Resources at the Speed of Need: The AI Imperative for Fundraising
The system that connects generosity to need is under strain.
Recent benchmarks show the divide clearly: While 53% of organizations grew fundraising revenue last year, among those that fell short of their targets, 51% cited not being adequately resourced as the primary reason—highlighting how fragile today’s models become when staff capacity is stretched.
That strain is only intensifying. Nonprofits are navigating a significant labor shortage and elevated turnover, particularly in fundraising roles. The Bureau of Labor Statistics projects that an average of more than 10,000 fundraising positions need to be filled annually. A 2023 study done by the National Council of Nonprofits found that nearly three quarters of organizations had open roles, with positions requiring public interaction (like fundraising) among the hardest to fill. And turnover is costly: SHRM estimates that replacing a fundraising professional can cost up to 200% of their salary in direct and indirect costs.
It’s no surprise, then, that six in ten social impact organizations cite revenue as a top concern over the next three years, with nonprofits especially anxious about their ability to sustain income.
But what if securing resources and activating around shared purpose became the most straightforward part of an organization’s mission? At Blackbaud, we envision a future where resources are unleashed at the speed of need. Achieving this means removing obstacles and reimagining work processes.
We believe AI represents the greatest potential unlock of mission acceleration in the history of the sector. Yet the ability to achieve meaningful impact is constrained by two persistent barriers:
- Lack of clarity or confidence in how to use AI effectively
- Fear that adoption of AI could erode constituent trust
Our most recent Blackbaud Institute research on Bridging the AI Effectiveness Gap shows that while 85% of social impact professionals are using AI, far fewer feel confident it’s actually helping or delivering clear ROI.
About 10% of organizations have moved beyond experimentation to systemic, transformational AI use, and they’re seeing the benefits—from revenue growth to donor retention and staff productivity. However, most organizations in the social sector are using AI in fragmented, individual ways, achieving only limited organizational impact and missing out on the more transformational shifts the technology enables.
Underlying all of this is a deeper issue: trust. Fundraisers must trust systems enough to rely on them when it matters most. Supporters must trust organizations to use AI responsibly. Without that bidirectional trust, even accurate systems can weaken relationships instead of strengthening them.
Trust isn’t a nice‑to‑have. It’s the gating factor.
Our research shows that donors are okay with AI. In fact, 71% of donors say they’re as comfortable or more comfortable with social impact orgs using AI than for-profits. They just want visibility: 76% expect transparency, but only 26% of professionals say their organizations disclose their use publicly. And from a staff perspective, while the vast majority of fundraisers are using AI, only about a third believe that it’s delivering strong organizational results.
When supporters don’t trust how systems operate, it impacts their willingness to give. When fundraisers don’t trust what software recommends (or can’t understand why) they ignore it, override it, or revert to manual work. That’s why responsible AI isn’t optional in fundraising; it’s essential.
The AI opportunity is unlike any other in the history of fundraising, but realizing it requires more than new tools. I believe organizations must make four practical mindset shifts about the role of technology to move from AI experimentation to real mission impact, all while preserving trust:
- From data as information to data as fuel for impact
- From systems of record to engines of context-aware intelligent action
- From proprietary platforms to optionality and openness
- From custom-bespoke implementations to assisted evolution
The AI Opportunity
Technology unlocking new efficiencies in fundraising isn’t a new idea. We’ve watched digital transformation unfold in waves: first organizing information, then connecting systems, then automating repeatable tasks. And the signal is clear: early adopters tend to outperform. Blackbaud Institute research links early technology adoption to better outcomes: 41% of early adopters exceeded their fundraising goals and 57% reported higher total revenue, compared to 31% and 46% overall.
So what’s different now? AI doesn’t just make the old model faster; it changes what’s possible. Up to this point, every wave of transformation has created a familiar tradeoff: yes, you gain efficiency in one place, but you introduce friction in another, like disconnected data, fragmented tech stacks, and handoffs between teams that slow execution. That’s why most gains have been incremental—even compounding—but not exponential.
Agentic AI introduces a new paradigm: moving beyond intelligent automation to human-reviewed autonomous action. These systems won’t only recommend the next best step, they can execute it alongside your team under clear oversight and guardrails. In other words, we’re no longer talking about software that supports work; we’re talking about software that can carry work responsibly, transparently, and in alignment with human intent.
And that’s why the upside is much bigger than saving minutes (or even hours) inside today’s stewardship models. The real value is the ability to stay consistently present, more personalized, and better informed across far more of the donor base. Over time, that consistency compounds: stronger retention, more upgrades, and more repeat giving. Those are the growth dynamics that let nonprofits scale without a proportional increase in cost or staff burden, making AI not just a productivity lever, but a foundation for sustainable fundraising.
But here’s the hard truth: we won’t unlock that value by bolting new AI tools onto old operating models. Trust-sensitive work demands more than experimentation; it demands systems, data, and governance that are built to support responsible autonomy.
That’s why future-ready organizations will embrace four mindset shifts that will move AI from isolated pilots to real mission impact, at scale.
Shift #1: From data as information to data as fuel for impact.
The most effective social impact organizations are moving beyond using data solely for reporting and compliance. With AI and analytics, data becomes a dynamic asset that actively powers decisions, fueling targeting, prediction, personalization, and continuous learning. Instead of documenting outcomes after the fact, data increasingly shapes them in real time.
However, most fundraising teams today don’t lack data; they lack usable data. Donor signals come from campaigns, events, email, social media, peer-to-peer, and payments, in multiple formats and across many siloed systems. Without strong integration, this information stays fragmented and limits strategy and stewardship.
