How advancement teams should respond in 2026
Artificial intelligence has arrived in nonprofit advancement with a mix of urgency and ambiguity. Boards ask about it. Vendors sell it aggressively. Leaders feel pressure to do something with it—often before they are entirely sure what problem they are trying to solve.
That pressure is understandable. AI is quickly becoming embedded in nearly every professional system, and advancement is no exception. But the danger for fundraising teams is not falling behind technologically. The real risk is adopting AI in ways that amplify existing dysfunction rather than strengthening donor trust.
There is a lesson most advancement leaders have already learned the hard way through CRM implementations: technology does not fix broken processes; it amplifies them. Ineffective workflows do not disappear when new tools are layered on. They become more visible, more expensive, and harder to ignore. AI operates under the same rule, only faster and at greater scale.
Used intentionally, AI can help organizations scale impact without scaling chaos. Used carelessly, it accelerates confusion, inconsistency, and risk. Donors feel the difference, even if they never see the technology itself.
Donors want value, not novelty
At the end of the day, donors are making an investment. They want confidence that their resources are being used wisely, efficiently, and in service of real impact. Personalization, efficiency, and transparency are not abstract preferences; they are signals donors use to judge whether an organization is a good steward of that investment.
This is where conversations about AI often go sideways. AI is sometimes framed as the opposite of donor‑centric practice, as if technology and care exist on opposite ends of a spectrum. Donors are not evaluating whether an organization feels high-tech or low-tech. They are evaluating whether it is worthy of their trust.
Efficiency matters because inefficiency is not neutral. Missed follow‑ups, duplicated work, inconsistent communication, and overextended staff all signal risk. Donors may not articulate these concerns explicitly, but they experience them as friction and uncertainty. An organization that cannot execute smoothly struggles to inspire confidence, no matter how compelling its mission.
When AI helps reduce waste, improve follow‑through, and allow staff to focus on higher‑value work, it supports donor‑centric outcomes. When it is used for novelty’s sake, or dropped into weak processes, it does the opposite.
The false choice between AI and donor‑centricity
AI is often framed as being at odds with donor‑centric fundraising; a frame that misunderstands both donor expectations and how the technology is used.
Donor‑centricity is not defined by how many humans touch a process. It is defined by whether donors receive:
- Timely and relevant communication
- Consistent stewardship
- Evidence of impact
- Confidence that their investment is being handled responsibly
Manual effort alone does not guarantee any of these outcomes. In fact, excessive reliance on manual processes often undermines them. When staff are buried in administrative work, donor experience suffers—not because staff don’t care, but because the system is poorly designed.
AI creates value when it enables care and erodes trust when it tries to imitate it.
Technology amplifies what already exists
This is where leadership judgment matters most.
Anyone who has overseen a CRM implementation has seen the pattern: longstanding inefficiencies suddenly surface, and frustration gets directed at the tool. Technology is doing exactly what it was designed to do by making the organization’s processes visible at scale.
AI functions the same way. If an organization has unclear ownership, inconsistent data practices, or poorly defined workflows, AI will not correct those issues. It will magnify them. Faster outputs simply mean faster mistakes.
This is why AI readiness is not a technical question; it is an operational one. Before asking how to use AI, advancement leaders need to ask whether their underlying processes are sound. Without that foundation, AI adoption becomes a force multiplier for chaos rather than clarity.
AI as a collaborator, not a substitute
The most productive way to think about AI in advancement is as a collaborator rather than a replacement.
A collaborator does not make final decisions. It prepares, organizes, surfaces patterns, and removes friction so humans can focus on judgment, strategy, and relationships. This framing preserves what is most valuable about advancement work while acknowledging the realities of scale and complexity.
AI excels at tasks that are repetitive, high‑volume, and pattern‑driven. Humans are not particularly good at this kind of work, and asking them to do it extensively drains energy from the areas where they add the most value.
In other words, AI does the sorting so humans can do the caring.
This distinction is not philosophical; it is practical. Sorting is about data, probability, and consistency. Caring is about context, discretion, and trust. Confusing the two is where AI adoption goes wrong.
