Where the idea came from
The idea for Street Turn originated from our team member Dario, who is already working with a company on a project associated with the validated problem of inefficiency in container logistics. When brainstorming, two directions were identified as relevant for us: education or logistics. As Dario already knew the validity of the problem in container logistics, we found this a valuable problem space to build on further. In the traditional supply chain, there is unnecessary back-and-forth hauling that creates extra empty-truck miles, port congestion, operational costs and avoidable carbon emissions. Thus, this presented a genuine problem-opportunity space.
My role and own contribution
My role on the team was driving the problem and opportunity framing space, keeping the team deadlines in check, and taking responsibility for defining the problem space, value proposition, and overall business model. Coming from a background in Industrial Design Engineering and having previous experience taking projects from zero to MVP, I understood how crucial it was for desirability to be maximized early on by defining the problem boundary. I also understood how relevance for different stakeholders across micro, macro, and meso levels is critical for the success of subsequent phases. Furthermore, I drove the design of our business model, making sure it was profitable for the primary user, the freight forwarders, as well as secondary stakeholders like freight companies. I also ensured it was beneficial in the grand scheme by factoring in corporate sustainability compliance, specifically helping freight forwarders diminish their carbon emissions so they can reach their targets under CSRD compliance. Therefore, I ensured that value capture, creation, and delivery were prevalent on all levels.
Key decisions and iterations
A pivotal early decision was selecting and validating our specific problem space. While education and reading anxiety offered an interesting market with a heavy focus on the human aspect, our discussions and sparring sessions made it evident that the logistics problem was already validated. This presented an obvious opportunity to build a technology-backed solution that we could quantify effectively. This led to sessions where we mapped out the wider options within the logistical problem-opportunity space.
Iteration was also evident in how Street Turn would maintain its competitive advantage and to what level artificial intelligence (AI) would be prevalent. It developed from a simple route optimization engine into a business model that embedded regulatory compliance and carbon offset tracking directly into freight cost savings, turning environmental compliance into a direct financial benefit.
What surprised me and what failed
What was most surprising was how straightforward the technical problem space became once we mapped out the operational frictions in container trucks. AI fits naturally as an intelligent layer to automate the matching logic and streamline the communication between dispatchers.
However, the biggest surprise stemmed from my own personal study background. In this project, I was surprised to see how decisions were driven more heavily by business metrics, return on investment (ROI), labor costs, and operational efficiency. I realized that while our project originally stemmed from a desire to operate more efficiently, ensuring that companies actually adopted the solution was of utmost importance, and adoption is driven by bottom-line profitability, cost reduction, and increased efficiency. Learning to innovate within these realistic business constraints was an eye-opening shift in perspective. What failed, however, was user testing; while getting feedback is crucial, live user testing remains a future phase to be stepped into beyond the demo MVP stage.
How feedback changed the work
Throughout the project, real world data quickly stripped away subjective assumptions. This enabled us to lock in our core concept early. For instance, when evaluating our opportunity scope, the team looked at hard data such as Eurostat statistics showing that roughly 21% to 25% of road freight kilometres are driven empty (Eurostat, 2025). This kept the project grounded in reality rather than wishful thinking. The team was very much in synergy when it came to approaching the project throught he lens of strict operational filters and data which allowed the focus on the business model, pricing structure and go-to-market strategy.
What I learned
What I mostly learned is how to view bringing something from zero to one within the bounds of a business context. Beforehand, I had always placed user empathy and human-centered design at the absolute center of product development. Through this project, I learned to balance user desirability with business viability. I discovered how strategic decisions in an organizational context rely on system efficiency, the reduction of friction, and the necessity of ensuring that a solution integrates seamlessly into existing business operations and multi-party ecosystems.
How my understanding of AI and strategy changed
Until now, my understanding of AI was mainly focused on machine learning mechanics, neural networks, and how algorithms could be built into physical products. I had not yet been exposed to how AI could fit into corporate strategy. Through this project, however, I can now see how it acts as an enabling layer that provides value to organizations by lowering the cost of processing information, searching for data, and coordinating between different parties. In our project, AI acts as an ingestion tool.
At the same time, deploying AI also comes with integration challenges and trade-offs. I have learned how to analyze where AI creates genuine leverage rather than forcing its full implementation everywhere. In Street Turn, this is demonstrated by how AI is used strictly as an ingestion tool to read messy emails and PDFs and structure that data so a deterministic constraint solver can test it, while the final decision-maker remains the human dispatcher.
References
Eurostat. (2026). Road freight transport by journey characteristics. Statistics Explained. Retrieved October 7, 2026, from https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Road_freight_transport_by_journey_characteristics
