Many nonprofit leaders recognize AI’s potential to transform their finance functions. The most visible use cases, however, may feel designed for the private sector, risky from a data-security standpoint, or difficult to justify as an upfront investment.
For organizations operating under heightened scrutiny from boards, funders, and regulators, confidence in AI requires a clear, practical understanding of where these tools fit into existing finance and accounting workflows, and whether the return justifies the complexity and cost.
In Grassi’s recent webinar, AI in Finance: Practical Tools for Smarter Nonprofit Operations, our Nonprofit and Technology advisors shared where organizations are already using AI today to improve accuracy, strengthen internal controls, and reduce manual effort without compromising oversight or accountability.
Below, we outline the highest-impact AI use cases across nonprofit finance, accounting, and audit, along with the risk considerations leadership teams should address before adopting them.
The Case for Adoption: Understanding Today’s Pressures on Nonprofit Finance Teams
Boards and funders expect timely reporting and clear visibility into how organizations deploy their resources. At the same time, finance and accounting teams continue to absorb growing technical complexity, from ASC 958 contribution disclosures and nuanced grant accounting guidance to ongoing compliance with ASC 842 lease standards. The requirements continue to become more rigorous, without corresponding increases in headcount or budget.
Amid these pressures, nonprofit finance leaders increasingly view AI not as a future investment, but as an operational stabilizer within the finance function.
What Nonprofit Leaders Should Know Before Implementing AI
As discussed in our most recent article, leadership must clearly understand where AI fits into their processes, what decisions it can support, and where human oversight remains essential. Before exploring specific use cases, three concepts are important to understand:
AI Extends Beyond Chat-Based Tools
Chat-based tools such as ChatGPT are powered by large language models (LLMs), AI systems trained to predict and generate text. These tools are skilled at drafting documents, summarizing information and supporting basic research. The most impactful applications for finance teams, however, use machine learning and agentic AI embedded directly within finance systems. These tools can analyze large volumes of transactions, automate routing and coding of expenses, and flag unusual activity for review.
In their newest iterations, many machine learning and agentic tools have added an LLM layer on top of these workflows. This makes the tools easier to use and govern by letting finance teams query exceptions in plain language, surface the rationale behind flags, and generate consistent summaries that speed up review, documentation, and follow‑up.
AI Requires a Solid Data Infrastructure
Enterprise Resource Planning (ERP) platforms have been a major initiative for many organizations in recent years. A connected, single source of truth for an organization’s data, further supported by advanced data warehousing, creates the standardized data environment that AI needs to function effectively.
Begin Prioritizing Manual, High-Volume Tasks
When evaluating where to begin, high-volume, repeatable workflows are the most practical starting point. Accounts payable, reporting, payroll compliance and reconciliations are well-suited for automation and should already operate within defined approval structures.
AI Use Cases Across Nonprofit Accounting, Finance, and Audit
The following applications reflect where nonprofit finance teams are seeing practical impact today, based on real-world implementation examples discussed in the webinar:
- Accounts Payable: When transactions fall within defined thresholds, invoices can be scanned, coded, and routed for approval through automated, rule-based agentic workflows. CFOs, Controllers, and other designated approvers receive daily summaries via an automated email to review exceptions or anomalies.
- Expense Reporting: AI-powered virtual card and expense platforms can restrict spending to approved categories, apply itemized transaction limits and prompt users to submit receipts immediately after a purchase. These tools can code expenses as they occur, accelerating close timelines and mapping certain expenses to funds and grants.
- Payroll and Labor Compliance: For organizations with hourly or grant-funded staff, AI can pinpoint recurring overtime, unexpected rate changes or missing personnel documentation, allowing teams to run checks alongside payroll processing.
- Budgeting and Forecasting: Budgeting has traditionally required weeks of manual data gathering. AI can consolidate historical financials, program data, and funding inputs more quickly, enabling finance teams to model scenarios, refresh forecasts on a rolling basis, and instead focus their time on validating assumptions.
- Accounting Standards and Technical Research: AI tools can scan large volumes of authoritative guidance, pinpoint relevant sections, and organize excerpts by issue or fact pattern. This can accelerate research, improve consistency across analyses, and help teams draft clearer technical memos and documentation.
- Audit-Ready Schedules: Depreciation schedules, present‑value calculations for pledges receivable, and amortization schedules can be generated directly from system data, shifting staff time away from manual preparation toward validation, documentation, and review.
The Importance of Human Judgment, Accountability, and Governance
For all its valuable use cases, AI cannot sign off on a financial statement, defend a judgment call, or take responsibility for a control failure. Human‑in‑the‑loop control, where people remain directly accountable for approvals, interpretations, and outcomes, must be built into any implementation from the start.
The same responsibility applies to data protection. Finance teams handle donor information, employee records, and financial data that cannot leave controlled systems. When donor trust is central to an organization’s credibility, leadership must stay current on data protection requirements, and ensure employees understand that AI tools should only be used within approved enterprise environments.
Starting Points for Adoption
Leadership teams across the nonprofit sector are still learning how to use AI well. The right approach is to experiment with intention, not to overhaul everything at once.
- Start with one or two pain points: Expense management and AP processing are where most organizations begin, given their high-volume, repeatable nature.
- Build sustainably: Take time to understand the tools, train staff and measure results before expanding.
- Establish oversight: Consider forming a small internal committee or designating a point person to coordinate AI governance, evaluate new tools, and share what is working across departments.
With the proper approach, the value of AI is not automation for its own sake, but time returned to the finance team that can be redirected toward strategic decision-making and the mission-driven work that defines the organization.
How Grassi Can Help
Grassi’s Nonprofit and Technology advisors work with organizations at every stage of the AI implementation journey, from evaluating data infrastructure and identifying high-impact workflows to implementing tools and establishing governance frameworks. To discuss how AI can support finance operations, connect with a Grassi advisor today.
To continue exploring these examples and practical guidance referenced in this article, watch the on-demand recordings from Grassi’s nonprofit technology series:
AI in Finance: Practical Tools for Smarter Nonprofit Operations
Process Improvement Through Technology: Driving Operational Effectiveness
