Six Questions Nonprofit Leaders Should Ask About AI
Many nonprofit leaders view artificial intelligence (AI) as a potential way to address the challenges organizations across the country are facing, such as staffing shortages, rising administrative costs, and operational inefficiencies. Adopting AI can help reduce manual work, provide more data for improved decision making, and allow staff to focus on mission-driven priorities. Yet, there is a significant gap between curiosity and adoption. As we found in our 2026 Q2 State of the Nonprofit Sector report, more than half of nonprofits surveyed are actively exploring AI, but only 1% are actively implementing it. Meanwhile, 76% report they do not have a formal AI strategy.
The organizations making the most progress aren’t starting with technology. They’re starting with questions. The following are the six questions to ask when addressing challenges for your nonprofit.
1. What Problem Are We Actually Trying to Solve?
Many successful AI initiatives begin by identifying a specific operational challenge rather than an AI platform or tool.
Staff spend hours compiling reports, reconciling information across multiple systems, responding to routine inquiries, or manually analyzing data. As we found in our research, 31% of organizations report spending too much time on manual reporting, 34% struggle to access key metrics in real time, and 41% say they experience delays and inefficiencies because of disconnected systems. Only 9% consider themselves highly data driven. These pain points create a natural starting point for AI conversations.
Rather than asking, "How can we use AI?" leadership teams should ask, "Where are our people spending time on work that technology could simplify?" The objective isn’t simply to automate tasks. It’s to help reduce administrative burden and create capacity for relationship-building, strategic thinking, program delivery, and other mission-focused activities.
2. Where Can AI Quickly Create Value?
Though many nonprofit leaders may assume AI requires a large technology investment, some of the most practical applications build on data, systems, and workflows organizations already have in place.
Fundraising is one of the more common entry points. Organizations are increasingly using AI to help segment donors, identify giving patterns, draft outreach content, and prioritize stewardship efforts. In fact, as we found, 61% of nonprofits that have adopted AI use it for donor-facing content, donor analytics, or outreach activities.
However, fundraising is only one area of opportunity. Across nonprofit functions, early use cases often include:
· Donor segmentation and personalized outreach
· Fundraising analytics and donor propensity modeling
· Financial reporting and forecasting
· Accounts payable workflow support
· Grant compliance monitoring
· Internal knowledge management and policy search
· Employee onboarding and HR administration
· Program measurement and reporting
The common thread is practicality over technological sophistication. Organizations often see meaningful value when AI supports routine, time-intensive processes that consume significant staff capacity.
3. Which Use Case Should We Start With?
One common challenge is trying to tackle too many initiatives at once. The nonprofits seeing the strongest early results tend to start with focused pilot projects rather than large-scale transformations. These pilots typically are designed to address a specific challenge while producing measurable outcomes that leadership can evaluate in a relatively short amount of time.
An initial pilot typically addresses a manual, repetitive process; involves limited operational complexity; relies on existing data and systems; produces results that can be measured clearly; and causes minimal disruption to daily operations.
The experience of one national nonprofit offers a useful example. The organization began by applying predictive analytics to better understand donor engagement patterns and improve fundraising efficiency. Rather than attempting a broad deployment, leaders focused on a specific challenge: improving donor outreach and stewardship. They later expanded AI use into finance operations by supporting accounts payable workflows while maintaining human oversight.
For many nonprofits, beginning with a focused use case, addressing a manual burden, and building on early successes can help create organizational momentum.
This measured approach often builds organizational confidence in addition to these efficiency gains.
4. Are We Enabling Our People to Use AI Effectively?
Many nonprofit leaders identify limited expertise and staff capacity as significant barriers to wider AI adoption. Concerns about change, unfamiliar tools, and evolving roles can slow progress even when promising use cases exist.
Organizations experiencing sustainable adoption often invest in people and processes alongside technology. That includes explaining why AI is being introduced, communicating how it supports organizational goals, providing role-specific training, encouraging experimentation and feedback, and clarifying where human judgment remains essential.
One of the most important messages leaders can reinforce is that AI is not a replacement for employees. AI can support routine tasks so employees can focus on work requiring judgment, creativity, empathy, and relationship-building.
The national nonprofit mentioned above found success by emphasizing the "why" behind AI initiatives and by helping managers identify practical opportunities within their teams rather than deploying one-size-fits-all training. That focus on accessibility, communication, and change management helped build engagement across the organization.
5. Do We Have the Right Guardrails in Place?
Nonprofits are already experimenting with AI, while critical policies often remain underdeveloped. In our research, we found that 80% of those surveyed lack an acceptable-use policy, and 76% do not have an AI strategy.
To help address potential risks, organizations may want to establish clear guardrails around acceptable AI use, data stewardship and privacy, human review requirements, vendor selection criteria, and accountability.
This does not require building an extensive governance infrastructure on day one. However, organizations can evaluate which data can be used, where sensitive information may warrant additional restrictions, and how outputs can be reviewed before being incorporated into important decisions. Ideally, this task can be delegated to cross-functional governance groups that evaluate opportunities, prioritize use cases, and establish responsible practices as adoption expands.
6. How Will We Measure Success?
Organizations may initially evaluate success based on whether a new tool was deployed on time. A more meaningful question is whether the initiative improved operational performance or mission delivery.
Common measures may include time saved on administrative tasks, reduced manual reporting effort, faster access to organizational insights, improved financial visibility, shortened cycle times for routine processes, stronger donor engagement, and indicators aligned with mission outcomes.
The strongest pilots establish these measures before implementation and compare results over time. This creates clarity for leadership teams, boards, and staff and helps organizations determine whether an initiative should be expanded, revised, or discontinued. Most importantly, measurement helps keep AI initiatives aligned with organizational priorities.
Moving From Experimentation to Execution
While AI isn’t a solution to every operational challenge, it can be a powerful tool when applied thoughtfully to the right problems.
Chasing technology trends may be less effective than identifying specific opportunities to reduce administrative burden, improve decision making, and increase organizational capacity. Organizations should begin with focused use cases, supporting staff through change, establishing appropriate guardrails, and measuring outcomes that matter.
As nonprofits move from experimentation to adoption, experienced advisors, like the team at Forvis Mazars, can help leaders evaluate opportunities, prioritize investments, develop implementation road maps, and create governance structures that align with organizational goals.
Continue Learning
To explore additional resources on AI strategy in nonprofits or to connect with an AI professional at Forvis Mazars, visit our website. While you’re there, register for our webinar titled AI in Action: Practical Applications for Nonprofits coming up on Monday, November 9.
This content was paid for by Forvis Mazars. The editorial staff at The Chronicle had no role in its preparation. Find out more about paid content.
Source note: Statistics, findings, and referenced examples are drawn from the 2026 Q2 State of the Nonprofit Sector Report: AI Adoption & Governance, including the anonymized nonprofit example.
The information set forth in this presentation contains the analysis and conclusions of the author(s) based upon his/her/their research and analysis of industry information and legal authorities. Such analysis and conclusions should not be deemed opinions or conclusions by Forvis Mazars or the author(s) as to any individual situation as situations are fact-specific. The reader should perform their own analysis and form their own conclusions regarding any specific situation. Further, the author(s)’ conclusions may be revised without notice with or without changes in industry information and legal authorities.
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