The AI Gap in Higher Ed Fundraising (and How to Close It)

Editor’s note: Data sourced from the Omatic 2026 Nonprofit Technology Ecosystem Trends Report, based on more than 800 survey responses from nonprofit professionals across industries. Omatic is a GiveCampus integration partner.

Half of nonprofit professionals are already using AI at work. According to Omatic’s 2026 Nonprofit Technology Ecosystem Trends Report, just three to five percent of those same organizations say AI in higher ed fundraising—or any sector—is part of a robust organizational strategy.

Fifty percent use AI. Three percent do so strategically. The gap between those two numbers is where most advancement teams are living right now—and it’s costing them more than they realize.

Why individual AI use stalls out

Advancement professionals at schools of every size have access to AI tools. Many are already using them daily: drafting stewardship emails, writing appeal copy, summarizing donor notes, generating talking points for gift officer calls.

The problem is that most of this is happening in parallel—each staff member running their own experiments with different tools, different prompts, and different workflows, with no shared inputs and no integration into how the team operates. When that’s the setup, the compounding benefits of AI never materialize. Individual productivity gains accumulate without improving the program as a whole.

The upside of a coordinated approach is substantial. Data cited in the Omatic report, shows that organizations using AI features see an average one-time donation of $161, compared to an industry average of $115. Monthly recurring donations average $32 for AI-enabled organizations versus $24 for those without.

Higher education’s version of the problem

Higher ed advancement teams face a particularly acute version of the coordination challenge. Omatic’s report calls out the pressures facing colleges and universities: 

• Re-engaging a generation of alumni with declining institutional loyalty
• Navigating decreasing enrollment
• Managing complex constituent data across advancement, alumni relations, and development teams that don’t always share systems

That last point is the crux. AI is only as good as the data feeding it. And if your donor records are fragmented across systems that don’t talk to each other—giving history in one place, engagement data in another, communication preferences in a third—AI amplifies that fragmentation.

An AI tool trained on incomplete or siloed data will score donors incorrectly, suggest the wrong ask amounts, and surface prospects who shouldn’t be in the pipeline. It will do all of this at scale and with apparent confidence, which is harder to catch than a human error.

An AI strategy in higher ed depends on data infrastructure. A coherent organizational approach requires a foundation where constituent data is complete, current, and connected across systems.

What an organizational AI strategy looks like

An organizational AI strategy embeds shared data inputs, consistent workflows, and a common framework across the whole advancement team. Closing the gap takes a handful of deliberate choices about how AI gets used. Here’s what that looks like in practice:

Shared inputs

An organizational AI strategy starts with shared data. That means prioritizing donor data integration—connecting your giving platform, CRM, and engagement tools so that the constituent record your AI is drawing from is complete, current, and trusted. When your systems are aligned, AI can identify patterns across large datasets that no human can process manually.

AI embedded in repeatable workflows

The highest-value AI applications in advancement build intelligence into recurring workflows—prospect scoring that runs automatically before every major gift officer review cycle, smart ask amounts that recalibrate based on recent giving behavior, predictive segments that identify which annual fund donors are most likely to lapse before they actually do. The goal is institutional muscle.

A common framework across the team

When different staff members are using different tools with different prompts, you get inconsistent outputs and no shared learning. A simple internal standard—agreed-upon tools, shared prompt libraries for common tasks, a process for flagging what’s working—turns individual experimentation into collective knowledge.

AI that supports the relationship

Advancement is a relationship-driven field. A donor who upgraded their gift last year wants to feel recognized for it before they’re asked to do it again. A lapsed alumna who attended three campus events this fall is worth a personal call—not another mass appeal. AI gives gift officers the context to have those conversations: who’s engaged, who’s at risk of lapsing, what they’ve given, what moved them before. The goal isn’t to automate the relationship. It’s to show up better informed.

The data prerequisite

Clean, integrated donor data is the prerequisite for any AI strategy that produces trustworthy results. Omatic’s report found that 54 percent of nonprofits identify incomplete or inaccurate data as a major obstacle to maximizing donor information—and it’s no coincidence that AI adoption and data quality keep appearing together in nonprofit technology research.

The data quality problem predates AI by decades. AI raises the stakes for solving it.

For many advancement services teams, moving data between systems is still a largely manual process—exporting from your giving platform, reformatting, importing into your CRM—which introduces errors and delays that compound over every campaign cycle. An AI strategy built on that foundation will produce recommendations your team can’t trust, which means they’ll (rightly) ignore them. 

Fix the data problem first and the AI strategy will follow. It’s a problem GiveCampus knows well—constituent data integration has consistently been one of the biggest hurdles to unlocking AI functionality for advancement teams, and it’s where we’ve invested heavily. The tools to solve it exist.

The opportunity for schools that move now

AI is the number one organizational priority for 2026, cited by 42 percent of respondents in the Omatic report. The sector has made up its mind that AI matters. The question is whether your institution will build the infrastructure to use it well, or whether you’ll end up with a collection of individual experiments that never quite compound into a strategy.

For advancement teams at colleges and universities, the schools that close the gap—that move from individual AI use to a coordinated organizational strategy—will have a real and durable advantage in donor identification, stewardship, and retention. The tools exist, the data challenge is solvable, and the strategy is achievable. This is a gap you can close.

GiveCampus helps advancement teams connect the data, solutions, and workflows that make a coherent AI strategy possible—starting with getting constituent data in cleanly, completely, and without the manual lift. Talk to a fundraising expert to see how GC Intelligence—including Smart Segments, Smart Ask Amounts, and Smart Sends—puts AI to work across your entire fundraising program. 

For a deeper look at the data behind Smart Ask Amounts, read the white paper.