The AI Strategy Lab changed my perspective on the relationship between AI and business strategy. I knew that AI is and will become a huge part of many solutions to modern-day problems. But after this project I became even more aware of how easily AI can be implemented and help with almost all daily routines, struggles, and problems. However, it also made me more critical of whether an attractive solution can sustain its value with AI becoming more and more easy to use and accessible. Our project explored AI-assisted planning for customized football trips at Voetbaltrips.com. The idea came from our shared interest in football, which made it enjoyable to work on. But this also could have been the reason why we didn’t question the problem enough at the beginning of the project.
I mainly focused on the defensibility, agentic future, and critical evaluation. I thought about how our proposal could retain value when customers and competitors could also get access to the same AI tools. So I had to come up with reasons why a customer would choose Voetbaltrips rather than doing it themselves using their own general AI assistant. Working on this made me realize that initially improving a service and building a lasting competitive advantage are two different challenges.
So the main lesson I learned was that access to AI is not enough to protect our idea, because a conversational planning interface can be copied relatively easily. But reliable access to tickets, supplier relationships, and strong support when the plans change could be reasons why customers would choose to stay with Voetbaltrips.com. However, I treated these assets as potential advantages as they still require evidence, which we couldn’t generate. But nonetheless, the project made me more critical of what things aside from the main purpose of a project can generate an advantage and value.
The future scenario also challenged my initial optimistic approach. When customers can use their own agents to compare packages, they will probably be less likely to pay a premium for planning. And competing agencies can also generate quotations more quickly. So AI could reduce our operating costs but also weaken the value of our offer because others will be able to do certain parts themselves. So analyzing this made me realize that it is indeed easy to come up with a great solution to a problem, but at the same time this technology changes the behavior of everyone in the market. So next time I would ask myself earlier on in the project if the proposed advantages can survive when the competitors and customers gain access to the same technology.
What really surprised me was how easy and accessible prototyping has become due to AI. Prior to this project I was always interested but had never really worked on developing a platform with the use of AI. Now that I have seen our team turn an idea into a working prototype, building an actual working digital solution seems more achievable than before. Of course our interface used fictional data and could not actually make any bookings. This would take a lot more additional work and partnerships to achieve. But nonetheless, the interface and the vision are there, and that makes me really excited for possibilities in the future.
When I look back at our project and compare it to solutions other teams came up with during Demo Day, I think our original problem may have been a bit too niche. Another team presented an AI layer for rebooking flights, making the process faster, which they came up with after an incident where there were widespread flight cancellations. I found the connection between their solution and the real-life problem really clear. This made me reconsider how we had chosen our own problem. Although I found our subject interesting, in hindsight I wish we had spent a little more time coming up with a recent and more relevant problem, which we could link to an AI-based solution. I think that would have made our project a little easier and less broad solution-wise.
A niche problem can of course still support a valuable business, but we didn’t have a lot of evidence about how many customers our solution would attract or what they would pay for it. Our platform also needs several connected services, which made it hard to estimate implementation costs and expected returns. So we mostly relied on broad assumptions for our economics. So looking back I would have preferred a problem with a clearer operational baseline. This would have allowed us to analyze the solution more deeply.
The collaboration on this project was one of the strongest aspects. Our team bond was great; we communicated clearly and could agree on deadlines. This really helped us in coordinating our work and communicating openly. My strategic section also made me clearly see how our separate responsibilities still required a shared understanding of the project. To judge the future of our project I needed a clear understanding of the prototype and the economic assumptions. Completing my part of the final project was only part of my responsibility. I also needed to check how their conclusions fitted the team’s overall argument.
Demo Day really broadened my understanding of the opportunities that AI brings with it beyond my own interests. All the other projects showed me solutions I would not have considered myself, in particular improvements to existing bottlenecks. For a future project, I would spend more time comparing problems before choosing one and already seek evidence about the demand and existing data. I would keep the team’s clear communications and deadlines, but bring the different analyses together sooner. I feel a lot more confident about exploring AI-based solutions, and more deliberate about asking what problem they solve, how the value can be tested and why the value would last.
