1. Brainstorm use cases
You shouldn’t adopt AI—or any technology for that matter—just for the sake of having new technology. Instead, start your AI journey with specific use cases in mind.
Brainstorm overarching AI goals and how AI might solve them. For example, one of our nonprofit clients here at Heller Consulting has multiple fundraising growth goals, including increasing donor conversion rates and improving funding through constituent scoring and segmentation analysis. They are working with our team to evaluate and determine which AI capabilities could help them reach these goals.
During this step, consider AI risk and ethics. Less than half of people trust AI due to issues like bias, data insecurity, and unreliability. Plus, when it comes to nonprofit work, overreliance on AI can jeopardize the human connections with supporters. While AI tools for nonprofits improve every day, it’s essential to research and communicate these risks with your team so everyone’s on the same page and understands how to use them properly.
2. Look into what AI tools you might already have
Remember, your current tech stack likely already has AI functionality. You can dip your toe into AI by experimenting with the tools your organization’s software already offers (or might offer soon).
Some software providers have even rolled out (and continue to develop) AI functionality specifically for nonprofits. For example:
- Within Microsoft’s Copilot AI functionality, nonprofits can access specific AI capabilities for Microsoft Cloud for Nonprofit that help optimize fundraising, drive donor-centric marketing, improve constituent engagement, and identify supporters who are likely to donate.
- In addition to its Einstein AI capabilities, Salesforce’s AI solutions include Einstein for Nonprofit Cloud. It helps users complete nonprofit-related tasks like segmenting supporter lists, creating cultivation emails, designing web pages, and drafting personalized gift proposals.
- Blackbaud has embedded AI across Raiser’s Edge NXT and its CRM platforms through features like Prospect Insights, which predicts major gift likelihood and suggests the next best actions. The advanced Prospect Insights Pro add-on delivers predictive modeling for planned giving and deeper wealth analysis. Together, these tools are part of Blackbaud’s broader Intelligence for Good strategy, aimed at giving nonprofits actionable recommendations and automated efficiencies.
3. Analyze the state of your data
AI relies on large, accurate datasets to function. AI tools for nonprofits craft responses based on the data they’re trained on. Before using AI, answer these questions about your data:
- What data do you have to work with? As you think about use cases for AI, consider whether you have the data points needed to reach your goals. For example, if you want AI to predict which donors are most likely to upgrade their gifts, you’ll need past giving histories, engagement metrics (like event attendance or email opens), and demographic information to train the model.
- How clean is your data? AI is only as good as the data that feeds it, so if you don’t have reliable data, then you can’t count on AI to work accurately. If you have incomplete, duplicate, or outdated data, it’s time to start getting it in shape. It’s also a good time to develop or update your data governance strategy to ensure your data stays in good order. For instance, if one supporter appears in your database three times under slightly different names, an AI tool might treat them as three separate people.
- Where is your data? If your data is scattered in multiple software systems, then you need a plan to compile it in some way so that your AI tools have access. For example, if your program data lives in one platform, your donor data in another, and your marketing data in a third tool, an AI model won’t see the full picture of supporter behavior until those systems are connected through integrations or a central CRM.
4. Develop an initial AI strategy
Once you’ve completed the first steps, it’s time to outline how your nonprofit will approach AI in greater detail. This doesn’t need to be a full-blown roadmap, but it should set a direction for how you will explore, test, and evaluate AI. In addition to the previously mentioned details, a starter strategy might:
- Prioritize pilots. Choose one or two low-risk use cases (like AI-assisted donor thank-you messages or predictive email send times) to test first, so your team can learn by doing.
- Assign ownership. Designate a staff lead or small working group to oversee AI exploration, capture lessons learned, and share updates with leadership.
- Create policy. Create an AI policy that guides your staff on how to use AI, with guardrails that ensure its use is consistent with your organization’s technology, security, and privacy policies. Continue to update your AI policy as your organization and the technology evolve.
- Set evaluation criteria. Define how you’ll measure success for each pilot, whether it’s saved staff time, increased conversions, or stronger constituent engagement.
During this step, you might want external expertise to guide you. Team Heller Consulting offers an AI Preparedness Assessment for Nonprofits that can guide you through responsible AI adoption. This assessment is designed to help nonprofits:
