For the Information Strategy course, we, Project Group 19, decided to tackle a relevant and frequently occurring problem while travelling by plane: disruptions (e.g., cancelations and delays). Currently, at many airlines, rebooking and compensation are managed manually at on-airport service desks, which cause long queues and high workload during these disruptions. Our team proposed to solve this issue by supporting decision-makers with agentic AI-based recommendations and improving communication with passengers using an extension in the existing airline mobile app.
While brainstorming business cases, I proposed this idea because I experienced it a few years ago while travelling back home, which caused me to arrive two days late. During this trip, I was sent to multiple desks at the airport, and it took hours to get my flight rebooked and receive compensation. Therefore, I thought this would be a great problem to solve using AI. While pitching it to my teammates, other, more recent experiences came up for discussion as well, indicating this problem is still relevant.
Solution
Initially, when pitching the problem, an AI solution had enough potential to create value for the airlines, however, the exact direction of our solution was not clear yet. The guest lecture on agentic AI provided us with new insights, which allowed us to establish a clearer understanding of how we wanted to design the new process and prototype. We decided to implement agentic AI for recommendations, but most importantly, not fully automate the process, but keep the human-in-the-loop, who makes the final decisions.
Personally, I believe the new information we gained during the lectures, our criticality, and collaboration allowed us to form a better idea early on, which helped us over the course of the project.
Collaboration
Overall, our project group had diverse backgrounds, which is very useful when working on a project that requires many different perspectives. Of course, everyone tried their best to understand all parts and support each other where necessary, but the previously gained knowledge on certain parts helped with us to divide the work, do what we feel comfortable with and express our ideas better.
Besides that, I think our project group was very open to meetings, made sure we were on schedule using internal deadlines, and communicated well overall. Though some teammates had complications in their schedule, I believe we were all flexible and did our best to manage our time well and, in the end, submit a concise, well-designed project.
Feedback
I think feedback from both within and outside the project group was very important during the project. During my bachelor’s, and even now, I struggle a lot with asking for help or clarification, because I believe I need to do everything to come to a solution myself, which is not true, of course. Often the best ideas stem from brainstorming and taking in others’ perspectives to improve the idea, which indeed helped us during this project as well.
Within the project group, weekly meetings and discussions after lectures or in text helped us form our problem and solution initially. Afterwards, when finishing up the project, everyone made an effort to give constructive feedback and was open to taking it and improving the final deliverable. This shows that iterations and feedback are key for improving the project.
From outside the project group, the teaching staff was very open to further explaining their feedback and parts of the project that we struggled with. For instance, our product has no clear pricing model or network effects, so we did not know how to proceed with that, but after some clarifications, we were on the same page and could continue. I believe this communication, not only within the project group but also outside, is so important to understand what is necessary and deliver a complete project.
Contribution
As mentioned before, as a project group, we initially decided to divide the work based on our academic backgrounds. Personally, I have a more technical bachelor’s degree, which meant I was more comfortable with the practical implementation of the solution and the more general parts, such as problem definition, business models, and the final recommendations. When discussing, everyone laid out their preferences, and I ended up making the business model and prototype parts. For the business model, we used the Business Model Canvas, which I had used before and is relevant to the course and our project. For the prototype, we decided to use Figma and made separate prototypes for the staff member and the customer. Though I had used Figma before, it was a while ago, so I had to get used to the new additions to the program, but in the end, I am proud of my team for providing me with useful feedback and of our final product.
An Improved Understanding
During my bachelor’s, I had many courses and projects on AI, so from a more technical perspective, I am very familiar with the contents. However, I think this course showed me that there is so much more to think about when looking at AI. I always saw AI as a tool, a technology that can be used to improve processes, but this course takes AI from a different approach, namely, seeing AI more as a product or a service to base a business on.
Furthermore, I had also done a course on business development before, a couple of years ago, but seeing this from a more strategic perspective has brought me new insights. I now know that there are multiple different digital markets and platforms, the importance of network effects, and the more economical perspective of these businesses. Also, to me, it is interesting to see how, in a few years, you can see the change in technology already, which makes it exciting to see what is coming in the next years.
Lastly, though I believe this course shows the importance of AI within our current economy well, I believe it is important to stay critical and be able to look through the AI hype. Of course, AI is an amazing tool, but there are use cases in which AI is not beneficial (e.g., economically, ethically, and environmentally). Coming from a bachelor’s program where ethics, regulations, and the environment were as important as the technical implementation of AI, it is surprising to me how AI is framed as mostly good in this course. What I take from this is that we need to prioritize critical thinking and responsibility when working with AI as part of a business.
