AI for Nonprofits: Real-World Uses, Risks, and Best Practices
In our interactions with clients, we’re starting to see more nonprofits and other social enterprises adding AI into their workflows, with (usually) positive results.
Today, we’ll look at the ways nonprofits use those tools, so you can get a better idea of how AI might benefit your own organization. We'll also look at the risks associated with AI platforms, and how best to avoid them!
AI Use Cases for Nonprofits and Social Enterprises
We see nonprofits using AI in three main areas: Marketing, fundraising, and internal operations.
Nonprofit sector statistics compiled by Nonprofit Tech for Good indicate that those are among the most common use cases.
Here’s how AI is being used in each of those areas:
Marketing and Communications
Drafting Content: Teams use tools like ChatGPT and Claude to write first drafts of newsletters, social media posts, blog posts (ahem 😉), and press releases.
Graphic Design: Image generators and platforms like Canva help create presentation slides, flyers, and digital graphics quickly. You’ve probably seen some of our graphics that fit that description.
Transcription: Speech recognition tools convert audio from meetings or donor calls into notes and public appeals. This is a big one. What a time saver!
Fundraising and Donor Relations
Personalized Outreach: AI can analyze past giving history to tailor donation requests and thank-you notes for specific supporters. Non-AI automation can help here too.
Prospecting: Predictive models identify potential major donors and forecast the best times to reach out.
Chatbots: Virtual assistants can answer routine donor questions and guide users through online donation processes 24/7.
Operations and Administration
Grant Writing: AI assists by summarizing funder requirements and structuring initial grant application drafts. Be sure to review the summaries and recommendations and keep an eye out for hallucinations!
Task Automation: Staff use AI for scheduling meetings, transcribing notes, and managing volunteer sign-ups.
Data Analysis: Tools process large spreadsheets and generate data visualizations to measure social impact. Again, we’ve heard tales of hallucinated numbers, so be vigilant if you try this out.
Hopefully those use cases gave you a few good ideas. Now let’s look at the risks that come along with AI tools.
AI Risks for Nonprofits and Social Enterprises
While artificial intelligence offers healthy productivity boosts, nonprofits must navigate unique reputational, ethical, and legal risks.
Safeguarding trust is paramount for mission-driven and donor-funded organizations.
Nonprofits should proactively avoid five major risks when adopting AI tools:
1. Data Leaks of Sensitive Donor and Beneficiary Information
The most dangerous error is inputting confidential data into general-purpose, open-source LLMs (like free versions of ChatGPT or Claude).
The Risk: Most free tools use input data to train their models. Leaking names, addresses, credit cards, or sensitive beneficiary case notes violates data privacy laws and breaks stakeholder trust.
The Fix: Strip out all personally identifiable information (PII) before using AI. For handling sensitive data, invest in dedicated, closed enterprise AI systems with strict data privacy terms.
2. Replacing Genuine Human Relationships
Donors give to causes because they connect with human stories and shared values.
The Risk: Relying entirely on automated chatbots or fully AI-generated emails can make communication feel sterile and transactional. Over-automation risks alienating long-term supporters.
The Fix: Use AI to draft structural outlines or analyze data trends, but ensure a human always edits and finalizes donor-facing communications.
3. Unchecked Algorithmic Bias
AI tools learn from historical data, which inherently carries human biases. Yes, that does sound like the computers are blaming us for their mistakes (it’s still true though).
The Risk: If an organization uses AI predictive models to screen grant applications or identify "high-value" donor prospects, the algorithm may unintentionally favor specific demographics while systematically sidelining diverse communities.
The Fix: Regularly audit AI outputs for bias. Avoid allowing AI to make automated, final decisions regarding funding, hiring, or community resource allocation.
4. "Shadow AI" and Lack of Governance
If an organization fails to set clear rules, employees and volunteers will often experiment independently to save time.
The Risk: This leads to "Shadow AI" - unsanctioned use of various tools across the organization that IT and leadership cannot monitor for security compliance.
The Fix: Establish a formal Nonprofit AI Usage Policy. Define permitted and prohibited use cases, mandate human oversight, and train staff on safe tools.
5. Legal Pitfalls and Factual Errors
Generative AI tools are notorious for "hallucinating" false facts or creating intellectual property issues.
The Risk: Publishing inaccurate statistics in a newsletter damages credibility. Furthermore, relying on AI to write contracts, grant disclaimers, or bylaws can expose the organization to massive legal liability.
The Fix: Fact-check every AI output against trusted secondary sources. Always have a human attorney review legal documents rather than relying on an LLM.
How Are You Using AI?
Here at Blue Fox, we continue to experiment with AI throughout the organization.
This blog post, for example, was drafted using AI. It was revised, edited, and reviewed by real, live humans though! 🤖🤝🙎♂️
To minimize risks, we shifted our AI use away from some of the more popular models over to CoPilot because Microsoft offered clearly defined data security policies. As an accounting firm, we can’t operate with anything less, and, if you want our opinion, most nonprofits probably shouldn’t take that risk either.