Nonprofits are increasingly exploring AI to enrich their workflows. Fast Forward’s 2025 AI for Humanity Report indicates that the current top AI use cases are grant writing and applications (77% of respondents) and content creation and marketing (77% of respondents), which often involve using generative AI tools like ChatGPT and Gemini to streamline these processes.
If your organization has already started working with AI and is ready to take its usage to the next level, consider leveraging AI to better manage donor data.
Perhaps the legacy solution you’ve always used to manage donor data is no longer effective, or you’re lacking new opportunities for deeper donor engagement. AI can help you keep pertinent information organized so you can use it to fuel stronger donor communications and campaigns.
To help you get started, we’ll walk you through a nonprofit CRM cleanup and enrichment sprint powered by AI. By implementing a similar routine, your organization can start using AI to organize donor data and save time that you can allocate to other areas.
Phase 1: Audit Data Quality
If the donor data you have stored is incorrect, outdated, or improperly formatted, it will be difficult to get in touch with your supporters, and you may end up wasting resources.
Start your CRM cleanup sprint by cleansing your donor data. Your CRM should have built-in AI capabilities to help with:
- Deduplication. Duplicate entries can complicate supporter management, leaving you with an incomplete view of each donor and potentially leading you to contact the same constituents multiple times. Top nonprofit CRMs use AI to identify duplicate records and notify you about them. Then, you can review them and decide whether to merge them or not based on how confident you are that the separate profiles match.
- Validation. Many organizations conduct National Change of Address (NCOA) processing to validate donors’ mailing addresses and standardize address formats, allowing them to confidently send direct mail campaigns and promote local events. Additionally, you may leverage a deceased supporter suppression service to prevent your nonprofit from reaching out to donors who have passed. The right donor database can automate these processes, keeping your constituent information up to date.
Additionally, if you have payment information on file for recurring donors, your CRM may automatically update that data, allowing supporters to continuously give and your nonprofit to receive consistent cash flow. Check with your provider to see what options are available.
Phase 2: Data Enrichment
Now that your data is clean, it’s time to enrich it with additional insights. For instance, you may use wealth screening to identify potential major donors based on their giving capacity, propensity, and affinity.
While you may upload your clean donor data to an external tool, some CRMs have integrated wealth screening features that automatically pinpoint donors with high giving potential and enrich their profiles with generosity data so you know who to reach out to for larger asks. Top platforms can even identify high-capacity prospects not in your CRM, whose giving history and affinity make them ideal candidates for major contributions.
Additionally, your CRM should use AI to flag incomplete records, ensuring you can reach every donor in your database. For example, you may receive a notification that you have 52 records missing an email address. From there, you can reach out to those donors to confirm their contact information or work with an external data append provider to obtain that data.
Phase 3: Segmentation
Next, activate your newly cleaned, enriched data through segmentation. Rely on your CRM’s AI-powered segmentation tools to suggest donor segments and group them accordingly. While some segments may seem obvious based on the data at hand and your nonprofit’s goals, AI can detect more covert patterns and develop niche segments that allow for hyper-personalized targeting.
Let’s say you have a group of local supporters with high giving capacity and a history of attending your nonprofit’s annual 5K. Instead of manually combing through your database, your CRM can automatically recognize these similarities, create a segment for these supporters, and recommend that you invite them to sponsor next year’s event.
Phase 4: Standardization
To prevent a buildup of dirty data in the future, you need to standardize your AI-powered data management procedures and implement them across your entire team. This process may include:
- Assigning responsibilities. Everyone should understand their role in maintaining a clean and actionable database. For example, you may have someone on your marketing team review AI-generated content segments to ensure they’re relevant to your nonprofit’s goals.
- Training your team on AI tools. Host training sessions that help team members understand how to trigger your CRM’s AI capabilities and how these tools can simplify their work. Provide tips for how they can maximize these features.
- Mandating regular data audits. Whether monthly or quarterly, consistently checking your database for incorrect, outdated, disorganized, or incomplete data helps you catch and resolve problem areas before they become larger issues. Note common problems so you can make a plan to fix them more proactively in the future.
Additionally, consider adding AI-validated fields to your donation page, volunteer sign-up page, and any other forms. That way, you can ensure the data you collect is accurate and properly formatted. For example, you can use an address autocomplete field to standardize address information that enters your CRM.
Organizations that want to adopt a data-centric strategy and get ahead of the curve need to find innovative ways to cleanse and manage their donor data. By incorporating AI into your data management practices, you can gain powerful supporter insights and save time simultaneously, giving you more freedom to use this newly organized data for higher-value activities such as forming stronger donor relationships.
After introducing AI into your data management process, iterate on your approach as needed. Track and analyze results from campaigns powered by your newly cleansed data to identify successes and areas for improvement.
