While thinking about AI, sometimes I find myself spiraling—not in fear, but in fascination. Neural networks, as we use them in artificial intelligence, are modeled (loosely) after the way our own brains work: a web of interconnected nodes that adapt, reinforce, and prune connections based on feedback. It struck me: if this structure is good enough to power both intelligence and adaptation, what would it look like to run a tech team in the same way?
That thought stuck. Because if there’s one thing our industry still struggles with, it’s resilience. Not just uptime resilience, but people resilience. Process resilience. Momentum resilience. So here’s a thought experiment—how do we build tech organizations that operate more like the human brain?
I'm about to throw a lot of concepts at you. Treat it like a quick checkup. You're probably doing many of these things, but jot down any that could be improved upon or implemented. If you want expansion on any of them, let us know! Ok, lets go:
Systems Redundancy
We start with the obvious parallel: system resilience. The brain has backups. If one part is damaged, another can compensate. In tech, we strive for the same with graceful degradation. That means when something fails, the experience slows down instead of crashing entirely. A part of that comes from using messaging queues, which let systems talk to each other asynchronously—a bit like how your brain doesn't expect every neuron to respond instantly.
When you architect with microservices and fault tolerance, you’re giving your systems a way to keep functioning even if one module stumbles. That, paired with multiple availability zone deployments, ensures even physical infrastructure failures don’t take you offline. It’s not just about keeping things up, but keeping things flowing.
A truly resilient architecture embraces these principles holistically. Design with stateless services so instances can die without pain. And lean into immutable infrastructure, which favors deploying clean new versions over patching fragile old ones.
Chaos Engineering
Brains are forged in stress. So are tech teams. That’s why I believe in Game Day exercises, or planned failure drills as safe spaces to break things before the real world does. They help you learn how your team and systems behave under pressure, before it’s real. You can take that further with controlled latency injections—intentionally slowing systems down to see what cracks.
And of course, you need to practice disaster recovery, not just write about it. What happens if your region goes offline? Your team should know that answer because they've rehearsed it. Across all of this, runbooks are essential and we’ll discuss them more below.
Organization Redundancy
Now zoom out. What happens if someone on your team disappears for a week? Or forever? That’s where end-to-end ownership matters. When a team builds something, they should run it; not as a burden, but as a way to retain critical context.
To share that context, you need runbooks and playbooks, for daily operations and onboarding as well as outages. Pair that with role rotation and shadowing to let people cross-pollinate knowledge. The best teams are those where no one person is irreplaceable.
And the glue for all of this is documentation. Not random wikis. Structured, searchable, service-and-function-oriented docs in an internal developer portal. It’s your long-term memory. Without it, you’re back to tribal knowledge and risk.
Adaptive Culture (Not Heroic)
The brain doesn’t have a single heroic neuron. Teams shouldn't either. Start with code reviews. They build collective understanding, standards, and trust. Then keep your backlog prioritized, so your team is always working on what matters most.
Resilient cultures normalize asking for help. If something is hard, your team should be able to say so. And when success happens, it should be about team ownership, not individual heroics. Make that stick with capacity-aware planning that reflects actual availability and energy, not just sprint math or arbitrary velocity targets.
To navigate uncertainty, use decision-making frameworks. Define who decides, who contributes, and how input gets gathered. It’s the difference between a slow stall-out and a clear path forward.
Plasticity Needs Feedback
Brains learn through feedback, and so do teams. That starts with team retros—intentional space to reflect, learn, and adapt. But it also means doing blameless postmortems when things go wrong. No finger pointing, just honest inquiry.
Then bring in outside input. Your team should have access to customer feedback. That’s how they build intuition and care. And while we talked about code reviews earlier in Adaptive Culture, here’s the twist: they’re not just a gatekeeping step. They're also one of the most effective peer-to-peer feedback loops we have.
Conclusion
Brains don’t aim for perfection. They aim for adaptation. Your tech org should do the same.
- Can any single server failure wake the CEO? If yes, revisit Systems Redundancy.
- Do you run chaos exercises when you change infrastructure? If no, schedule a Game Day before your next deployment.
- Could two random engineers swap projects tomorrow? If no, bolster Documentation and Role Rotation.
- Does every decision have a named owner and a due date? If no, adopt a decision framework.
- Do outages end with actionable, blame‑free follow‑ups? If no, rewire your postmortems.
If any of these gave you pause, good! Keep adapting. It’s a key aspect of building thriving teams and resilient products.