AI’s Rapid Evolution vs. The Nonprofit Reality

Buu-Linh Tran, CPA, and A. Michael Gellman, CPA, CGMA

A significant divide is opening in the world of nonprofit organization finance.   Nonprofit leaders are constantly navigating a complex web of immediate challenges—from ensuring long-term sustainability and revenue diversification to managing chronic cost constraints. These urgent survival mandates often force AI adoption to a lower priority, creating a stark contrast to the technology’s breakneck evolution.  

This “push-pull” reality is on full display in the significant divide widening in the world of nonprofit organization finance. On one side, accounting leaders and software publishers are moving at a breakneck pace to integrate Artificial Intelligence (AI) into the core of their operations. On the other, many nonprofit finance leaders are just beginning to ask what AI means for their missions. This “speed gap” highlights a critical shift: while the technology is ready to run, many organizations are still focused on mastering the foundational steps of automation.

The Pace-Setters: Moving at the Speed of AI

In the professional services world, the transition to AI is no longer a multi-year roadmap—it is a current mandate. Recently, PwC US CEO Paul Griggs sent a clear message to the industry: you cannot opt out of AI. PwC is rapidly shifting toward an AI-first operating model and “outcomes-based pricing,” moving away from traditional hours-based billing. By treating routine manual tasks as a commodity handled by AI platforms, they are signaling that the value of finance is moving from “doing the work” to “delivering the outcome.”

The ERP Landscape: Publishers in a Sprint

This urgency is mirrored in the software landscape, where publishers are racing to embed AI into the very fabric of their applications. Market leaders like Sage have been leveraging machine learning for over six years, while smaller publishers are just beginning to enter the space. Regardless of size, every major financial software publisher is now marketing AI as a core feature rather than a futuristic add-on.

For example, many mid-market cloud-based ERP solutions like Sage Intacct has moved beyond simple automation to “Continuous Accounting” by leveraging several key AI features:

• GL Outlier Detection: Like an automated internal auditor, this feature uses machine learning to analyze thousands of journal entries in real-time. It flags anomalies that don’t match historical patterns, allowing finance teams to catch errors or potential fraud before the books are closed.
• AI-Powered AP Automation: This goes beyond standard OCR. It uses AI to “read” invoices and automatically predict GL coding and dimensions based on past behavior, significantly reducing the manual labor associated with accounts payable.
• Sage Intelligent Time: For nonprofits managing multiple grants and projects, this AI assistant reconstructs a user’s workday by analyzing calendar and email activity. It suggests time entries, ensuring that every hour of labor is accurately captured and allocated to the correct funding source.

Finance Intelligence Agent

Recently released in Feb 2026, this natural language interface allows executive directors and CFOs to “talk” to their data. By asking a simple question like, “What is my remaining budget for the City Grant?” the agent instantly pulls the data, eliminating the need for manual report generation.  

Expanding the Ecosystem: AI Beyond the ERP

While publishers in the ERP space are moving at a fast pace to embed AI, the entire ecosystem is following suit. Many other financial systems outside of the ERP space are also embedding AI into their solutions:

• BILL: Recently released its “Invoice Coding Agent,” where the system dynamically codes multi-line bills based on previous behavior, significantly reducing manual data entry.
• Martus: This budget management platform now allows users to enter report descriptions in plain English. Martus builds the report, accordingly, eliminating the need for staff to learn complex report-building technicalities.

The Reality Check: Adoption Barriers and “Baby Steps”

Despite the technological advances, adoption in the nonprofit sector remains uneven. Several factors contribute to this “wait and see” approach:

• Awareness and Change Management: These tools are new, and not all finance teams are aware of the features available in their existing software. Even when there is awareness, embracing change takes time; many users continue to operate software the same way they have for years out of habit.
• The Complexity of Use Cases: AI features are not yet a “magic wand.” While they are becoming more robust, they don’t yet address every complex or unique use case found in nonprofit accounting.
• The Foundational Gap: Many organizations, especially smaller nonprofits, are still struggling with basic operational hurdles. They are focused on automating “the basics”—processing cash receipts, revenue recognition, and reducing manual Accounts Payable (AP) workflows. For these organizations, the journey involves taking “baby steps” toward automation before they can effectively “run” with advanced AI agents.

Bridging the Perspective Reconciliation

Why is the speed of adoption so vastly different for nonprofit organizations? The answer often lies in the underlying governance and incentives.

While the corporate world leverages AI to scale revenue, nonprofits often face an internal “perspective reconciliation.” Boards frequently prioritize immediate risk mitigation, creating a strategic misalignment with management and staff who are charged with ensuring long-term mission delivery and competitive relevance to constituents. This challenge is often compounded when fiduciary management rests with an ever-changing set of volunteer Board members constrained by short service terms (2 to 3 years).

Planning Tip: Build in regular monthly financial reporting and accounting system integrity checks, asking two questions: 1) is financial reporting meeting user needs, and 2) how can we make accounting systems more efficient and secure? Do not wait for problems to arise to spur new AI-enabled system integration and implementation. Use a basic checklist centered on key financial reporting and accounting system integrity chokepoints (bottlenecks) to spur faster adoption and integration of new AI-enabled solutions.

Conclusion

The lag in AI adoption within the nonprofit sector is not a surprise; in many ways, it is expected. While for-profit organizations leverage AI to drive bottom-line numbers, nonprofits are more focused on the heavy lifting of achieving their missions. Financial constraints often mean that nonprofit organization technology cycles are measured in years, not weeks.

By the time a nonprofit bridges this foundational gap, the solutions available are leaps and bounds beyond their current legacy systems. The goal is not to match the breakneck pace of the global corporate world, but to build a more favorable culture of awareness to adopt a proactive digital foundation today that will support the mission for years to come.

1. Michael Gellman, CPA, CGMA, is a nonprofit organization fiscal and financial strategist and co-founding principal partner of Sustainability Education 4 Nonprofits, LLC (SE4N) and Fiscal Strategies 4 Nonprofits, LLC (FS4N)