How FirstMile's CFO Uses Stampli to Run Finance with Interns—And Why That Shouldn’t Scare You
Published in Parcel: How AI makes interns as capable as seasoned finance staff
In a recent first-person article published on Built In, Scott Hale, CFO of shipping logistics company FirstMile, laid out a bold but efficient operating model: his company runs core finance functions, including accounts payable, using a team that’s largely composed of college interns. On the surface, that sounds risky. But Hale isn’t cutting corners—he’s relying on Stampli’s AI to make it work. And if you’re wondering how that’s possible—or whether it’s something your organization could replicate—what follows will help clarify how Stampli’s technical capabilities address legitimate skepticism.
What would make you skeptical?
- Can less experienced staff really handle critical finance operations?
- Will automation fail when codes or processes change?
- What happens when invoice exceptions arise?
- Is AI just a fancier rules engine?
- How much oversight is required to avoid errors?
Here are five quotes from Hale’s article that speak to those concerns—each paired with a clear, technical takeaway.
1. “For example, we rely on AI capabilities from Stampli to code our invoices and route them through the approval process. The tool automatically detects the codes we use by analyzing existing invoices, then codes incoming invoices on its own.”
Takeaway: This isn’t a rules-based engine requiring constant updates. Stampli’s AI uses pattern recognition to determine GL codes automatically, which means even novice users aren’t responsible for maintaining logic trees. This lowers the skill threshold for managing AP without increasing risk.
2. “Previously, we relied on AP software… that didn’t rely on real AI. It automatically coded invoices based on rules… But we still had to set up the rules and update them whenever we modified our codes – tasks that were difficult for inexperienced staff to handle.”
Takeaway: Rule-based systems often introduce hidden technical debt—every time your chart of accounts changes, someone has to reconfigure the logic. Stampli eliminates that maintenance burden by learning from historical coding behavior. That means fewer break points and no need for manual retraining when your business evolves.
3. “Despite our interns’ limited experience… we’ve been able to maintain highly productive, reliable and consistent financial processes.”
Takeaway: This is not just about reducing headcount cost—it’s about robustness. Stampli reduces the margin for human error and helps ensure that process quality doesn’t fluctuate with staff turnover. For companies with seasonal or rotating teams, that kind of operational continuity is critical.
4. “We’ve reduced the time it takes to process each invoice from an average of about five minutes to less than one. And the accuracy rate is very high; it’s quite rare that we have to recode an invoice manually to correct mistakes made by AI.”
Takeaway: Sub-one-minute processing time with high accuracy is not a marginal gain—it’s a step change in throughput. This is particularly important if you’re scaling invoice volume without proportional increases in team size. The few minutes saved per invoice compound into hours saved per week, with very low intervention needed.
5. “We no longer have to manage invoicing rules because our AI tool detects invoice coding patterns on its own. Whenever our codes change, it adapts automatically.”
Takeaway: True AI systems adapt in real time. Stampli’s self-adjusting model means it doesn’t become obsolete the moment your accounting structure shifts. You don’t need to anticipate every change in advance or rely on IT to implement it—this level of adaptability is what allows less experienced staff to stay productive.
Conclusion:
The point of this story isn’t that interns can replace accountants—it’s that the right technology can handle the heavy lifting so that less experienced team members can execute with confidence and accuracy. What Scott Hale has demonstrated at FirstMile is not just a cost-saving measure—it’s a validation of Stampli’s core technical premise: when AI can do the hard parts of AP, your team doesn’t need to be seasoned experts to achieve expert-level results.
If you’re thinking about how to build a more resilient, scalable finance operation, the model at FirstMile isn’t an outlier—it’s a preview of what’s possible.
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