AI in Advancement: Where Things Stand

Taking a look at AI in advancement in the context of donor trust and building connections.

Anwesha Kiran

Published: 

July 31, 2026

Updated: 

AI has arrived in advancement faster than the field has had time to make space for it. It’s influencing advancement ways both large and small, showing up everywhere from internal operations to donor engagement.

Institutions are experimenting, donors are forming opinions, and best practices are still taking shape. The most relevant question emerging seems to be how institutions can make use of this technology without weakening the very relationships they’re meant to nurture. As institutions try to find these answers, recent research offers an early look at what's changing, what's holding steady, and what donors are asking for.

AI in Advancement: Where Things Stand in 2026

Where do advancement teams stand?

Most institutions are still in an experimental phase with AI. Some teams have found workflows where it clearly helps. Others are still figuring out where it fits or whether it fits at all. That's the backdrop advancement teams are operating in, regardless of whether their institution has a formal AI policy in place yet.

Fundraising staff, for the most part, have already moved past that experimental stage individually. Almost two-thirds of nonprofits and foundations already report using AI in their work, according to a 2025 Center for Effective Philanthropy survey.

From: How Foundations and Nonprofits are Thinking About and Using Artificial Intelligence

Governance hasn't kept pace with that adoption: a 2026 report found 47% of nonprofits have no AI policy at all.

This gap between individual use and institutional guidance shows up clearly in how nonprofits handle disclosure. Only 15% of nonprofits disclose their use of generative AI tools, even though a majority of donors say they want to know when AI is involved. Blackbaud's donor research puts a number on that expectation: 76% of donors want to know when and how an organization is using AI. What their research also found is that transparency, not AI use itself, is what protects donor trust.

How do donors feel about AI?

The data above points to a stance that isn’t necessarily for or against AI: donors are simply asking to be told when it's in use. This reframes the ethical question for advancement, taking it from adoption itself to how openly that adoption happens.

Donor opinion on AI isn't uniform

A 2024 study led by Nathan Chappell and Cherian Koshy, surveying more than 1,000 donors, found that younger donors (ages 18-44) show more openness to AI-driven innovations, while donors 60 and older expressed a stronger preference for maintaining traditional human interaction.

This split is particularly relevant to advancement, since most alumni bases span every one of these generations at once. A single approach to AI use will land differently depending on who's receiving it, which is all the more reason disclosure and consent need to be built into institutions’ AI strategy from the start.

Why advancement carries different stakes

Most of the AI conversation in higher ed treats advancement like any other back-office function: a place to automate reporting, speed up admin work, save staff time. But advancement carries a distinct responsibility on top of that. It's the function that manages an institution's most valuable, hard-earned asset: trust with the people who choose to care about it.

  • AI drafting a grant report or cleaning up a spreadsheet stays invisible to the donor. The output gets judged on accuracy and speed alone, and the work itself is mechanical.
  • AI touching a donor-facing email or a segmentation decision operates inside a relationship the institution has spent years building. Getting it wrong here could leave a donor feeling like a data point instead of a person, which affects their giving decision.

That's what makes the stakes in advancement multi-dimensional, and harder to get right than in most other back-office use cases.

A predictive model flagging alumni at risk of lapsing is a promising, emerging use of AI in this space. Applied without care to how that flag gets acted on, the same model can turn a relationship into a transaction.

What is driving and blocking adoption?

The main driver for adoption right now seems to be time saved. Teams are using AI to handle the repetitive parts of the job, drafting, data cleanup, first-pass research, so staff time goes toward the relationship work that really does require a human. Cost plays a role too. Advancement teams are on the leaner side, relative to the scope of their work, and AI gets pitched internally as a way to do more without adding headcount.

On the barrier side, sensitive data handling is the concern that comes up most. Advancement holds some really sensitive data an institution has: giving history, wealth indicators, family relationships, or personal details shared over years of stewardship. That sensitivity raises the bar for what responsible AI use means in advancement specifically, and it shows up clearly in the numbers. Data privacy and security are the top adoption barrier in higher ed, cited by 61% of individuals and 56% of institutions.

From: How Foundations and Nonprofits are Thinking About and Using Artificial Intelligence

Data privacy and security concerns aren’t the only reservation teams have with AI adoption. The same survey found newer concerns emerging too: environmental impact now shows up among the top three barriers for more than 1 in 5 respondents, and concern about AI-related role elimination nearly doubled year over year, from 7% to 14%. Training hasn't kept pace with any of this. Concerns around training will likely continue to shape reluctance around embracing AI.

Put together, this paints a picture of willingness to adopt but also one of uneven adoption. Nonprofit-wide, 92% of organizations report using AI in some capacity, with a disconnect: only 7% agree that it’s made any significant impact.

