AI governance for nonprofits is quickly becoming a management, finance, data security and leadership responsibility . . .not simply an IT conversation!

Dr. Stephanie Rose-Belcher of JMT Consulting explains how nonprofit organizations can gain enormous efficiencies of AI without surrendering human judgment, accountability or control of sensitive organizational data.  AI can accelerate everything from contracts and presentations to financial analysis and routine administrative work. Stephanie describes tasks that once required hours of formatting becoming dramatically faster with AI. But speed introduces a new business question: . . .who is responsible for the result?

Stephanie’s answer is direct: “You are still accountable.”

That matters when nonprofit employees begin experimenting independently with free AI tools. A grant manager, fundraiser or finance professional may see an easy way to analyze information without realizing they could also be moving organizational data into an environment leadership has never approved.

As Stephanie puts it, AI governance rests on three connected elements: “ . . . people, technology and policy and process.” Organizations need to decide what AI tools are approved, what information may be entered, which uses are acceptable, how outputs will be validated, and where important workflows need to become standardized.

The finance implications are especially important. If multiple employees independently create AI processes for the same accounting function, the organization may gain speed while losing consistency, traceability and auditability. AI-powered work still needs controls that allow someone to determine where an answer came from and how it was produced.

And smaller nonprofits are not excused because enterprise software costs money. Stephanie recommends establishing an acceptable-use policy defining what information is public, private and confidential—even when the organization cannot yet purchase a secure enterprise AI environment!

Key Takeaways:

Human accountability remains with the employee and organization using AI.

Build AI governance around people, technology and policy—not software alone.

Audit how employees are already using AI before assuming you know.

Protect donor, financial and organizational data from unauthorized AI use.

Standardize important AI-assisted finance processes so results remain repeatable and auditable.

Create an acceptable-use policy even when enterprise AI tools are outside the current budget.