⬜️ 1. Document the specific pain point this AI solution will address
Write a one-sentence problem statement with baseline metrics (e.g., “donor emails currently take 3 hours daily to answer”). Document current process inefficiencies and ensure AI addresses a genuine business need, not just technological novelty.
⬜️ 2. Calculate the baseline time/cost of the current process
Document exactly how many hours and dollars are spent monthly on this task today. This baseline is essential for calculating ROI later and justifying the investment to your board and funders.
⬜️ 3. Design a short pilot (1 or 2 months)
Define a controlled pilot with clear boundaries, success gates, and rollback procedures. Starting small reduces risk, enables rapid learning, and provides concrete go/no-go decision points.
⬜️ 4. Set SMART goals aligned with mission impact
Set 2-3 specific, measurable, achievable, relevant, time-bound objectives like “reduce response time from 48 to 12 hours by Q2.”
⬜️ 5. Assemble a cross-functional team with defined roles
Include project lead (5+ hours/week), domain expert, IT representative, legal advisor, and end user representative. Document each person’s responsibilities and time commitment in writing.
⬜️ 6. Survey 3-5 end users about workflows and concerns
Interview staff who will use the tool daily, documenting their top 3 concerns (e.g., job replacement, complexity). Create a response plan for each concern before implementation begins.
⬜️ 7. Prepare stakeholder communication plans
Draft separate one-page explanations for board, beneficiaries, donors, and staff about AI usage. Include opt-out procedures and emphasize human oversight for sensitive decisions.
⬜️ 8. Recruit 5-8 diverse testers including skeptics
Include early adopters, resistant staff, different skill levels, and various departments. Document tester profiles to ensure representative feedback.
⬜️ 9. Track both performance metrics and user experience
Measure accuracy, speed, error rates AND user satisfaction, trust levels, and workflow integration. Create weekly dashboards showing trends in both categories.
⬜️ 10. Document quick rollback procedures
Write step-by-step rollback instructions and ensure all team members know the escalation process.
⬜️ 11. Verify insurance coverage for AI
Confirm your insurance covers AI-related errors.
⬜️ 12. Make human-in-the-loop review mandatory for sensitive decisions
Define clear thresholds requiring human review (e.g., grant decisions >$5,000, beneficiary eligibility, employment).
⬜️ 13. Create and test an incident response plan
Document response procedures: system pause triggers, stakeholder notifications, root cause analysis, and remediation steps. Run quarterly drills to ensure readiness.
⬜️ 14. Get legal and compliance team review and sign-off
Get written sign-off on HIPAA, FERPA, state privacy laws, and funder restrictions. Document any regulatory changes that would trigger reassessment.
⬜️ 15. Designate and train 2-3 super users
Provide advanced training to internal champions, so they can solve complex issues and mentor others.
⬜️ 16. Schedule daily monitoring for the first week
Schedule daily standups for week one, then weekly reviews. Track issues, questions, and adoption metrics with rapid response protocols.
⬜️ 17. Schedule quarterly governance reviews
Schedule reviews with stakeholders including ethics assessment, bias audits, and strategic alignment checks.
⬜️ 18. Compare actual vs. projected ROI monthly
Calculate time saved, cost reduced, and quality improved against baseline. Create executive dashboards showing trends and variance from projections.
⬜️ 19. Create a scaling roadmap with decision gates
Define clear phases: pilot → department → organization-wide. Set success criteria for each phase and go/no-go decision points with stakeholder approval.
⬜️ 20. Deploy shadow deployment and champion/challenger models
Run new processes or models alongside production versions for 2-4 weeks before switching.
⬜️ 21. Establish an AI ethics board with external advisors
Form committee including beneficiaries, ethicists, and domain experts meeting quarterly.
⬜️ 22. Pursue third-party AI audits and certifications
Engage independent auditors annually for bias, security, and fairness assessments.
⬜️ 23. Contribute to open-source nonprofit AI initiatives
Share validated prompts, frameworks, and case studies on GitHub.
You should probably adapt this checklist to the specific needs and priorities of your organization. You can copy the contents of this page into a Google Doc or similar tool, edit the list and maybe export it as PDF to share it with your colleagues.