Nonprofits should not begin an AI fundraising program by purchasing software.
They should begin by identifying a fundraising problem that is costly, repetitive and measurable.
AI can help with donor research, campaign analysis, grant preparation, segmentation and follow-up. Results depend on data quality, staff adoption, governance and clearly documented workflows.
Phase 1: Define the Fundraising Problem
Choose one specific problem, such as delayed acknowledgments, unidentified lapsed supporters, slow donor briefings, manually tracked grants, difficult campaign comparisons, inaccessible program information or conflicting donor records.
Avoid goals such as “use AI to raise more money.”
A better goal is: “Reduce the time required to prepare a major-donor briefing from two hours to 30 minutes while maintaining accuracy.”
Phase 2: Document the Existing Workflow
Document what triggers the process, what information is required, where it is stored, who performs each step, which decisions require approval, what the final output should contain and how success is measured.
Automating a broken workflow usually creates faster confusion.
Phase 3: Assess Data Readiness
Review donor records for duplicates, missing history, inconsistent campaign names, outdated contacts, incomplete preferences, unverified notes, records outside the CRM and excessive access.
Do not connect AI to sensitive information until leadership understands where data is stored, whether it is retained and whether it may be used for training.
Phase 4: Establish Governance
Define approved tools, permitted uses, prohibited information, required review, vendor-security requirements, disclosure standards, accountable employees and error-reporting procedures.
NTEN’s AI resource hub emphasizes mission alignment, ethical guardrails, cultural readiness and policy development. Explore NTEN’s resources.
Phase 5: Select a Low-Risk Pilot
Good pilots include drafting acknowledgments, summarizing public grant requirements, preparing internal campaign reports, identifying incomplete CRM records, creating briefing drafts and converting approved reports into stewardship updates.
Phase 6: Define Success Measures
Measure staff hours saved, error rates, corrections, donor response rates, follow-up speed, campaign conversion, employee confidence, donor complaints and retention.
The NIST AI Risk Management Framework recommends continuing governance, measurement and risk management throughout the system’s life. Review the NIST framework.
Phase 7: Train Staff Around the Workflow
Employees need to understand how to use the system, what information they may enter, how to verify outputs, when approval is required, where source information comes from and how to report errors.
Phase 8: Review and Scale Carefully
Expand only when the pilot demonstrates measurable value and reliable controls.
Build the Knowledge Foundation
A secure AI Knowledge Hub such as Maisy helps nonprofits organize current program descriptions, impact statistics, gift policies, campaign priorities and approved communications language within Microsoft 365 and SharePoint.
Maisy does not replace a CRM, fundraising strategy or professional judgment. It provides the organized knowledge foundation needed to make AI-supported workflows reliable.
A successful program is built by solving one real problem, governing the system carefully, measuring results and expanding only after the workflow proves itself.
