The Cornell AI Innovation Hub recounts (June 15, 2026) how a two-semester collaboration made it possible to recover $100,000 in unidentified payments using AI. The problem: every year, Cornell receives hundreds of wire transfers and ACH payments without enough information to route them (no invoice number, vague vendor name). The funds accumulate in a suspense account — active backlog ~$1M, historical peak $4M — and New York State law mandates escheatment if they are not resolved in time. Two treasury staff members were spending up to half a day a day on this.
The project's structure illustrates Ethan Mollick's Leader-Lab-Crowd framework. The Lab is the AI Hub (Pete Stergion and Phil Williammee, co-tech leads, plus a cohort of students). The Crowd is Treasury (Cheryl Barnes, Marie Graves, Kevin Mooney, Debra Federation), holder of the business knowledge and the data — Kevin provides 3 years of Oracle GL history (10,000+ records). The student analysis surfaces the key insight: 99% of payments carry a vendor name, versus less than 4% an invoice number.
The build follows a "context first, then plan, then build" discipline: via Claude Code Plan Mode, the team loads all the context (notes, manual process, prototypes, sanitized data); Claude Code proposes an architecture to validate before writing any code. A semester of notes becomes a working tool in a single session. The Python pipeline (exposed as a skill /treasury) chains three steps: fuzzy matching against the GL (filtering out noise words like Inc/LLC/Corp), vendor lookup via Gemini Enterprise Web Search, then Claude synthesis producing, for each payment, a likely department, a confidence level, and a contact. Output: an Excel file sorted by confidence, in a few minutes — all within the governed Cornell AI Gateway (PII stripped, no external model training).
The backtest (9,131 resolved payments) shows 97% → 100% accuracy for recurring vendors with the full AI chain, and 76% → 100% for unknown vendors. Documented limitation: vendors billing multiple departments. Operational result: 23 departments contacted, 7 responses, 5 payments = $100,000 confirmed.
Beyond the figure, the case is a counter-example to the narrative that "AI doesn't create business value": it does here, because a Lab, an expert Crowd, and real groundwork came together. And the $100,000 plays the role of the visible reward Mollick prizes — the tangible proof that legitimizes and spreads adoption, by removing the drudgery rather than the jobs. "The $100,000 is a start."