I was 20 years old and working as a clinical coordinator for a health plan operator in Chicago. My cubicle sat near the main call center that served our customers, which meant I had a front-row seat to the chaos.
A queue board was mounted on the wall. It constantly flipped between red and green. Red meant we were missing our service-level agreements (SLAs): customers were waiting too long, penalties were looming, and managers were scrambling to push more people onto the phones. Green meant we had overshot the mark: too many people staffed, not enough demand, and ballooning payroll costs.
Staffing often swung by as much as 22 percent, leading to either penalties and overtime or wasted labor.
It was a coin toss of which ditch of waste we’d land in on any given day.
One afternoon, a director walked by my desk looking particularly frustrated. In a moment of youthful confidence (or naïveté), I muttered something that would change the next few months of my life.
“It just seems dumb that nobody has stepped back to better predict call volumes and demand curves to staff against,” I said. “It feels blindly random.”
She stopped and turned toward me.
“Oh yeah, hotshot? What would you recommend?”
I shrugged. “I don’t know exactly, but surely if you looked at things like customer types, plan structures, historical seasonality, where a customer was in their implementation cycle, time-of-day patterns, and other drivers… There must be some order in the chaos. How hard could it be? We shouldn’t be wasting this much.”
The next morning, I was summoned to her office.
“You now work for me,” she said. “You have 90 days to solve that. Get to work.”
I remember stammering: “So… here’s the thing. I don’t actually know how to do that. It just seems like it should be possible.”
Her response was simple: “Figure it out.”
At the time, I was working full-time and attending college. So I went to my statistics professor for help.
He listened patiently as I explained the challenge.
“You’d need to take about four courses over the next two years to learn the theories needed to build something like that,” he told me.
“I’ve got 90 days,” I replied.
He looked at me like I’d just asked him to bend the laws of physics. “Good luck, kid. That’s not how things work in academia.”
Business rarely gives you the luxury of waiting two years.
So I started digging.
Microsoft Excel 97 could handle only about 66,000 simultaneous computations before it corrupted the workbook and imploded. I learned that the hard way.
So I enrolled in a crash course on Microsoft Access databases to handle the tables, queries, and comparisons needed to wrangle the data.
I met with IT teams, analysts, and business leaders—anyone who might help me better understand the system. Nights and weekends were spent on spreadsheets, models, and trial-and-error experiments.
Slowly, patterns began to emerge.
What initially looked like chaos actually had structure underneath it. Customer segments behaved differently. Implementation phases mattered. Time-of-day demand curves had rhythms. Seasonality influenced volume. Certain plan types created predictable spikes.
In other words, the system wasn’t random. It was complex.
By identifying the key drivers and weighting them correctly, we built a workforce forecasting model that reduced our margin of error from roughly ±22 percent to less than ±6 percent.
The impact was immediate. Operational expenses dropped. SLA penalties fell dramatically. Customer satisfaction improved. The organization finally had a tool to make smarter staffing decisions.
However, the most valuable outcome wasn’t the spreadsheet. It was a lesson.
First, behind what appears to be chaos, there is often an underlying order. Economic systems, customer behavior, and operational processes all have patterns. Leaders must develop the discipline to hunt for the threads: drivers, indices, and variables that explain what’s really happening.
Second, complexity doesn’t mean impossibility. Often, dozens of variables are in play, but not all of them matter equally. The real work is identifying the handful that drive most outcomes.
Third, progress often belongs to those willing to hustle and figure things out. Too many barriers in organizations are artificial: You need the degree. You need the certification. You need the budget. That problem can’t be solved.
Many of those barriers exist because no one has tried hard enough yet.
I resonate with Elon Musk’s idea that we should question every constraint not anchored in physics.
Fourth, sometimes the journey itself is the training ground. That project probably taught me more practical business insight than the degree I was simultaneously paying for. The pressure, constraints, and uncertainty formed muscles that would shape the rest of my career.
My college eventually disappeared. The company was sold. The software I used is now ancient history.
The lesson, however, stayed.
If you want to advance in a large organization, or any organization, you must be willing to solve problems. Create value. Step back and care more about the system than your title or job description.
Finally, a word of caution: Watch it, hotshot.
It’s easy to criticize from the sidelines, to point out what others should be doing differently. But if you’re not willing to step into the arena and figure it out yourself, your criticism doesn’t help much.
Things always look simpler from the spectator seats.
The arena is messier, harder, and more complex.
And that’s where growth happens.
Do hard things. Solve real problems. Dive into the chaos and look for order.
You might just build something valuable—and along the way, build yourself.
