How AI saves time for nonprofits depends far less on the excitement surrounding the technology and far more on the quality of the organization’s data, workflows, and financial controls. Buu-Linh Tran, Senior Vice President of Financial Solutions at JMT Consulting, and Torbjorn Nilsen, Director of Business Solutions at DATABASICS, explain where AI can deliver measurable value—and where nonprofit leaders should proceed carefully.

The conversation moves beyond broad promises about efficiency and into the daily work of nonprofit finance operations. Data entry, receipt review, expense coding, compliance checks, anomaly detection, and financial reporting are all areas where technology can reduce repetitive work and help employees focus on higher-value decisions.

One example shows how AI can examine an itemized receipt, recognize an alcohol brand, and flag a potentially unallowable expense. It can also identify spending drift, unusual fund-code activity, or patterns that may be missed when transactions are reviewed individually.

But automation is not the same as control.

As Torbjorn cautions, “We can’t let the machine control. The control still has to be there.” AI should help nonprofit teams surface concerns and direct attention—not make unchecked financial decisions.

Buu-Linh offers another important reality check: “Look at the basics first—look at tools that help you streamline your operations.” Poor data, inconsistent coding, and inefficient processes do not become reliable simply because AI has been added.

The guests also discuss natural-language reporting, which could allow managers to ask direct questions such as, “How much have we spent on this conference?” or “Are supply costs higher than last year?” Instead of learning a complicated reporting system, users may receive the information they need in plain language.

JMT Consulting has served nonprofit organizations since 1991 and currently supports more than 2,300 nonprofits. DATABASICS has worked with nonprofit organizations since the mid-1990s, helping manage time, expenses, grants, and workforce processes.

Key Takeaways:

Begin with the operational problem—not the desire to adopt AI.

Clean, consistent data is essential for reliable AI-generated analysis.

Data entry and high-volume receipt processing are strong automation opportunities.

AI can flag anomalies, unallowable costs, spending drift, and questionable fund coding.

Natural-language reporting can make financial information more accessible to non-finance managers.

Human oversight, privacy controls, and cross-department collaboration remain essential.

 

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