Hot Takes

AI Adoption in Nonprofits: Practical Applications & Ethical Best Practices

AI is seemingly everywhere… but how do you tame the hype to focus on the most practical and impactful applications?

This is a challenging question and Further is continuously testing new AI tools and possibilities. But non-profits typically have limited resources and can benefit from a strategic and structured approach to taming AI that balances risk against opportunity in an acceptable way.

Here are some considerations and best practices you may want to take into account:

  1. Preserve Creative Integrity
    • Use AI tools like Adobe Firefly or Canva Magic Studio for ideation and prototyping, not final storytelling.
    • Frame AI as a tool to enhance human creativity, not replace it.
    • Ensure final content reflects mission values and emotional nuance.
  1. Label AI-Generated Content Transparently
    • Clearly disclose when content is AI-assisted or fully AI-generated.
    • Use metadata tools like Adobe’s Content Credentials to embed provenance.
    • Consider promoting “AI-Free” content to reinforce authenticity.
  1. Fine-Tune AI Models with Mission-Aligned Data
    • Train models using your own appeals, donor stories, and brand guidelines.
    • This ensures AI outputs reflect your unique voice and values.
  1. Enhance Donor Experience with Personalization
    • Deploy AI to tailor website content, donation forms, and email campaigns.
    • Tools like Fundraise Up and Optimizely One can dynamically adjust based on user behavior.
    • Personalization should feel intuitive and respectful—not intrusive.
  1. Use Conversational AI Responsibly
    • Consider implementing chatbots to handle FAQs and guide donor journeys.
    • However, you should disclose when users are interacting with AI and offer human escalation paths.
    • Avoid using bots for sensitive or regulated topics.
  1. Apply Predictive Outreach Thoughtfully
    • Use AI to identify high-potential donors, churn risks, and upgrade opportunities.
    • Prioritize re-engagement strategies based on donor signals.
    • Ensure outreach remains mission-driven and not purely performance-focused.
  1. Build Trust Through Transparent Data Practices
    • Communicate clearly how donor data is collected and used.
    • Offer easy-to-understand consent options and preference controls.
    • Use CMPs like OneTrust or Cookiebot to manage compliance and trust.
  1. Segment with Purpose, Not Just Profitability
    • Go beyond RFM models to include values, behaviors, and engagement types.
    • Use AI to create micro-segments and reduce donor fatigue.
    • Ensure inclusive outreach that doesn’t deprioritize smaller or less active supporters.
  1. Test for Bias in AI Models
    • Regularly audit models for demographic bias or exclusionary patterns.
    • Ask vendors about training data and validation methods.
    • Ethical modeling means asking who’s being overlooked—not just who’s likely to give.
  1. Translate Insights into Action
    • Use dashboards to visualize donor behavior and campaign performance.
    • Align strategy with what the data reveals—e.g., shift channels if SMS outperforms email.
    • Share back learnings with supporters to close the loop and deepen trust.