Forward Deployed Learner: Enabling 22-year-olds to build domain expertise and judgement in the post AI world

The emergence of better AI has transformed the conversation about the future of work, as human judgment and domain expertise become increasingly valuable. However, the traditional path of gaining such expertise through work is changing. PwC's 2026 Global AI Jobs Barometer indicates that entry-level roles in the US, exposed to AI, are seven times more likely to require skills traditionally associated with senior employees, such as leadership and strategic thinking.

A student learning about a supply-chain issue, such as a beverage company running out of a particular product, could previously only rely on textbooks, case studies, and internship access. Today, they can use AI to understand the complex interactions between demand planning, procurement, lead times, promotions, and inventory, analyze data, and even build a simple forecasting model. While AI can provide a plausible answer, it is not the same as the correct one.

AI does not grant students domain expertise; it merely enables them to enter a professional conversation faster. The value lies in helping students recognize their assumptions, learn from experienced operators, and build a portfolio of problems they have solved with real teams. This approach suggests a new way of thinking about education and early-career development. A Forward Deployed Learner would engage with real business problems sooner rather than waiting until graduation.

Companies could adopt this model by publishing real problems, rather than just job descriptions, to attract talent. These could include opportunities to improve inventory allocation at a retail store, optimize routing for a logistics company, or reduce administrative processing time in healthcare without adding work for clinicians.

Universities can then collaborate with these companies to match relevant problems with courses and students, providing faculty support, creating multidisciplinary teams, and having students explain their process, challenges, and learning outcomes.

Startups are particularly well-suited for this model due to their abundance of problems and lower bureaucracy. A student working with a startup can observe product, sales, customer support, and operations, gaining a holistic understanding of decision-making consequences across functions and building relationships with founders and operators.

This approach does not require extensive internship programs; even a single problem, student team, and an experienced mentor willing to provide guidance can create a valuable learning experience.

Repeated exposure to real-world constraints and challenges outside the classroom teaches students to question the analytical soundness of answers and the importance of testing assumptions and verifying reality. Therefore, in addition to learning how to use AI effectively, students must also learn how to use AI responsibly while being accountable for solving real problems.