This course explores how organizations and individuals can strategically harness AI for sustainable competitive advantage, moving beyond adoption to transformation. Through an iterative approach combining theory with hands-on experimentation and prototyping, students actively build, reconfigure, and test prototypes and workflows to measure impact and extract learnings. The course addresses both predictive and generative AI across three core dimensions: competitive strategy reformation, organizational transformation, and human-AI collaboration.

A central principle of the course is problem-first thinking. Students learn to identify and validate genuine challenges and opportunities before considering technological solutions. Rather than searching for places to apply AI, participants develop skills in recognizing real needs, testing assumptions, and then strategically evaluating when and how different AI technologies can drive meaningful innovation and impact. Through rapid experimentation cycles, students create working solutions, test hypotheses, and iterate based on evidence, actually building things to understand both possibilities and limitations while addressing critical challenges around governance and human factors.

Team-based case work explores organizational AI implementation, while individual projects address personal dimensions including how to leverage AI for professional development and navigate the evolving landscape of work and skills. No technical background required. The focus is on strategic judgment and leadership capabilities for driving AI initiatives. The course blends structured frameworks with experiential learning and rapid iteration, enabling students to develop informed perspectives on AI's transformative potential. Active engagement expected both in sessions and through collaborative work.

Faculty: Sebastian Krakowski