Monk will host Net 30, an applied AI summit on finance, on October 21 at The Times Center in New York for 150 CFOs, operators, founders and researchers. The central question is how AI changes cash, capital and the finance function in measurable terms. The premise matters because a Gartner survey of 183 CFOs found broad adoption but little return: 84% of finance organizations had deployed AI or planned to, while only 7% reported high impact.
Evening format and agenda in New York
The event runs from 5 to 8 p. m. at 242 West 41st Street, with doors opening at 5 p. m. and the program starting at 5:30 p. m. Attendance is complimentary and by invitation only, positioned as a curated evening for people responsible for how capital moves through companies. Dominic-Madori Davis of TechCrunch will moderate a panel, with additional speakers to be announced. The format signals a working discussion rather than a product showcase.
The name Net 30 refers to the most common payment term in business, the 30-day window where cash is won or lost. Monk frames those 30 days as the place where AI now produces measurable outcomes, not just faster work. CEO George Kurdin said leading teams use agents to cut costs, increase cash flow or drive revenue, and that AI in finance should be judged by financial outcomes. In this logic, operational efficiency alone no longer counts as success.
The program covers running finance at scale with a lean team, a theme directly relevant to companies that cannot expand headcount. Another topic is what separates agentic pilots that reach production from those that stall, which addresses the gap between the 84% adoption figure and the 7% high-impact figure. The agenda also includes how law firms are rethinking business models and what happens when AI agents operate in markets without guardrails. The evening closes with designing the finance organization of 2030 and where human judgment matters most if agents perform much of the work.
What this means for finance teams
For companies adopting AI, the summit points to a shift in evaluation criteria from hours saved to cash on hand, lower costs and revenue. Finance leaders can apply the same test to current pilots in invoicing, collections, forecasting and procurement support. A small company can use it to decide whether a lean team plus agents covers month-end close and payment discipline. A large organization can use it to compare results across business units instead of counting deployed tools.
The limits of this discussion need attention before drawing purchasing conclusions. The published details name the venue, date, audience size and moderator, but not the full speaker list or case studies. The Gartner figures describe adoption and self-reported impact, not which use cases produced the impact. Buyers should ask vendors which financial metric changed, over what period, and under what controls for data access and approvals.
The marker to watch is whether Net 30 produces documented cases where agents moved payment terms, collections or costs rather than task speed. Publication of such examples after October 21 would confirm that finance AI is moving from productivity projects to balance-sheet results. Absence of that evidence would leave the 84% versus 7% gap as the main fact for budgets.
