Nonprofit finance teams face constrained resources and rising expectations for transparency. The pressure to “do something about AI” is real. So is the risk of moving in the wrong direction.

Start with what you’re already paying for

Most nonprofits already use automation without recognizing it. QuickBooks categorization, Bill.com approval workflows, and payroll anomaly flagging are features of tools you already pay for, increasingly powered by AI.

The first question isn’t “what should we adopt?” It’s “what has our vendor already turned on, and does anyone review it?” Organizations regularly find a feature that has been suggesting account codes for eighteen months, unexamined.

Where nonprofit accounting breaks generic tools

Restricted funds, grant-by-grant reporting, and functional allocation present challenges commercial tools rarely address.

A categorization engine learns from transaction history. It cannot see the grant agreement splitting this vendor’s invoices 60/40 across two restricted awards. It repeats last year’s treatment consistently, so the error doesn’t look like an error. It looks like a pattern.

Allocation follows suit. The system cannot know that a program director shifted 20% of her time to development work. It keeps allocating her salary cleanly until someone – often an auditor – catches it.

The control is simple: exclude anything touching a restricted fund or functional allocation from automatic posting. The tool suggests. A person posts.

What your board and your auditor will ask

Three governance items deserve attention before adoption, not after.

  •  Data governance. Define in writing what may not be entered into an AI tool: donor records, compensation, and grant-confidential material.
  •  Human review. AI-generated entries, narratives, and summaries still need sign-off by a qualified person. Auditors will ask who reviewed them; be prepared with a documented answer.
  •  Your vendors’ policies, not just yours. Ask professional services firms touching your finances what their acceptable use policy says.
Where to start

Begin with tasks that are high-repetition, low-consequence, and easy to check:

  •  Drafting a board-packet variance narrative from a P&L you have already reviewed
  •  Summarizing a grant agreement’s reporting requirements and deadlines into a checklist
  •  First-pass categorization of unrestricted, routine transactions, reviewed before posting
  •  Turning identified reconciliation exceptions into plain-language notes for the audit file

The pattern: the human establishes what is true; the tool handles the writing or sorting.

An acceptable use policy belongs in this phase. Staff are almost certainly already experimenting on their own accounts. A sanctioned environment turns that into managed activity.

What automation won’t fix

Automation applied to a process you don’t understand doesn’t eliminate the problem. It accelerates it, or worse, obscures it. An uncleaned chart of accounts, an unjustifiable allocation methodology, a late close: none are technology problems, and none improve with a tool.

Documenting how a process actually runs and fixing it is the prerequisite, and usually where the first efficiency gains come from.

Five questions worth asking now
  1. Which AI features are already active in our systems, and who enabled them?
  2. What are staff entering into AI tools today, and where does it go?
  3. Which processes are documented well enough that automating them would be safe?
  4. Who signs off on AI-assisted output before a funder or auditor sees it?
  5. What will our audit firm expect us to document about AI use this year?

If those questions have clear answers, the organization is well-positioned regardless of which tools it adopts. The organizations best positioned to benefit from AI are not the fastest, but the ones moving with the clearest understanding of their own processes, governance standards, and what they actually need. 



About the Author: Elizabeth Stasiowski is the Team Lead & Associate Finance Director at Insource Services Inc.