Fall 2026 application instructions:

• Apply here: https://reg.kellogg.northwestern.edu/experientiallearning/‍ ‍

• Deadline: For Fall 2026, the application deadline is July 27, by end of day.

• If you are applying as a team, EACH team member must complete an individual application by the deadline.

Likely clients / projects for Fall 2026‍:

• NBA team (name withheld for now): generating recommendations on how to improve revenue outcomes. Working directly with senior executive team.

• Perfect Game: leading baseball amateur event and scouting organization.

• 3M: using data science and AI methods to improve production outcomes.

• MLB team (name withheld for now): generating a framework to assess AI opportunities for business improvement.

• WNBA team (name withheld for now): generating a framework to assess AI opportunities for marketing, revenue, and fan experience improvement.

• Northwestern Athletics: helping build an analytical framework to help allocate resources to optimize winning. Very relevant given the changing business of college sports.

• National specialty retailer (name withheld for now): a $1B company with a unique in-home sales model and rich transaction data. Students will identify revenue opportunities across pricing, customer targeting, and sales performance

How ACL works:

• If you are admitted to ACL, I will do my best to assign you to a project that you would really like. However, especially with sports clients, demand is often higher than supply. I recommend that you apply to ACL only if multiple projects sound interesting to you.

• Please note: “Analytical and AI consulting” isn’t just about statistical analysis and ML model-building around large datasets. Many companies already have data science teams for that. What they often lack, but truly value, are business-minded experts who can help them think differently about how to use data and AI. In some of the below projects, the real value (and often, the real fun) lies in uncovering new ways to solve business problems using existing data: reframing challenges, surfacing hidden opportunities, and shaping innovative use cases.

• Most ACL projects fall into one of the following categories:

  • Empirical analysis of a data set to generate recommendations - sometimes these projects might require building ML models and sophisticated model diagnostics, while others might involve simpler “insight generation.”

  • Helping clients map out how data can be useful to a particular goal, like “personalization for revenue generation.” These projects are a bit more about creating a strategy to use data to achieve a business goal, rather than conducting primary empirical work.

  • Helping clients map out how generative AI tools / LLMs can be used to achieve specific business goals. Given the early stage of industry’s use of gen AI, these projects tend to be more open-ended and require significant self-direction by the student team.