Humans do not behave rationally—even when they have more information.
The last few years of generative AI adoption have made this clear.
We now live in a world where knowledge that once took years to accumulate is available instantaneously.
Yet human behavior—which can be emotional, relational, and at times, messy—has not accelerated at the same pace. The widening gap between information speed and human decision-making may be the most important force reshaping fundraising right now.
The bottleneck is behavior, not software.
The fundraising sector is built on processes optimized for a world that no longer exists. Our systems, staffing models, and “best practices” were designed for an era of slow-moving information, predictable donor behavior, one-way institutional communication, and institutions as the primary source of expertise. AI has not disrupted those systems; it has simply revealed how outdated many already were.
So, what does this mean for us right now, especially as organizations grapple with resourcing, complexity, and working at the top of license (i.e., doing the work aligned with one’s training and expertise)?
The Reality Check: AI isn’t the Task Wizard We Expected
Three years ago, many imagined generative AI as an all-purpose task engine: a tool that would take a prompt, spin up a fully complete deliverable, and replace whole categories of operational labor. But the lived experience has been more subtle and more revealing.
Large language models (LLMs) are accelerators, not substitutes. They make people who are already good at a task faster and more capable. They improve drafting, accelerate research, and strengthen analysis—but their output is only as good as the human shaping it. This aligns with industry patterns seen in the Fundraising Effectiveness Project, CASE, and BWF client work: Organizations with strong human expertise see the greatest gains from technology.
At the same time, AI exposes uncomfortable truths about which tasks we have historically defined as “expert work.” If a model can generate a prospect briefing, a stewardship message, or a segmentation outline in 20 seconds, then the true value of a fundraiser is not in producing that deliverable, it is in interpreting, contextualizing, and acting on it.
Knowledge is now free. Execution is the only differentiator.
The Speed of Knowledge is Rewriting “Best Practices”
One of the most profound changes introduced by AI is the dramatic acceleration of operational knowledge. What once took months to benchmark, debate, or learn can now be generated instantly. Best practices update overnight. Digital engagement norms shift yearly. Donor expectations change faster than institutional processes can react.
This information velocity exposes a structural vulnerability: Fundraising organizations still make decisions—and change—at human speed. After all, human nature has not changed, nor have our underlying mental processes. Risk tolerance has not grown. And institutional inertia has not disappeared.
This all means that best practices are no longer something you can adopt and then deploy for five years. They are living, shifting, and adaptive. And they require change management at a level in which the sector has historically underinvested.
As BWF’s research and practice show, knowing what to do is only the first step—success comes when organizations consistently convert knowledge into action.
The Changing Ecosystem: Scale Without Losing the Human Thread
Even amid this rapid change, the nature of the fundraising ecosystem offers new opportunities. Digital fundraising accelerators—such as Donor AI-assisted warming sequences that move prospects from “cold” to “ready”—demonstrate how early-stage engagement can now be scaled efficiently.
But scale is not a goal on its own. Scale is only useful if it frees humans to do more of what only humans can do.
Relying too heavily on technical systems can create its own kind of fragility. When core processes—and even the developmental tasks that once built early-career judgment—are pushed into singular platforms or AI-driven workflows, a service outage or model failure can create new bottlenecks, halting activity in ways human capacity never would.
This is the core of top-of-license thinking in advancement. Bill Gates recently predicted that the modern workweek could shrink to 1.5-2 days of real labor, with technology increasingly performing background processes . The question for fundraising organizations is not whether AI can save staff time—it already does—but what we choose to do with the time returned to us.
If AI gives frontline fundraisers and operations staff back 10–20 hours a week, the strategic question becomes: What do we do with that time? Generate more dashboards? Attend more meetings? Or engage in deeper relationship discovery, better collaboration and mentorship, and more thoughtful portfolio management?
This is where the sector’s future will be determined.
Knowledge into Action: The Human Step Cannot be Automated
A useful metaphor is this chain: recipe → shopping list → delivery → cooking.
AI can now generate the recipe and assemble the shopping list. It can even gather and deliver the raw ingredients.
But AI cannot (yet) cook the meal. The final act—translating knowledge into action—remains stubbornly human.
In fundraising, those inherently human moments include:
- Sensing when a donor is truly ready.
- Knowing what to ask and how to ask it.
- Navigating ambiguity with confidence.
- Reading emotion, trust, and relational nuance.
- Understanding how identity, belonging, and community shape generosity.
These are the variables that drive philanthropic behavior—confirmed by research from the Lilly Family School of Philanthropy, Bekkers & Wiepking, and AFP’s donor motivation studies. AI models can shape the work but cannot embody the human element at the heart of giving.
Where We Go Next
Looking ahead, the institutions that thrive will be those that:
- Acknowledge that the old operating model is changing.
- Shift from knowledge acquisition to execution capacity.
- Invest in change management as a primary competency.
- Redeploy AI-generated time savings toward top-of-license relationship work.
- Center fundraising in the human motivations that defy automation.
Generative AI accelerates knowledge; it does not replace judgment. It expands capacity without reinventing courage. It exposes outdated processes without presuming to remedy them.
The next era of fundraising will belong to organizations that can turn free knowledge into meaningful action—led by humans who understand that the heart of philanthropy has always been, and will remain, profoundly human.
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