There are moments in our lives that refuse to leave us.
They linger in the background, resurfacing every now and then as half-finished thoughts, what-if scenarios, and unanswered questions. Sometimes these moments are born from failure, sometimes from decisions that did not work out as intended, and sometimes from ventures that looked promising but could not find their place in the world.
For me, one of those moments has always been AirLoop.
AirLoop was not a passing idea scribbled on the back of a napkin. It was a venture we poured our energy into, with a clear vision to transform how small and mid-sized businesses could engage their customers. We created a loyalty engine that was tactile, intuitive, and surprisingly elegant. It worked across devices with no technical hurdles for clients. It was simple to deploy and did exactly what we promised. And yet, despite everything we got right, we could not scale.
For years, that question has stayed with me. Why not? Was it fatigue? Burnout? Biases that blinded us to reality? Overconfidence in the clarity of our solution? Or perhaps something more subtle, like the wrong narrative at the wrong time. It is a humbling reminder that sometimes even when the mechanics are sound, the momentum never materializes.
I used to revisit those questions from time to time, almost like picking at an old scar. What were our blind spots? Where did we underestimate the complexity of scale? What were we actually doing well without realizing it? These are not the sort of questions you can easily resolve with hindsight, because the people you want to ask are no longer around, and the context in which the venture lived has shifted. The moment is gone, but the questions stay.
This is where something remarkable has happened recently. I began using Perplexity and ChatGPT, both Pro versions, not as search engines and not as shortcuts, but as conversation partners. I gave them the persona of seasoned mentors, the kind of veterans with scars and wisdom from building and exiting companies in the loyalty and hospitality space. I fed them scenarios, decisions, even my doubts. I asked them to help me sense make, to walk with me through the choices we made with the information we had at the time. By running the same questions through both, I often find myself triangulating insights, comparing angles, and arriving at a more nuanced understanding. That triage of perspective has been invaluable.
What followed was not a single revelation but a series of clarities. The tools acted as mirrors, not telling me what I should have done but helping me see why certain decisions played out the way they did. They pointed out patterns, surfaced assumptions I had forgotten, and reminded me that even our failures contained moments of brilliance we never recognized. It felt less like a conversation with a machine and more like reliving a rigorous case study from business school. In fact, it has been like doing an MBA course all over again, except this time I am the protagonist of the case, with deep context and a far richer appreciation of the operational decisions that shaped the outcome.
The most powerful part of this experiment is not in the answers themselves but in the process of questioning. I am learning not only about what went wrong, but also about what went right. I am discovering that we were ahead in ways we did not appreciate, and behind in ways we should have seen. That paradox is the essence of most ventures that never fully make it, and to sit with it now, years later, is strangely liberating.
What excites me most is how this experiment is flowing into my present. The learnings from these conversations are not staying in the realm of reflection. I am carrying them into my work at the Ottawa Community Foundation, into projects I am building, and into conversations with those I mentor. In many ways, we are all trying to make sense of AI together, and this specific use case has given me a powerful way to model both learning and leadership in real time.
What strikes me is how different this use of AI feels. We often think of AI as a tool for efficiency, productivity, or content generation. But its most transformative potential may be in how it helps us revisit our own past with fresh perspective. The ability to take an old failure, feed it into a structured dialogue, and emerge with renewed clarity is an extraordinary form of learning. It transforms regret into insight, and self-doubt into capability.
This is not about rewriting history. The past remains what it is. AirLoop did not scale, and that fact will never change. But the meaning we extract from that experience can change, and that meaning is what shapes us as leaders and decision makers. If sense making is the cornerstone of leadership, then perhaps these tools give us a new language for it.
I find myself thinking that everyone has their own version of an AirLoop, some unanswered question that lives rent free in the back of the mind. The difference now is that we have tools that allow us to revisit those questions in a way that is generative rather than paralyzing. Tools that allow us to turn private frustrations into personal growth.
So here is my reflection and my invitation. Do not let old failures sit quietly in the corners of your mind. Revisit them. Question them. Model them with the tools we now have. You may not find the neat closure you imagine, but you will find clarity. And clarity, more often than not, is enough to move forward with a lighter step.
What is an old question you are still carrying?