When a Drone Became More Than a Line in My Spreadsheet

GRIP: Financial and Operations Analytics at Aerialoop in Medellín, Colombia

Cover image: One of my favorite reminders that there were real people and real machines behind every number in my model. I spent much of my summer tracing aircraft costs through spreadsheets, but some of the best learning happened when I stepped away from the screen— talking with the Aerialoop team, seeing the aircraft firsthand, and understanding the operational realities behind the data.

When I arrived in Medellín this summer, I expected my internship to be mostly about finance. I was joining Aerialoop, an autonomous logistics and drone company, as a Finance and Operations Analytics Analyst, so I assumed I would spend a lot of time building models, analyzing financial statements, and working with spreadsheets.

I did all of those things. But the most important thing I learned had very little to do with Excel. It started with a simple question: How much money does one drone actually make?

That question turned out to be much harder than I expected.

At Aerialoop, a drone is not just a piece of equipment sitting on a balance sheet. It has a manufacturing cost, flight hours, maintenance events, warranty claims, engineering time, satellite connectivity costs, deployment expenses, and revenue attached to it. I began building a model from scratch that followed each aircraft through that entire lifecycle— from manufacturing and deployment to maintenance and ultimately profitability.

That meant I couldn’t just look at the company’s financial statements and stop there. I had to understand what was happening behind the numbers. I spent time talking with people across the company, including the CFO, operations, manufacturing, and supply chain teams. I learned about the physical realities behind the data I was modeling: how aircraft were built, what components failed, how much engineering time went into keeping them operational, and how different customers and environments affected the economics of a deployment.

One of the most memorable findings came when I started looking at maintenance alongside revenue rather than treating it as a separate operating expense. At first, I was asking a straightforward question: how much does each aircraft make? But once I followed the costs through the aircraft’s lifecycle, I found that some rental contracts were generating revenue while Aerialoop was also absorbing significant costs to keep those aircraft operational. The revenue line alone could make a contract look attractive; the aircraft-level economics told a different story.

The more I dug into the maintenance data, the clearer the margin leak became. In some cases, Aerialoop was effectively paying out of pocket for repairs while collecting a fixed rental payment from the customer. That meant every maintenance event could quietly eat into the contract’s profitability. The issue was therefore not simply that the drones were expensive to maintain. It was a question of contract structure: who bears the cost when something breaks?

That changed the way I thought about maintenance. Instead of viewing it as an unavoidable operating expense, I started thinking about how maintenance costs should influence pricing, support fees, warranty assumptions, and customer contracts. A contract’s profitability depended not just on what Aerialoop charged, but on what costs Aerialoop had agreed to absorb.

The data also showed me how carefully I had to interpret what I was seeing. One aircraft had more than $300,000 of maintenance costs recorded against a manufacturing cost of roughly $41,000. I traced the figure back to a single salvage event and found component-level entries that appeared inconsistent with the economics of the aircraft. Rather than simply deleting the outlier, I had to distinguish between a genuine operational risk and a data-quality issue before allowing it to influence the model.

That was one of the biggest lessons of my summer: financial modeling is not just about calculating what happened. It is about figuring out why it happened— and what the business should do differently because of it.
That lesson changed how I approached the rest of my internship.

I started thinking less about building a “financial model” and more about building a system that could help someone make a decision. I connected accounting data with aircraft utilization, maintenance history, customer economics, capital allocation, and valuation. The goal became much bigger than calculating EBITDA or IRR. I wanted the model to answer questions that a CEO or CFO could actually use: Which aircraft are making money? Which contracts are destroying margin? Which customers generate the most value? Where should the company invest its next dollar?

That perspective also changed the way I thought about finance itself.

At Wharton, it is easy to think of finance as something that happens in a spreadsheet, a pitch book, or a valuation model. In Medellín, I saw how deeply financial outcomes are connected to operations. A change in maintenance costs affects margins. Utilization affects revenue. A warranty problem affects cash flow. A decision to manufacture another aircraft affects capital requirements. Those operational decisions eventually flow all the way through to enterprise value.

But understanding Medellín itself required a similar shift in perspective.

I did not want my experience of Colombia to begin and end at the Aerialoop office. On weekends and after work, I tried to experience the city and country beyond the version I could see from my laptop: With the Intern Group cohort of other interns, I visited Guatapé and climbed La Piedra, explored Comuna 13 and learned about the history behind its transformation, and spent time simply walking through Medellín rather than experiencing it as a backdrop to my internship. I tried fruits I had never eaten before, listened to local music, and experienced Colombian dance and other parts of everyday culture.

One of the experiences that stayed with me most was visiting Comuna 13. I had read about Medellín before arriving, but walking through a place with such a complex history made the city feel much less like a statistic or a destination and much more like a collection of communities, histories, and people. It reminded me that emerging economies cannot be understood only through the financial or economic indicators we study from afar.

That became an unexpected parallel to my work at Aerialoop. In both cases, I found myself becoming more interested in what sits underneath the headline number: A company’s revenue does not tell you whether a contract is actually profitable. A country’s GDP does not tell you what it feels like to live and work there. A spreadsheet can show you maintenance costs, but it cannot tell you why an aircraft is failing or who is bearing the cost. And a map can show you where Medellín is, but it cannot teach you the history of Comuna 13 or the energy of a city until you experience it yourself.

That is what I valued most about spending my summer in an emerging economy. I was able to learn not only how a company operates, but also how business, technology, infrastructure, and culture interact in a place that is different from the environment I know at home and at Penn. Being immersed in that environment made me more comfortable asking questions before forming conclusions— and more aware of how much context can be lost when we look at a problem only from a distance.

The experience made me more interested in the space between disciplines. I still love finance, but I now understand it less as an isolated function and more as a language for connecting decisions across an organization. Data tells you what happened. Operations help you understand why. And actually being present— whether on a factory floor, in a neighborhood, or around a table with colleagues— helps you understand the context behind both.

I came to Medellín thinking I would spend my summer building financial models. I left realizing that the most valuable models are not the ones with the most complicated formulas. They are the ones that help you see the real business more clearly— and that sometimes, seeing clearly requires getting much closer to the world behind the numbers.