FieldFinch, a platform designed for academic experts that want to connect and elevate their academic career. Through FieldFinch, researchers can connect with experts outside their own network whom they would otherwise never have met. This not only strengthens their individual work, but also academic progress across universities. In addition, FieldFinch connects academics with the corporate world, so their research can be tested in real world applications.
At the start of this project, I knew that AI could have a big impact on businesses and strategic decision-making. It can transform the way we perceive and do things and that it offers a significant number of opportunities. However, I came to the realisation that AI is not the solution to every problem you might have. The real value within AI comes from knowing where it genuinely makes a difference, not following it blindly. And I believe this will be the power of future businesses.
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Within the project, the first task I had to tackle was the economics behind FieldFinch. To be completely honest, I did not really enjoy it. I had a difficult time deciding on what was needed to finance both the AI and human side of the platform. In my opinion, an AI-supported digital platform is quite complex. Without the right expertise about the infrastructure needed to support it, it becomes difficult to estimate the costs.
Evantually, I decided to base the AI costs on the use of an external AI model, with a paid per token process, instead of building and maintaining our own infrastructure. For the human costs, I combined sources like WR and Indeed and my own knowledge to determine what kind of staff was needed and what this would cost. Looking back, I realise that there is probably more than just the price per token when you want to scale your AI-supported digital platform. So, for future projects, a better understanding of the infrastructure and workflows behind AI-driven platforms is essential for making economic decisions.
Besides the economics, I also analysed how future-proof FieldFinch is in our rapidly changing world. This part of the project let me to an important conclusion: that the human side of doing business in our more and more AI driven future is fundamental. Keeping a human in the loop increases peoples trust, raises the standard of privacy, and creates a more personal experience than interacting with an AI model alone.
Of course, not everyone would agree with this point of view. People would argue that AI will be efficient and reliable enough to replace human involvement. However, especially on a platform that is focused on connecting people. A human touch is the element that creates value.
Looking back at the project, one of the most important decisions for me was made at the beginning. We had to narrow our scope down, rather than target the same broad audience as LinkedIn. This decision started with our professor, Ting Li, who pointed out that without a narrower focus, the assignment would become too complex. Following this advice, we still had to decide which target audience to focus on.
At first, I wanted to create a platform for students and graduates, this to help them kickstart their careers. Because lowering the barrier for taking that first step would really help a lot of people in my opinion. However, after discussing and researching the needs and barriers of multiple groups. We concluded that young academics had the greatest need, they often do not have the network to find the right expertise outside their own circle. Although at first, I was disappointed that we did not choose students and graduates. I am now glad that we focused on a group we are not as familiar with. Because we could not rely on our own experience, we had to base our choices on the research we did rather than our own assumptions, which made our project stronger.
The use of AI throughout the project was a real surprise to me. Last year, I took a gap year in which I barely touched my laptop. Seeing how much to use of AI has changed in just one year amazed me. It is not that tools like Claude and ChatGPT were doing all the work. It was more like students and AI models had started working together as partners.
This also forced me to make an important decision of my own: how could I use these models to improve my work and keep on challenging and developing myself? In the end, I decided to create a teaching assistant. I first produced my own work and then asked the assistant for feedback on the parts that needed improvement. With that feedback I critically analysed what could be improved. If I agreed with the feedback, then I would refine the work without the help of AI and run the same process again. Within our team we applied the same principle. For example, we have used Claude to refine our wording, to design the cover slide and run the evaluation simulation.
Working together with AI again thought me a lot, but working with the team thought me something different. We often came together to work on the project. During these meetings, we would divide work among the four of us. The goal was to have everything finished by the end of the day. Most of the time we did succeed, however it always took longer than necessary because of all the chatting and banter between us. Although, we had fun, and we could easily share ideas and ask each other for help. In future projects I think we should work separately for a couple of hours to really focus on our individual tasks. And then come together to align our work and discuss the progress we have made that day.
All these experiences have changed the way I look at AI and strategy. Artificial Intelligence is everywhere nowadays, and anyone who admits to being hesitant about using it is seen as quickly falling behind. Personally, I think there are two sides to the story. It is important to keep up with the stream, because people who use AI efficiently will be more productive than those who do not. On the other hand, it is important to keep on pushing your own personal development, instead of letting AI do all the work.
The same applies to businesses and their strategies. If you let AI tools decide what your goals, strategies and key decisions should look like then your business will become generic and won’t succeed to compete within markets. FieldFinch is a great example, the AI model we used is available to anyone. So, our advantage must come from trust, privacy and the data we would create over time and not the technology itself. This project confirms what I was starting to realize at the beginning:
AI is not everything. Real value is created when you challenge yourself and your business, make you own decisions and stay true to your values, and with AI as the boost, not the driver.