Where AI adds real value in advancement
When aligned with donor value and organizational readiness, AI can be a powerful support tool. The most effective use cases tend to be behind the scenes—quiet improvements that donors experience indirectly through better execution.
AI is well suited for:
- Segmentation and analysis, where large datasets can be examined for trends that inform strategy
- Workflow automation, including data entry, reporting, scheduling, and internal coordination
- Content drafting, when treated as a starting point that staff refine with judgment and voice
These applications reduce administrative drag and create space for staff to focus on mission‑critical work. Importantly, they improve consistency and responsiveness—qualities donors associate with competence and trustworthiness.
What AI should not be used for is just as important. Donor‑facing relationship decisions—when to solicit, how to steward, how to navigate sensitive dynamics—require human judgment. Expressions of gratitude, empathy, and accountability are not broken processes in need of automation. They are moments where authenticity matters most.
Using AI to replace those moments does not feel innovative to donors; it feels hollow.
Efficiency is a donor promise
Efficiency is often discussed internally as a staffing or budget concern. From a donor’s perspective, it is something else entirely.
Efficiency signals that an organization can handle growth responsibly. It reassures donors that additional investment will lead to greater impact, not greater disorder. This is why the ability to scale impact without scaling chaos matters so deeply.
AI supports this promise when it strengthens execution rather than distracting from it. When staff time is freed from low‑value tasks and redirected toward strategy, stewardship, and learning, donors benefit—even if they never know AI played a role.
In this sense, AI is not in opposition to donor‑centric practice. It is one of the tools that can make donor‑centricity sustainable at scale.
Transparency builds trust before questions are asked
As AI use becomes more common, donor expectations around transparency are rising. Silence is no longer neutral. Donors increasingly assume technology is being used with their data, and they want reassurance that it is being handled thoughtfully.
Clear, plain‑language data governance and AI‑use statements are becoming a baseline expectation. These statements do not need to be technical. They should answer simple questions: What data do we collect? How do we use it? Where does AI fit in? What do we not use AI for?
Transparency does more than reassure donors. It creates internal alignment. When boundaries are explicit, staff are better equipped to use AI responsibly, and leaders are less likely to be surprised by unintended consequences.
In a trust‑based sector, clarity is a strategic asset.
Lightweight policies and real training matter
One reason nonprofits hesitate to address AI formally is fear—fear of getting it wrong or locking themselves into policies that won’t age well. The answer is not to avoid policy, but to keep it simple and principle‑driven.
Effective AI policies focus on intent rather than tools. They clarify where AI can support work, where human oversight is required, and who is accountable for outcomes. They emphasize review, data protection, and judgment—not technical minutiae.
Training is equally important. Staff need guidance on how to use AI responsibly, how to verify outputs, and how to recognize situations where AI is inappropriate. Just as importantly, training creates space for staff to raise concerns and surface risks early.
Together, policies and training normalize intentional use rather than reactive experimentation.
What advancement leaders should take away
AI will not build donor trust on its own. But used intentionally, it can strengthen the systems that trust depends on.
- Donors want value, not novelty. Efficiency and effectiveness are signals of good stewardship.
- AI is not the opposite of donor‑centric practice; inefficiency is.
- Technology amplifies existing processes; AI will not fix what is broken.
- Treating AI as a collaborator clarifies its role and protects human judgment.
- AI should do the sorting so humans can do the caring.
- When used well, AI helps organizations scale impact without scaling chaos.
The leadership task is not to chase tools, but to design work thoughtfully. Donors are not investing in technology; they are investing in an organization’s ability to execute with integrity and focus.
AI can help meet that expectation but only when it is used to strengthen relationships, not replace them.
-
Advancement Technology Leader
Barb Fischer is a transformational leader with over 20 years of experience in driving operational excellence and spearheading strategic initiatives within the nonprofit and technology sectors. At the University of Toledo Foundation, she led the CRM conversion project, aligning it with organizational needs to enhance data availability.
View all posts
