Team 22 – Enerwise
Introduction
Enerwise is an AI-powered platform that helps people decide when it is worthwhile to switch energy suppliers. Not only comparing prices, our project also looks at a person’s energy contract and usage to assess whether switching would actually save them money. Having experienced the difficulties of comparing energy contracts ourselves in the Netherlands, my team and I decided to work on a project that would help people save money on energy without having to spend too much time doing all the comparisons and calculations themselves.
The main tasks I worked on throughout the project included the business model and ecosystem analysis, as well as adding contributions to the sources of competitive advantage, future market analysis, and assumptions. Thanks to these tasks, I was able to inform myself a lot on developing an AI-based business and learnt that it involves a lot of trial and error, critical thinking, and as well as root cause analysis. By which I mean understanding customer needs, how to generate revenue, and finding ways to obtain competitive advantage in an existing market.
My Contributions
The first responsibility I was met with was creating the business model, particularly how the platform would create, deliver, and capture value. This was a key component of our project because it required me to think about how the system could genuinely help consumers while also allowing us to be financially sustainable. Coming up with a general idea versus actually executing it into a realistic business proved to be more of a challenge than expected.
Initially, we drafted different methods of earning revenue, such as monthly subscriptions, supplier commissions, and a token based payment system. All of these had their own pros and cons, so we had to decide how to generate revenue while also obtaining the trust of our customers. I realized that although we could have all the right intentions, customers must see enough value in our service to be willing to pay for it. Another part of the project I worked on was the ecosystem and competitive analysis. This involved considering the different stakeholders, including households, energy suppliers, technology providers, and comparison platforms. The biggest challenges we faced was identifying how Enerwise could gain a competitive advantage over existing competitors. We had to make sure our project offered something unique, otherwise there would be little reason for customers to choose our service.
Our first thought of utilizing AI to provide personalized recommendations seemed like a strong advantage. However, while working on this section, I realized that such a feature could easily be replicated by competitors. Therefore, we focused more on what could make Enerwise valuable in the long run, such as reliable data, customer trust, and continuous contract monitoring. Applying concepts such as multihoming and platform envelopment also helped me understand that customers could use several comparison services at once, while existing platforms could easily introduce similar features. This showed me why Enerwise needed to offer something beside traditional price comparison.
Decisions Throughout the Project
Something that continuously came to mind throughout the project was how often we had to rethink different parts of our ideas and make sure they worked together as a realistic business concept. When we improved one part of the business model, that also could have potentially created problems elsewhere. For example, earning commissions from energy suppliers could help generate revenue, but it could also raise concerns about whether Enerwise’s recommendations were truly independent. Through this, I learned that business decisions often involve trade offs and opportunity costs rather than one clear and simple solution.
Working on the future-market analysis also made me consider a future where customers, energy suppliers, and competitors all use AI agents. If Enerwise offers something unique today, competitors could develop similar services in the future. I learned that business strategies should not only focus on the current market but also consider how technological developments could change competition over time.
Another important learning point came from the evidence we gathered. Our simulation with 25 households showed that Enerwise could avoid recommending switches that would result in financial losses. However, feedback from six people also showed that not everyone would feel comfortable allowing AI to automatically switch their energy contracts. This means that even if a system works technically, it does not necessarily mean customers will trust it or be willing to pay for it. It helped me understand why testing our assumptions is important before developing a business further.
Teamwork
Overall, I had a very positive experience working with my team. We divided the different tasks amongst ourselves, which made it easier for everyone to focus on different parts of the project. At the same time, we all brought our own ideas to the table and frequently kept in contact to discuss how to improve our project as a whole. Our communication also worked well, although for myself I do not live in Rotterdam, it was challenging for us to meet up as often as we would have liked due to the distance and other constraints. To manage this, we held online meetings whenever meeting in person was not possible, which worked quite well.
The main challenge for me was time management, as we had to balance this project with other assignments while also studying to keep up with our courses. The deadlines appeared to approach sooner than expected, and at some moments my team and I felt the pressure, but we were able to organize ourselves and our work to eventually bring everything together.
During the demo day, we also had to pitch our project to other groups, and I thought that went very well. We explained our project clearly and were able to answer questions and doubts other students may have had. This project taught me that good teamwork is not only about dividing tasks, but also about sharing ideas, communicating regularly, and making sure everyone is up to speed and working towards our shared goal.
Conclusion
Overall, I really enjoyed this project and will take away several important lessons from the experience. It helped me better understand how AI and businesses can work together, especially after hearing about the creative ideas other groups presented during the demo day. I now understand that AI can change how a business operates, generates revenue, and competes with other companies when used effectively. I also developed a better understanding of business models, competitive strategies, and the challenges that come with introducing AI based services. Most importantly, I learned to look beyond what AI can do technically and consider how it can be used to create meaningful and sustainable value for both customers and the business.
