RSMUS.comAI readiness in nonprofit finance starts with more than a technology decision. For organizations managing grants, memberships, program fees, and contracts across multiple entities, progress depends on reliable data, clear governance, and defined human review. Those elements help finance teams evaluate where AI can assist without weakening accountability.
The readiness gap is a trust gap.
Many nonprofit finance teams have discussed AI, reviewed a demonstration, and then paused when questions arose about auditability, data use or review. The hesitation is understandable. Finance leaders need to know how an output was produced before they can rely on it for grant reporting, Form 990 preparation, funder dashboards or board materials.
Sage research released in July 2026 reported that 71% of finance leaders would reject an AI tool that was 99% accurate if it could not explain its answers. The same research reported that finance professionals spend nearly 13 hours each week reconstructing, validating, and defending AI outputs, while 49% of U.S. respondents spend 15 hours or more. For a lean nonprofit team, limited visibility can shift work into verification rather than reduce it.
Build the data foundation first
AI readiness in nonprofit finance depends on the structure beneath each transaction. Diversified revenue can include restricted grants, membership dues, program fees, contracts and earned revenue. Each stream may have different recognition, reporting, and forecasting requirements. Chapters, affiliates and related foundations may also maintain separate systems or spreadsheets.
When entity, program, grant, funding source, and restriction data are not captured consistently, an AI-enabled tool has less context to explain a variance or support fund reporting. A multidimensional, fund-based general ledger can provide a more consistent structure for analysis while giving staff a clearer record to review.
The practical goal is not perfect data across every process. Start with the workflow selected for automation, identify the dimensions it needs and address gaps before activation. This narrow approach makes governance and reviews easier to define.
Set governance, ethics and cybersecurity expectations
Nonprofit finance systems may connect with sensitive donor, member and constituent information. Any AI evaluation should answer four questions: What data can the model access? Who can prompt it? Where do outputs go? Is the activity traceable for review?
The source draft states that Sage labels AI across its products and describes an approach aligned with GDPR, CCPA, the EU AI Act, the NIST AI Risk Management Framework, the UK NCSC AI Cyber Security Code of Practice and OWASP AI and LLM security practices. It also identifies encryption, anonymization, access management and continuous monitoring as elements of that approach. Product, legal and cybersecurity reviewers should validate these statements against current documentation before publication.
Vendor review should move beyond general security statements. Ask for named frameworks, documented data handling, access controls, monitoring practices and a clear description of how donor and constituent data is treated.
Keep people in consequential decisions.
The strongest theme in the working draft is the human checkpoint. AI can organize information, identify patterns and flag exceptions, while authorized staff retain responsibility for review and approval. This model supports transparency and gives teams a defined place to challenge an output.
The draft identifies several Sage Intacct capabilities that illustrate this approach:
- Finance Intelligence agent: Provides plain-language access to financial information, with visible reasoning described in the source. The draft states that an administrator enables access and user permissions control it.
- Outlier Detection: Flags unusual journal entries by comparing them with historical patterns and materiality thresholds, leaving the review decision with staff.
- AP Automation: Creates draft bills, matches documents and identifies possible duplicates, while staff review and approve the work.
- Close automation: Tracks close tasks across entities and flags issues for staff attention.
These feature descriptions require product review for current naming, availability, configuration and behavior. The governance principle is broader: technology can surface information, but people should remain involved when a decision affects financial reporting, compliance or sensitive data.
Prepare the team for control ownership
Technology is only part of AI readiness in nonprofit finance. The finance role is also moving toward review, judgment and control ownership. The source cites Sage research reporting that U.S. finance leaders rank risk, governance and decision judgment as the leading skill for a new finance hire, nearly twice as often as deep technical accounting.
Make the operating model explicit. Tell staff which workflows AI will touch, identify who reviews flagged items, define response expectations and document what happens when an output is incorrect. Clear responsibilities can make adoption more practical and easier to monitor.
A practical 90-day starting checklist
- Select one workflow with a clear audit trail, such as AP coding, outlier review or grant and close task tracking.
- Document the entity, program, grant, funding source and restriction data needed for that workflow.
- Define the human checkpoint, review threshold and documentation requirement.
- Include compliance, legal, privacy and IT stakeholders before activation, and review access controls and data handling.
- Track time returned to staff and exceptions identified, then use the findings to decide whether to expand.
The takeaway
Closing the readiness gap calls for a dependable data foundation, a documented governance model and a team that understands its review role. Beginning with one workflow gives nonprofit finance leaders a practical way to test controls, examine outputs and learn before expanding automation.
RSM works with nonprofit finance teams on AI and data readiness, finance system modernization and governance planning. Consider a focused readiness discussion to identify one workflow, its data requirements and the human review it needs.
Explore a focused AI readiness assessment for one nonprofit finance workflow.
Mark Smallwood
Mark is a Senior Associate in the Technology Consulting practice and a Certified Sage Intacct Implementation Consultant. With a background in accounting and data analytics, he works with not-for-profit, financial services, and healthcare organizations to streamline financial operations and support efficient system implementations from planning through go-live.