Context is what connects scattered signals into a coherent story and a clear next step, transforming data into actionable intelligence. Context gets stronger when systems connect. When fundraising, finance, and operations share a continuous data loop, intent can be linked to outcome: from donation to disbursement, from outreach to impact. AI-powered, connected systems reduce handoffs, break down silos, and support faster, more transparent decisions grounded in real-time context.
Thus, it is more important than ever before to prioritize data—usable and contextualized data—as a critical component of accelerating impact.
Shift #2: From systems of record to engines of intelligent action.
Traditional platforms were built to store transactions and preserve history to help organizations see what had already happened. But with a strong data foundation and connected context, future-ready organizations are able to move toward systems designed to act: engines that combine data and context to surface recommendations, trigger next best actions, and orchestrate workflows across teams. These engines don’t just illuminate what to do next; they put intelligence into motion, responsibly and at scale, under human direction and with clear guardrails.
The real promise here isn’t automating what we used to do. It’s opening the door to entirely new possibilities. Just as online giving, peer-led fundraising, and text-to-give were unimaginable to the nonprofits Blackbaud furnished with their first computers back when we were pioneering digital systems of record for fundraising, agentic AI, guided by human judgment, will make new levels of personalized engagement possible at scale.
This doesn’t replace fundraisers. It changes how their time is spent, because even as systems become more autonomous, human guidance remains essential. AI excels at scale, pattern recognition, and real-time optimization, but it lacks human relationships which will remain essential in fundraising. In a trust sensitive environment like the nonprofit and education sectors, people must remain in control of when systems act, when they wait, and when they defer to human discretion.
AI systems don’t learn fundraising in the abstract. They learn from how real gift officers decide who to call back, when to wait, and when not to automate, and from context that reveals how donors have responded, or not, to which requests in the past. Deep domain experience ensures that automation reflects real-world fundraising best practices, not generic assumptions, and evolves alongside the sector itself.
And, when systems handle more of the operational load, people can focus on judgment, relationships, and storytelling. The parts of fundraising that require empathy, discretion, and human understanding become more central, not less.
We’re already seeing signs of this. Organizations that automate personalized engagement often see stronger retention and more consistent giving. AI extends those gains by learning continuously, rather than relying on static rules or one‑time configurations.
Shift #3: From proprietary platforms to optionality and openness
Rather than treating each system as a standalone tool, future-ready organizations design their technology ecosystems to share context, coordinate decisions, and evolve together. Strong APIs and interoperability aren’t just integration features – they’re what allow intelligence to move fluidly across the organization, so insights generated in one system can inform action in another without friction.
Emerging standards like Model Context Protocol (MCP) reinforce this approach by providing a consistent, secure way for AI systems to access and reason over shared organizational context. Instead of hardwiring bespoke integrations every time a new model or tool is introduced, organizations can preserve continuity as they add capabilities, swap vendors, or adopt new forms of automation. The result is greater optionality: teams gain the freedom to innovate without sacrificing coherence, governance, or trust.
This shift also forces nonprofits to reconsider what they expect from their software and partners. Success is no longer defined by how much custom work is required upfront, but by whether their ecosystem makes it easy to connect data and workflows safely, carry context across teams, and adopt new capabilities without constant re‑implementation. In an AI‑driven environment, organizations that treat openness as an operating principle—prioritizing interoperable tools, portable data, and clear governance—can move faster while staying aligned to mission, values, and constituent trust.
Shift #4: From custom, bespoke implementations to assisted evolution
Because of the pace of change and evolving needs, future-ready organizations are using flexible platforms that are designed to adapt, rather than rebuilding their tech stacks from scratch every time the organization’s needs evolve. Guided setup, intelligent defaults, and continuous optimization allow systems to evolve alongside mission needs—reducing technical debt, accelerating time to impact, and making change more sustainable over time.
Assisted evolution reduces the burden placed on already‑stretched teams. When systems are designed to guide configuration and improve themselves over time, organizations spend less energy managing technology and more energy advancing their mission. The result is not only faster impact, but a more sustainable operating model that doesn’t depend on scarce technical expertise to keep pace with change.
The difference is visible in outcomes. Organizations that rely on custom builds tend to experience progress in bursts—followed by periods of stagnation as systems catch up. Those using configurable, AI‑assisted platforms move forward continuously, making smaller, smarter adjustments that compound over time. This steady evolution is what enables long‑term impact without long‑term fragility.
Many organizations have learned that highly customized systems can look flexible on paper but become brittle in practice. Every change request turns into a backlog, and every optimization competes with day‑to‑day operations.
In a world of constant change, the most effective systems aren’t rebuilt; they’re designed to learn, adapt, and improve alongside the mission. And, of course, this vision only works when AI is grounded in meaningful information, enriched with useful context, and built on trust.
The Moment Ahead
AI will not change the heart of fundraising. People have always given because they care. That will always be true. But they will do it in new ways, with new expectations and potentially at a new scale if we harness the potential of AI for fundraising.
What AI can change is the friction that stands between intent and impact. Used thoughtfully, it can help resources move faster, teams work more sustainably, and missions reach further than they could before.
The organizations that succeed won’t be the ones that adopt AI simply because it’s new. They’ll be the ones that use it with care, grounded in context, and guided by trust.
At scale, impact compounds through networks. When nonprofits, schools, individual givers, and corporations participate in shared giving ecosystems, each new participant increases the value of the whole by amplifying awareness, trust, and generosity. AI can act as the accelerator of these network effects, helping missions reach beyond individual organizations to unlock collective impact.
The next era of fundraising won’t be defined by technology that replaces human connection, but by technology designed to strengthen it: to carry context forward, earn trust, and help fundraisers show up for supporters with consistency, relevance, and care. The decisions we make now won’t just shape fundraising technology, they’ll shape how generosity itself works for years to come.
Learn more about Blackbaud’s approach to Responsible AI here.
