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Evaluate an AI tool against a specific mission-related task—not its popularity or feature list. Decide whether AI is needed, identify the people and information at risk, test outputs and safeguards, and assign a person who remains accountable. There is no universally best tool: suitability depends on the task, provider terms, information involved, jurisdiction, and the organization’s ability to oversee it.
1. Define the task before comparing tools
Write down the problem the organization wants to solve, who will use the tool, who may be affected, and what benefit would count as success. Also ask what could happen if the system produces a wrong, incomplete, or harmful result. The UK Charity Commission advises charities to assess options and risks against their objectives and trustee duties; Australia’s ACNC asks whether AI is strategically the best solution for the organization.
Consider whether a non-AI process could meet the need with less risk or complexity. If the purpose is vague, or no one can explain how the tool would improve the work, pause rather than selecting a product first.
2. Match the use case to an appropriate level of risk
Begin with bounded, low-stakes, repetitive tasks where a person can readily review the result. Google for Nonprofits’ guidance recommends starting this way and reconsidering AI when work involves confidential data, high-stakes empathy, or final decision-making. AI should assist people, not quietly take over responsibilities that depend on human judgment or care.
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For a church, that distinction matters in pastoral care and spiritual formation: generated text may help with routine drafting, but it should not be treated as pastoral discernment or a replacement for trusted religious leadership. The Church of Jesus Christ of Latter-day Saints offers one institutional example—not a universal rule for every denomination—by saying AI should support rather than replace connection between God and people, protect sacred and personal information, and be tested and reviewed deliberately. Elder Brent H. Nielson Pingree said the principles are “intended to support the responsible use of AI by the Church workforce.” Read the Church’s AI principles.
3. Trace what information the tool will receive
Map data that could enter through prompts, uploaded files, connected services, or agent workflows. Include personal, sensitive, donor, beneficiary, employee, confidential, and sacred information. Then check the privacy policy, terms, retention and reuse practices, security documentation, and available access controls for the exact provider, account, and subscription tier you intend to use. These details can differ between products and plans, and can change over time.
- Minimize or remove identifying details when they are not needed for the task.
- Do not assume that information is private simply because a tool is used through an organizational account.
- Take particular care with children’s information and medical data; the UK Charity Commission highlights these as circumstances requiring heightened care.
- Check applicable privacy and compliance requirements in the jurisdiction where the organization operates and where affected people are located.
Google’s guidance specifically warns about personal and confidential inputs, while church guidance also emphasizes safeguarding sacred information. A tool’s suitability cannot be determined without knowing what information your workflow will expose.
4. Test quality, fairness, accessibility, and mission fit
Before wider use, test the tool with representative scenarios and have a qualified person review the results. Check for factual errors, omissions, harmful or discriminatory output, accessibility barriers, and tone that misrepresents the organization. Verify important claims against reliable sources rather than treating fluent language as evidence.
ACNC identifies risks including biased or discriminatory decisions, poor security, reduced human connection, and accessibility problems. Google for Nonprofits likewise recommends checking accuracy, fairness, representativeness, and whether the output reflects the organization’s authentic voice. Record meaningful failures and decide in advance what would require correction, escalation, or stopping use.
5. Compare tools against the same criteria
Once the task and risk are clear, assess candidate tools in the same organizational context. These comparison dimensions synthesize regulator and nonprofit guidance; they are not a ranked vendor scorecard.
| What to compare | Questions to ask |
|---|---|
| Mission and task fit | Does the tool address a defined need, and is AI preferable to a simpler non-AI process? |
| Data and security | What data is collected, retained, reused, or accessible? What protections and controls apply to the exact product and plan? |
| Quality and auditability | Can staff check claims, identify known failure modes, and reproduce or review how an output was used? |
| Fairness and access | Could the tool disadvantage affected groups, exclude people with accessibility needs, or produce biased results? |
| Human oversight and recourse | Who reviews outputs and decisions? Can an affected person ask for human help or challenge a consequential outcome? |
| Transparency | When should members, beneficiaries, donors, users, or staff be told that AI is involved? |
| Organizational capacity | Can the organization train staff, support the tool throughout its lifecycle, monitor it, and afford ongoing review? |
| Legal and reputational exposure | What local privacy, copyright, governance, or other requirements apply to this use? |
6. Keep accountability with people
Name the person or group responsible for approving the use, verifying outputs, monitoring for harm, handling complaints, and stopping the system when necessary. Do not treat generated output as the organization’s decision-maker. The Charity Commission for England and Wales states that trustees remain responsible and that decision-making should not be delegated to AI or based on generated content alone. Canada’s privacy commissioner similarly says accountability for decisions rests with the organization, not the automated system.
For significant decisions about individuals, Canadian privacy principles recommend telling people whether and how generative AI contributes, explaining safeguards and recourse, and providing an effective way to challenge the decision and obtain human review. Those principles and legal duties do not automatically apply everywhere; seek local legal or privacy advice when the stakes or obligations warrant it.
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A practical internal policy should make day-to-day decisions clearer. It can specify:
- Approved purposes and tools, plus prohibited or restricted uses.
- Data categories staff must not enter, and any permitted data-handling conditions.
- Who approves a use case and how risk is tiered.
- Required testing, human review, and verification before outputs are used.
- When disclosure or attribution is expected.
- What records to keep, how to report incidents, and who can suspend use.
- Staff training and a schedule for revisiting the policy.
The Charity Commission suggests considering a policy covering AI in governance, staff work, or service delivery. Google points nonprofits to Fast Forward’s no-cost Nonprofit AI Policy Builder; the guidance does not make that particular resource a requirement.
Maintain a concise record of the tool, task, data category, reviewer, known limitations, incidents, and the decision to continue or stop. Set a regular review date and revisit the assessment when the provider changes its terms or capabilities, the workflow changes, or relevant rules change.
What nonprofit adoption figures do—and do not—show
NTEN and The Bridgespan Group’s 2026 report page presents survey responses indicating that 30.42% of executive respondents (n=404) said a process to approve AI tools and vendors was in place, and 38.56% said written guidance on safe and responsible AI use was in place. Among staff respondents (n=264), 21.64% said staff training on safe and responsible AI use was in place. These are responses from the report’s respondents, not population-wide estimates or proof that any particular tool is safe. See NTEN’s research page.
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