"Can everyone hear me?" "Can you see my screen?"
That’s how they all start these days. Teams calls. Zoom. Every digital or semi-digital gathering where humans try to connect through pixels and bandwidth.
We ask because we need it. Feedback. Confirmation. The basic human assurance that we're being seen and understood. It's wired into us—this ancient need to know we are seen, our voice is heard, our message is understood.
For thousands of years, we got that feedback instantly. A nod. A frown. A smile. The subtle shift in posture that told us whether we were making sense or losing the room. These are the signals that activate our social super-power—collaboration.
But now? Now we're asking the same ancient brain that craves qualitative, human feedback to interpret conversion funnels, uptime metrics, and Net Promoter Scores. We expect ourselves to read spreadsheets with the same intuitive fluency we once used to read faces.
It's like asking someone who learned to navigate by stars to suddenly pilot by sonar.
Qualitative signals are our native tongue. The raised eyebrow that says "I'm confused." A silence can telegraph "I disagree." The long pause before someone responds. We process these instantly, intuitively, with stunning accuracy.
But quantitative signals? Metrics? Dashboards? That's a second language, a code, we're still learning. And like any second language, we make predictable errors.
We confuse correlation with causation. We misread what the numbers are pointing to. We overreact to noise and under-react to patterns. And worst of all, we build processes and products on top of those misunderstandings—codify the errors into the foundation of our systems.
This code gap creates cognitive debt—the accumulated flawed mental models and broken signal interpretations that mislead us.
In legacy environments, that debt compounds quickly. APIs stitched to batch jobs. Workarounds codified in spreadsheets (or in someone's brain). Unstructured tribal knowledge accepted without question.
We get dashboards that track performance—without explaining it. Metrics that signal urgency—without revealing cause.
A team sees order delays and EDI errors occurring at the same time. They enhance the EDI integration. Error rates go down. But on-hand inventory? Still low.
Why?
Because the real blocker was a manual document-matching step inside a legacy ERP module, owned by another silo, invisible in our system flow.
Ugh.
That's the cost of fixing what's visible, not what's causal, of speaking fluent metrics while missing the meaning.
And each enterprise function speaks its own dialect of this broken code.
Sales chases volume. Operations optimizes throughput. Finance focuses on collections. Customer service reduces handling time. And product chasing features nobody wants.
Everyone is optimizing locally. No one solving systemically. We become brilliant at fixing our slice of the problem—and blind to the whole.
This is where organizational design should help.
Stream-aligned teams, as part of team topologies, are meant to focus on outcomes, not output. Platform teams are supposed to enable flow, not just publish tools. Architects should guide us toward flexible, decoupled systems—not gatekeeping delivery. Yet, without a shared understanding of what actually drives customer value, all this structure becomes scaffolding for dysfunction.
Stream teams ship faster—in the wrong direction. Platform teams build infrastructure—no one can use. Architecture delivers blueprints—no one follows. And business is left wondering what the hell is going on.
Meanwhile, questions about outcomes go unexamined. Does this capability materially change why a customer chooses us? Are we solving for something meaningful—or just familiar?
And when a team finally finds the root cause—a flawed process, an outdated rule, a redundant handoff—they run into a deeper obstacle:
"That's how we've always done it." "We need these six approvals." “That’s outside of our operation.”
Culture debt. Process debt. Cognitive debt.
If change management is reactive, product teams will automate dysfunction instead of eliminating it.
They'll build gorgeous dashboards tracking irrelevant metrics. They'll make broken processes faster. They'll ship features that harden old logic.
The organizations that move with purpose aren't just good at reading dashboards—they're good at reading between the lines. They treat metrics like pattern: not just signal. What's under this number? What tension or behavior is it trying to reveal?
Their teams are fluent in both languages—human and machine. They know when a metric is a warning and when it's just weather. They don't just measure what's easy; they dig for essential.
And their leaders? They clear fog. They make space for clarity and velocity.
"Can everyone hear me?" isn't just an audio check. It's a question about alignment. Signal strength. Shared understanding.
Are we hearing the real problems? Are we solving what matters? Or are we just moving because motion feels like progress?
Technical debt slows you down. Cognitive debt keeps you from seeing where you're going at all.