Headline numbers from Ellucian’s 2025 State of AI in Higher Education Data

Advancement specifically is tracking a bit ahead of that nonprofit-wide baseline. Ellucian's third annual higher ed AI survey placed alumni relations and advancement among the field's Emerging Adopters, at roughly 59 to 60% momentum.

Within fundraising, AI shows up across:

  • Donor communications: 85%
  • Automation: 72%
  • Predictive analytics: 67%

Leaders credit AI adoption specifically in operational improvement and personalized engagement specifically, with only about 30% agreeing that there has been any direct impact on revenue.
The nuance remains that advancement teams are using AI to free up time for the parts of the job that actually move donors: the relationships. In this revelation lies the congruence between what donors expect and what advancement teams are trying to achieve.

Where AI fits into modern advancement

Advancement is working through AI adoption for the first time alongside every other part of the institution with no established playbook to follow. The leaders shaping this work right now are figuring out what responsible use looks like as they go. These are the open questions the field is sitting with and working through.

The problems AI shouldn't be expected to solve

Alumni giving participation has been declining for years. It's easy to assume the answer is better tools: smarter segmentation, more personalized outreach, sharper messaging. But the 2026 National Alumni Survey suggests the problem is something else entirely.

Younger alumni aren't giving less, they just give elsewhere rather than strictly to their alma mater.
With motivations shifting, the strongest predictor of giving has very little to do with their age and their capacity to give. It's whether they feel connected to their alma mater. Alumni who feel very connected are 24 times more likely to donate than those who feel disconnected.

More than convincing younger alumni to give, we should think along the lines of "How do we help more of them feel like they still belong?" Because the generosity is already there! It’s just a matter of establishing the connection, or rekindling it, rather.

If connection is the real problem, AI can help institutions:

  • Tell their story at scale and make it feel personal to each recipient
  • Catch drift early: predictive tools can flag alumni pulling away before they lapse, so teams can re-engage in a timely manner

The actual relationship-building work remains a deeply human function.
AI can widen the aperture on who gets noticed and when, but it can't replace the judgment and warmth that make an alumnus feel like an individual rather than a segment.

The line between assistance and substitution

For many advancement professionals, AI brings a lingering concern: where does this eventually lead; is it just another tool, or does it change the role itself?

So far, AI adoption has been more  practical rather than existential:

  • Institutions are using AI to cut administrative work, move faster, and support more personalized engagement. They’re expanding what teams can do with the people they already have.
  • Donors back this up as well. Most are comfortable with AI having a role to play, as long as it's transparent. Honesty matters more to them than whether AI touched part of the process.
  • AI comes in at the implementation stage: summarizing donor research, surfacing patterns, and touching anything that buys back time for real relationship building.

Toeing the line between assistance and substitution is a sensitive task, but one we believe teams are very much capable of doing. Some of these decisions will vary by institutional requirements. But the consensus is that AI works best in a supporting role while the relationships themselves must stay distinctly human.

Transparency and consent

For institutions still figuring out their AI strategy, there's no need for a sweeping rollout. Most teams are better served starting internal use cases (research, reporting, drafting, data analysis) and expanding as they get comfortable.

But as AI moves closer to donor-facing work, transparency matters most. Chappell and Koshy's study found 93% of donors rate transparency about AI use as "very important" or "somewhat important," which leaves little room for  disclosure to be optional.
Clear disclosure and real consent should be one of the main concerns in policy and cannot be treated as an afterthought. Across the research, one pattern holds: AI adoption is moving fast, donor confidence is catching up more slowly, and trust and transparency are what close that gap.

As for the decline in alumni giving, it isn't really about generosity at all. It's about connection and that's where AI can do the most good: helping teams uncover more relevant stories, personalize outreach, and catch signs of drift before alumni disengage entirely. The relationship itself still belongs to the people doing the work. Few advancement professionals, and even fewer donors, want to hand that part to a machine.

Where this leaves advancement

Watching this play out at CASE Summit this year, one thing stood out: the conversation centered on using AI well without losing the human touch.

That tracks with the data. AI adoption is already underway, even if unevenly and without formal policies at many institutions. Donors have made clear that transparency matters more than AI's mere presence. And the core challenge advancement teams are solving: helping alumni feel connected enough to stay engaged and give, is still fundamentally a human one.

AI can spot patterns, reduce admin work, and make personalization easier at scale. It creates more space for relationship-building, but it can't do the building itself.

The tools will keep improving, and institutional policies will catch up. What won't change are the pillars advancement has always depended on: trust, real connection, and people who know how to build both.

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Anwesha Kiran

Anwesha is an educator and pedagogy enthusiast, passionate about the transformative impact of education, kindness, and creativity on individuals and communities.

As an artist, she brings a unique perspective to her work and is committed to inspiring growth, empathy, and understanding

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