Practical AI use cases for nonprofits
Nonprofit organizations are often asked how they plan to use artificial intelligence, even when they operate with limited resources, high trust obligations, and little margin for error.
This page outlines examples of how AI is beginning to show up in nonprofit work, along with the conditions that tend to make these uses helpful or risky. The goal is not to encourage adoption, but to support clearer thinking about where AI may reduce burden, where it may add risk, and where restraint is often the better choice.
These examples are illustrative, not prescriptive.
How to read this page
Each use case is described along three dimensions:
- Readiness — what skills, data, and governance need to exist first
- Risk — who is affected if something goes wrong
- Reversibility — how easy it is to correct mistakes or withdraw use
In practice, many challenges arise not from the technology itself, but from overestimating readiness, underestimating risk, or treating AI outputs as authoritative when they are not.
Low-risk, internal use cases
Where most organizations should start, if anywhere.
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Internal document drafting and revision
What it helps with
Producing first drafts of internal documents such as memos, policies, agendas, or briefing notes that staff can then edit and refine.Readiness considerations
Clear ownership of final content, expectations that humans remain accountable, and shared guidance on tone and accuracy.Risk considerations
Outputs are frequently generic, overly confident, or misaligned with organizational tone if prompts and review processes are weak.Reversibility considerations
Drafts can be discarded or revised with minimal consequence, provided AI output is not treated as authoritative or final. -
Summarizing long reports and research
What it helps with
Reducing the effort required to digest long reports, evaluations, or research papers by producing summaries, comparisons, or thematic extracts.Readiness considerations
Staff must understand the source material and be able to assess what has been omitted or simplified.Risk considerations
Important nuance can be lost, and summaries may reflect the model’s assumptions rather than the author’s intent.Reversibility considerations
Errors are usually recoverable if summaries are treated as aids to reading, not substitutes for engagement with the original material. -
Grant application first drafts and reframing
What it helps with
Generating early drafts, alternative framings, or clearer explanations of programs and impact for grant applications.Readiness considerations
Strong internal clarity about mission and programs, and shared understanding of what funders are actually being asked to support.Risk considerations
Language can become formulaic or disconnected from real work if outputs are used without grounding or verification.Reversibility considerations
Low, as long as AI-generated language is revised and owned by staff before submission. -
Internal knowledge access
What it helps with
Making internal documents easier for staff to navigate by answering questions based on existing policies or guidance.Readiness considerations
Up-to-date documentation, clear version control, and agreement on which sources are authoritative.Risk considerations
If source material is outdated or inconsistent, AI can confidently surface incorrect information.Reversibility considerations
Errors are correctable if outputs are monitored and staff know how to challenge or escalate issues. -
Meeting preparation and recap support
What it helps with
Preparing agendas, outlining discussion points, or summarizing notes and action items after meetings.Readiness considerations
Clear understanding of meeting context, participants, and sensitivities.Risk considerations
Outputs can miss nuance, power dynamics, or unspoken concerns that matter in nonprofit environments.Reversibility considerations
High, as long as summaries are reviewed and not treated as definitive records. -
Brainstorming and outlining
What it helps with
Generating options, questions, or outlines when teams are stuck or facing blank-page problems.Readiness considerations
Willingness to evaluate ideas critically and discard those that do not fit organizational reality.Risk considerations
Ideas may be superficial or overly optimistic without grounding in constraints.Reversibility considerations
Very high, provided outputs are treated as prompts for thinking rather than direction.
Medium-risk, operational support
Potentially useful, but only with clear guardrails and human oversight.
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Fundraising research and prospect synthesis
What it helps with
Summarizing publicly available information about foundations, donors, or funding programs to support prospect research.Readiness considerations
Clear norms about acceptable data sources and staff understanding of donor context.Risk considerations
Shallow or misleading summaries may create false confidence about donor intent.Reversibility considerations
Moderate, provided outputs are not directly reused in outreach without review. -
Program evaluation and outcome synthesis
What it helps with
Synthesizing qualitative and quantitative data to surface themes for internal learning.Readiness considerations
Sound evaluation practices and clarity about data limitations.Risk considerations
Over-generalization or privileging easily summarized outcomes.Reversibility considerations
Moderate, as long as findings are treated as discussion inputs, not conclusions. -
Communications support with mandatory review
What it helps with
Drafting or revising newsletters, reports, or web copy.Readiness considerations
Clear brand voice and review processes.Risk considerations
Loss of authenticity or misalignment with community expectations.Reversibility considerations
Moderate, provided all external communications are reviewed before release. -
Scenario exploration for planning
What it helps with
Exploring hypothetical scenarios to support leadership discussions.Readiness considerations
Experienced facilitation and explicit assumptions.Risk considerations
Plausible-sounding but poorly grounded scenarios.Reversibility considerations
High, as long as scenarios are clearly framed as exploratory. -
Policy and briefing note preparation
What it helps with
Producing early drafts of policy summaries or briefing notes.Readiness considerations
Editorial oversight and clarity about accountability.Risk considerations
Obscuring uncertainty or contested issues.Reversibility considerations
Moderate, provided drafts are reviewed and revised before use.
High-risk or cautionary areas
Often discussed, rarely appropriate early.
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Constituent-facing chatbots
What it helps with
Automated responses to common questions from the public or service users.Readiness considerations
Robust governance, escalation paths, and content boundaries.Risk considerations
Incorrect or inappropriate information reaching vulnerable individuals.Reversibility considerations
Limited, especially once trust is damaged or harm occurs. -
Automated eligibility or prioritization decisions
What it helps with
Supporting triage or prioritization when demand exceeds capacity.Readiness considerations
Strong governance, transparency, and community input.Risk considerations
Encoding bias or obscuring ethical judgment.Reversibility considerations
Low, as affected individuals may experience lasting consequences. -
Voice agents or multilingual intake
What it helps with
Lowering language barriers or extending access.Readiness considerations
Deep understanding of user needs, consent, and fallback mechanisms.Risk considerations
Misinterpretation, exclusion, or erosion of trust.Reversibility considerations
Low, particularly when errors affect vulnerable populations.
How to use this page
These examples are intended as conversation starters. They can help leadership teams, boards, and staff reflect on readiness, risk, and responsibility before deciding whether to move forward.