As the final integrative project for Marketing Management, Operations Management, and Resource Planning & Control, our Learning Group—4square (Group 16)—was randomly assigned the emerging investment space of AI-powered drug discovery. The challenge: advise a hypothetical investor on which venture within this complex and highly technical space held the most strategic value. With no prior experience in healthcare or pharma, we navigated a steep learning curve to develop an investor-ready pitch, business model analysis, and operational recommendations grounded in market data, VC trends, and AI innovation.
MSEL LINK Project | AI in Drug Discovery
Babson College | Fall 2025
As the team’s coordinator, I helped build the foundation for our collaborative success. Early on, we completed a DISC team assessment, where I was identified as a high C (Compliance) and S (Steadiness). These strengths translated into managing our timelines, facilitating communication, and ensuring we remained aligned across courses and deliverables. I scheduled meetings, booked study spaces, took detailed notes, and—most importantly—created space for every team member to contribute meaningfully. Our group dynamic was a core part of our success: we prioritized accountability, mutual respect, and leveraged each other’s strengths at every turn.
Our teamwork shined during the Global Supply Chain Simulation, where we competed against all Babson MSEL learning groups and achieved the highest gross margin. We adopted a dual-supplier strategy that balanced cost-efficiency with flexibility, especially around Model B’s unpredictable demand. I played a key role in maintaining structure during high-pressure decision cycles—ensuring debates were resolved through consensus and, when needed, democratic votes. Our ability to adapt quickly during Year 4, based on shifting demand conditions, helped us outperform the competition and validate our approach under real-time market constraints.
For our final investor presentation, we recommended Aitia as our primary disruptor, citing its Gemini Digital Twin platform as a transformative force in accelerating drug discovery. We benchmarked Aitia’s value proposition against incumbent Pfizer, identifying key vulnerabilities in Pfizer’s R&D model and proposing how it might strategically respond to Aitia’s rise. Our pitch detailed Aitia’s early proof points, founder expertise, and potential exit paths, helping our fictional investor visualize both risk and reward. The project pushed us to operate like true strategic advisors—making data-driven recommendations in a space where traditional performance indicators were still emerging.
Final Report