Systems Thinking, Leadership
Systems Thinking and the Curious Case of the Anthill
Long before I encountered enterprise architecture or AI, an anthill taught me a lesson in systems thinking: the best solutions emerge when we focus not only on what needs to change, but also on what must be preserved.
4 Min
INSIGHT
FIELD NOTE
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Systems Thinking, Leadership
4 Min
estimated reading
2026
published
During a summer holiday in my school years, I was given what appeared to be a simple task: clean a small storeroom where ants had begun building a formicary beneath the floor. The first signs of an anthill were already visible.
The room was separate from our main living space and used primarily for storage. The prescribed solution was straightforward: pour water into the anthill, clean the room and seal the opening with cement or plaster.
Those were my precise instructions. Fortunately, because it was the summer holiday, nobody gave me a deadline.
Something about the solution did not feel right.
Why should scores of ants have to die simply because we wanted the room to be clean? Surely, I thought, there had to be another way.
Looking for a better answer
I set out to find one.
My first source of advice was a beekeeper who had previously helped remove a beehive. The beekeeper suggested using smoke. That was how wild honey could be collected without killing the bees.
Aha! That sounded promising.
After nearly suffocating myself in the process, I had some success. The ants reacted to the smoke, but they were remarkably stubborn. They did not leave.
My next stop was a veterinary doctor who lived near the edge of our colony. The veterinary doctor explained that while smoke could act as a deterrent, a strong spice placed around the anthill might irritate the ants enough to make them relocate.
Armed with this new idea, I went into the kitchen and selected what seemed to me the strongest and most irritating spice available: chilli powder.
I sprinkled it generously around the anthill, doing considerable damage to my own sense of smell and respiratory system in the process.
Nothing happened that day.
The following day, however, I saw a procession of ants moving away. By the third day, there were no visible signs of activity.
I carefully inspected the anthill and the formicary beneath it with a thin coconut-broom stick. It was long enough to reach inside while causing minimal damage. Once I was satisfied that the ants had moved away, I cleaned the room and sealed the opening.
I was delighted.
My parents were less impressed.
They could not understand why a small cleaning task had taken me an entire week. Nevertheless, they approved of the eventual result.
The lesson I did not recognise
It took me many years to understand what I had actually learnt from that experience.
We often approach problems like this:
Problem → Solution
The anthill taught me a different approach:
Problem
→ What are we trying to achieve?
→ What should be preserved?
→ What is the real issue?
→ Solve
The stated problem was the anthill.
The desired outcome was a clean storeroom with a sealed floor.
But the ants were not necessarily the problem. Killing them was simply the most obvious route to completing the task.
Once I distinguished the outcome from the prescribed solution, a different possibility emerged: allow the ants to relocate, then clean the room and close the opening through which they had entered.
I did not have the language for it then, but this was one of my earliest lessons in systems thinking.
The human part of problem-solving
Today, we have access to extraordinary infrastructure and tools. Some technologies are beginning to approach human-level reasoning in specific domains. They can shorten the path to execution, evaluate possibilities and help us act faster than ever before.
Yet the most important elements of problem-solving remain deeply human.
Before acting, we still need to ask:
What are we really trying to achieve?
What should we preserve?
Who or what could be affected by our solution?
Are we addressing the real issue or merely its most visible symptom?
Artificial intelligence can help us analyse a problem, generate options and accelerate execution. But it cannot relieve us of the responsibility to frame the problem thoughtfully and consider the consequences of the choices we make.
Technology can help us solve problems. Only thoughtful people can ensure that we are solving the right ones.
And, rather fittingly, the anthill taught me one more lesson that has remained relevant throughout my career:
The most thoughtful solution may not always delight the stakeholders, especially when it takes a week to complete what appeared to be a simple task.
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Long before I encountered enterprise architecture or AI, an anthill taught me a lesson in systems thinking: the best solutions emerge when we focus not only on what needs to change, but also on what must be preserved.
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