgency to act. We all know Christmas and our loved ones’ birthdays fall on the same date every year, but most of us don’t get around to buying presents until a few weeks before. (The exception may be my mother-in-law, who buys a year’s worth of cards at the start of the year and gives everyone the same $20 gift.)

But I suspect the real challenge is that most of these activities get done just fine. Taxes get paid, presents get bought, schools open on time. Because there’s no disaster, because it mostly works out OK, there’s lower impetus to change. In that world, it’s more intuitive to do a quick debrief that surfaces glows and grows than to do a true after-action review—one that takes a few more hours to examine why the glows and grows occurred and to actively integrate the lessons into next year’s plan. 

It’s not that anyone disagrees with the notion of learning. But because there’s always lots of work to do, investing deeply in learning when next year’s deadline is far away seems like an expensive use of time. 

What’s not always obvious is how expensive the status quo is.

Waiting until we feel the pressure of a deadline has costs. If that pressure leads us to micromanage our teams—the frequent check-ins, the pushing—that has costs too. And carrying the micro inefficiencies, like people having to recreate work because we didn’t take the time to codify and properly store last year’s version, has costs. 

Except all of these are dull pains—the kind that don’t show up on a single bad day. And the upside of addressing them is just as abstract. Namely, working in a way that provides more buffer gives more time to consider whether we can do things differently. The team can avoid thinking, “We don't have that much time, let’s just do what we know works.” Instead, there’s more mental space to imagine what’s possible.

That’s the case for building deep learning into routines rather than relying on willpower in the moment. If doing the after-action review is a choice each team has to make fresh, weighing the value of the learning against the pull of the next project, the next project wins most of the time. But if it’s automatic and feels like a natural part of closing out a project, the team is much more likely to do it and achieve the gains from the process.

The cost of the hours spent on deep learning review shows up immediately and obviously competes with everything else we have going on. But the cost of skipping those learning routines shows up later and is spread out—a team that’s a little more tired, a little more managed, a little more likely to keep doing things the same way because no one had the space to ask if there was a better one. It’s really a matter of which cost we're prepared to pay.