Use Cases
How AI Agents Help CFOs Uncover Hidden Cash: A Six‑Month Pilot at a Mid‑Size Manufacturer
Liam Quinn
@use-case-lab·October 1, 2026
2 min read0 views
When the chief financial officer of Alpine Tools, a mid‑size precision‑machining firm in the Midwest, read McKinsey’s "The cash hiding in plain sight" report, she saw a roadmap but no hands‑on solution. In March 2024 she launched a six‑month pilot that paired three AI agents with the finance team to target the report’s four cash‑unlocking levers: working‑capital optimization, expense rationalization, tax‑efficiency checks, and asset‑utilization reviews.
The pilot began with Agent W (Working‑Capital), a large‑language‑model (LLM) built on OpenAI’s GPT‑4o, integrated with the company’s ERP. Agent W scanned 18,000 purchase‑order records, flagging 342 invoices that exceeded standard payment terms by more than 30 days. Automated outreach, guided by Agent W’s suggested language, reduced average days payable outstanding from 45 to 31, freeing $4.2 million in cash.
Agent E (Expense) tackled discretionary spend. Over 12 weeks it parsed 9,800 expense‑report entries, identifying $1.8 million in duplicated software licences and $750,000 in under‑utilized travel allowances. By renegotiating contracts and tightening policy, the firm saved $2.5 million.
Agent T (Tax) cross‑checked the firm’s 2023 filings against state‑level incentives. The agent uncovered a missed $1.1 million credit for energy‑efficient equipment, which the tax team reclaimed in August.
Finally, Agent A (Asset) used IoT sensor data to map machine idle time. The analysis revealed 12,000 hours of under‑used capacity, prompting a $2.1 million lease‑back of surplus equipment.
By the end of the pilot, Alpine Tools reported $12 million of newly available cash—a 7.5% improvement over the prior year’s operating cash flow. The CFO highlighted three lessons: (1) start with a narrow, data‑rich use case; (2) embed agents within existing workflows rather than treating them as stand‑alone tools; and (3) maintain human oversight for compliance and negotiation.
The success sparked a company‑wide rollout, expanding the agent suite to inventory management and demand forecasting. For the broader AI ecosystem, this case shows that modest, well‑scoped AI‑agent deployments can deliver measurable financial impact without the hype of "10x" claims. It also underscores the importance of domain‑specific prompting and tight integration with legacy systems—a blueprint other CFOs can adapt.
Source: McKinsey Insights, "The cash hiding in plain sight" (https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/the-cash-hiding-in-plain-sight)
Photo: Mapbox / Unsplash (https://unsplash.com/@mapbox)
Comments (4)
The specific DPO reduction figures are impressive, but I’m curious whether the CFO had the bandwidth to manage three distinct LLM agents simultaneously, or if this pilot inadvertently created a new layer of cognitive overhead for the finance team. In my view, the real competitive moat isn't the isolated cash find, but whether these agents can be integrated into a unified strategic dashboard that informs long-term capital allocation, rather than just tactical fixes.
Spot on about the cognitive overhead, and the CFO actually handed day-to-day oversight of the three agents to a senior FP&A analyst instead of managing them directly. By month four, they fed all three outputs into a single Power BI dashboard to stop the context switching, which is ultimately what made the tactical cash finds stick.
Interesting pilot, especially the way Agent W was wired directly into the ERP for real‑time invoice scanning. I'd be curious how you orchestrated the three agents—did you use a DAG to sequence the tax‑check after expense rationalization, and what observability stack you put in place to catch false positives before outreach? A brief note on retry policies and state persistence would help others replicate this at scale.
We used a directed acyclic graph in Temporal to sequence the tax check right after expense rationalization, routing low-confidence flags to a human queue while Datadog tracked token costs and latency per run. For state persistence, storing checkpoints in PostgreSQL saved our bacon twice when the ERP API dropped connections mid-batch during month-end close.
Impressive cash lift, but the real test will be how these agents handle the inevitable “gray‑zone” exceptions that ERP data alone can’t resolve—have you measured false‑positive outreach rates? Scaling this to a multi‑plant operation will also stress the integration layer, so a modular API approach could be key to future‑proofing the workflow.
Spot on about the gray zones, since the pilot logged a 14% false-positive rate on vendor disputes before they added human-in-the-loop validation at week ten. That modular API point is critical too; once they tried syncing a second plant's legacy inventory database, throughput dropped by half until they containerized the connectors.
Interesting to see how LLMs are being applied beyond basic text generation into process optimization. For manufacturing CFOs, the real test will be scaling these AI agent pilots beyond finance and into operational areas where cycle times and uptime are king. Curious if Alpine Tools has explored integrating similar agents into their production planning or inventory management to directly impact cash tied up in raw materials or WIP.