Why Nonprofit AI Tools Are Only as Smart as Your Data

AI is everywhere. Like corporate peers, nonprofits are increasing their use of AI to gain an edge. According to BDO’s latest nonprofit benchmark survey, 97% of nonprofits are using AI tools across operations. Nonprofit tech platforms including Blackbaud and Virtuous are developing nonprofit AI tools for predictive fundraising, personalized donor outreach, automated reporting and more. AI has the potential to be a gamechanger for efficiency, but its benefits depend on the quality of your data.

In this article, we walk you through what AI-ready data looks like, why data integrity matters, and how to improve your organization’s data management to make the most of cutting-edge technology.

What Good Data Quality Looks Like for Nonprofits

While everyone can agree that data quality matters, it’s not always clear what success looks like, let alone how to get there. To assess your organization’s overall data quality, it’s helpful to break down the concept into constituent parts.

Aspects of Nonprofit Data Quality

• Accuracy: Are donations, names, and amounts correct?
• Completeness: Are required fields consistently filled?
• Consistency: Does the same donor look the same across systems?
• Timeliness: Is data current—or weeks/months behind?
• Uniqueness: Are duplicates under control?

Assess your organization’s data by considering each of the data quality aspects above. Are there areas where you can improve? Later on in this post, we’ll walk you through steps to boost data quality. For now, focus on spotting the problem to get closer to a solution.

Keep in mind that data perfection isn’t the goal. Many organizations are managing data that spans dozens of years, thousands of donors, and millions of dollars in donations. Instead of fixing every possible error in your database, focus on developing processes to ensure your data is reliable.

Why Nonprofit AI Tools Are Especially Sensitive to Bad Data

AI is a powerful tool—even more so when you understand how it works. Some of the most common forms of AI are LLMs, large language models, like Chat GPT, Claude, Copilot, and Gemini. These models rely on pattern recognition in language and usage to generate answers based on prompts.

Similarly, nonprofit AI tools in your CRM aren’t looking for truth, (that’s difficult to code for) they are looking for patterns in your data. These tools assume your data set is your reality, so errors in the data that AI uses will reappear in any AI outputs: reports, communications, summaries, insights, and more.

The data pitfalls that nonprofits are susceptible to, including duplicate donor records, incomplete constituent profiles, and improperly formatted fields, are especially difficult for AI. Without context, AI tools won’t know that Joe Gilbert and Joseph Gilbert are the same donor—and might suggest different types of outreach to both. AI will also ignore data it cannot interpret, leaving out crucial context from its outputs. In short, data can make or break your AI.

The Real Risks of Poor Data in AI-Driven Nonprofits

The strengths of AI can also be its weaknesses: its power and ease of use. If you have an AI-generated report based on incorrect data, you will get faulty insights that appear to be correct. Say your AI model predicts that donors are more likely to give within three months of volunteering, but your donors’ volunteering history is inaccurate. Your model will suggest mistimed solicitations. That’s tantamount to money left on the table.

There are greater risks to AI errors from bad data. When a board of directors receives an error-ridden report, that jeopardizes budgeting and resource allocation. When donors receive inaccurate numbers in a report on your financials, they lose trust and confidence in your organization and may dial back their support.

AI insights feel accurate; they make bad data harder to spot. Artificial intelligence can make results feel more “official.” Because machines seem less error-prone than humans, it’s easy to accept what AI gives you without questioning it, even when the underlying data is wrong. AI also adds a layer between you and incorrect data. When an AI tool is running analysis on bad data, it’s harder to catch things like mislabeled columns or missing fields – making it difficult to locate errors that could affect your output.

The Role of Integration in Creating AI-Ready Data

Think back to the goal of data quality we covered earlier. It’s about quality at scale. When data flows automatically between systems, you eliminate manual work, reduce errors, and avoid gaps that quietly hinder reporting and decision-making.

That’s why integration matters – solutions that automatically map, route, and transform data ensure information lands in the right place, in the right format, every time. A strong integration solution should do more than move data between systems; it should actively support accuracy, consistency, and usability as your organization scales, for example:

• Reduce manual data entry for error-free information
• Enforce consistent mappings so its usable across systems
• Create a single source of truth, reducing time spent looking for data or reconciling inconsistences across platforms
• Enable real-time or near-real-time data flow for frequent reporting and insights

With a proper integration solution connecting your entire tech stack with your CRM, you can ensure you’re working with reliable, AI-ready data. Learn about popular integration solutions for nonprofits.

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How Nonprofits Can Start Preparing Their Data for AI Today

Now that you know the importance of data that’s AI-ready, let’s talk about how you can get there. Here are five steps for getting your data in shape.

1. Identify systems of record for key data: Think about digital fundraising, email marketing, events, volunteer management and other platforms with important data on your constituents.
2. Audit data quality in one high-impact area: Is your donor data accurate? Volunteer history? Event attendance? Learn where the gaps are.
3. Reduce duplicates before applying AI: Make sure Joe, Joey, and Joseph Smith’s records are merged so you can have a greater degree of trust in your data.
4. Document data definitions and ownership: Store information on where data is coming from and how it should be mapped to your CRM.
5. Strengthen integrations before layering intelligence: Find a tool to help you connect data across systems, tailored to your organization’s needs

Conclusion: AI Success Begins Long Before You Turn It On

AI has the power to greatly accelerate your operations. Your data determines whether that acceleration will help or hurt you. Nonprofit leaders who develop a data strategy that ensures freshness and reliability will be able to reap the benefits of AI while those who don’t will be prone to risks.

Develop a data plan that ensures all five aspects of data quality. From there, assess your current processes to identify gaps and opportunities. An integration solution can be your best asset for data quality: explore options that are best from your organization.

Remember, AI is only as good as the data that powers it. By prioritizing data, you’re prioritizing a healthy, viable tech stack that scales with your mission!