Entrepreneurship through Acquisition with AI: Leveraging Machine Learning for Trident Succession Search Fund

This case is among the first to incorporate machine learning and data science into the search process of a search fund. A search fund is an entrepreneurial vehicle in which searchers raise capital from investors to finance the identification, acquisition, and operation of a small- to medium-sized business. The case follows Andrew Collins and Bram Jansen, two INSEAD MBA graduates who have two years to identify a company to acquire in the Netherlands—representing one of the first partnered search funds focused exclusively on this market.
Given the time- and labor-intensive nature of the search process, the duo seeks to automate their strategy by leveraging machine learning and predictive modeling. Specifically, they aim to estimate a company’s EBITDA when it is not publicly available, with the goal of efficiently identifying acquisition targets that fall within an appropriate EBITDA range.
The case serves as an effective learning exercise for participants studying entrepreneurship, as well as data science, machine learning, and AI for business. It is suitable for MBA, Executive MBA, Master in Management (MiM), specialized master’s, and executive education programs. The case provides a rich setting for discussion and hands-on problem solving in the context of entrepreneurship through acquisition, while also demonstrating the practical application of machine learning and AI in a real business environment.

1. Understand the search fund model and the proprietary search process.
2. Identify data- and AI-driven opportunities to improve the efficiency of the search fund process.
3. Understand how supervised machine learning methods and available data can support business-relevant predictions and decision-making.
4. Apply machine learning techniques for predictive modeling in a concrete business context.
5. Learn how to train, validate, and test predictive models, particularly when training on data from one region and deploying the model in another.
6. Gain practical experience implementing regression and classification models using code and generative AI tools.