MIT Research Fellow Highlights AI Prompt-A-Thons Boosting Decision Culture

Michael Schrage, a research fellow at MIT Sloan’s Initiative on the Digital Economy, has shared key insights from over 100 AI prompt-a-thons he has led since 2023. These sessions, which are structured, sprint-based events, focus on developing prompts for large language models (LLMs) like ChatGPT and Gemini. Schrage emphasizes that prompt-a-thons are not just workshops but mirrors that reflect how people think, what they value, and what they overlook.

During these 60–90-minute sprints, small cross-functional teams design, test, and iterate prompts to improve key performance indicators (KPIs), clarify workflows, and challenge assumptions. Schrage notes that most participants discover their initial thinking is often flawed, shallow, or stuck in spreadsheet autopilot. The prompt-a-thon process reframes prompting as a high-impact diagnostic and design discipline, engineered for fast, actionable insight. “It’s not just about using AI more effectively—it’s about thinking and collaborating more intelligently with it,” he said.

For finance leaders, Schrage suggests a different approach to upskilling people on AI. Instead of focusing on AI tools, he advises prompting cost centers and forecasting failures until something breaks and gets better. Financial planning and analysis (FP&A) is highlighted as a particularly powerful starting point. Prompt-a-thons in this area often surface hidden data, unchallenged assumptions, and areas of organizational ambiguity or resistance. “Prompt-a-thons aren’t about rainbows and unicorns,” he added. “But every so often, one shows up—usually disguised as a counterintuitive insight,” he said.

Schrage explains the importance of small teams or collaborative prompting. “A prompt is a hypothesis about how the world works—and the world pushes back,” he explained. “Solo prompting explores. But team prompting evolves and that’s where real learning happens.” He compares the approach to sports analytics: “You’re not just trying to win once. You’re trying to build the kind of team that keeps winning.”

From his experience, Schrage offers three recurring lessons learned from finance-driven prompt-a-thons. First, prompts are scaffolds, not shortcuts. Great prompts don’t replace critical thinking—they sharpen and amplify it. One-shot prompts are useful; iterative ones are transformative. Second, avoid trying to automate what you don’t understand. The danger isn’t that LLMs get things wrong—it’s that they confidently reinforce flawed assumptions baked into broken processes. Third, look beyond cost-cutting. Most finance prompts chase efficiencies. The best ones expose strategic blind spots and generate new hypotheses worth testing.

Schrage’s key takeaway is that the quality of a team’s prompts reveals the quality of its decision culture. “People don’t just learn how to prompt; they learn what their organization won’t let them ask,” he said. “That’s when everything changes.” Prompt-a-thons expose what firms know, want to know, and avoid, he said. By closing these gaps, teams not only boost AI fluency—they get better at asking and answering the questions that matter, Schrage said.