Bridging the AI Effectiveness Gap​

Compared to early‑stage or experimental organizations, AI‑Adaptive organizations are significantly more likely to report:

• Increased overall revenue, and exceeding goals

• Improved fundraising revenue

• Strengthened donor retention

• Increased staff productivity and capacity

To support these efforts at scale, AI-Adaptive organizations are dramatically more likely to have:

• Formal policies for sensitive data

• Human review of AI outputs

• Clear accountability for AI decisions

These organizations also report higher confidence, stronger alignment, and greater willingness to expand AI use responsibly over time.

Policy Adoption is Increasing: In our 2025 Status of Fundraising in the AI Era study, we found that only 14% of organizations had formal AI policies in place. As of 2026, this number has more than doubled to 30%, with an additional 37% planning to take this action.

Efficiency without intention rarely produces impact—and intention is needed now more than ever.

When we look at who reports using AI “almost constantly” or “several times a day,” two groups stand out—compared to an average of 33% for all professionals:

• 39% of Millennial Professionals

• 50% of Leadership Professionals

These groups share a pattern of:

• Early Adoption: 60% of Leadership and 56% of Millennials identify as Early or Very Early Tech Adopters. This is nearly double the rate of their Gen X and Baby Boomer counterparts.

• Time Saved: Millennials and those in Leadership Positions claim to save an average of 11 and 13 hours each week, respectively, thanks to their frequent AI use.

• Adoption Exposure: These groups are most likely to say that AI is actively being promoted or encouraged at their organization.

• Tangible Benefits: As champions for adoption, they are more likely than other groups to say that AI helps with efficiency, reduces cost, and increases staff capacity.

There is a virtuous adoption cycle occurring with Millennials and those in Leadership Positions. They are using AI more frequently, experiencing more benefits, and understand how their saved time can be translated into tangible results.

The security cost of free versions of AI tools should be a priority consideration for all organizations. When a tool is free, your data—and potentially your supporters’ data—becomes the value that’s exchanged.

With concerns over misuse of data and private personal information for both organizations and donors, the lack of guardrails and governance with free AI tools can be a roadblock to adoption or—for those already using those tools—a latent security risk.

In a 2025 Blackbaud Institute report on the Status of Fundraising in the AI Era, professionals were optimistic about AI’s positive impact on the sector but were held back by resourcing and access.

In 2026, we see some progress towards more effective use, but barriers persist.

Lack of staff knowledge, training, and AI skills: “Training staff, understanding where AI can be used effectively, evaluating risks versus rewards and placing parameters around best use.”  -Leader at a Large School

Concerns about data security and accuracy: “I think clarity on if AI has the correct guardrails in place to prevent data being misused before we allow it to become part of our workflows.” -Development and Operations Professional at a Large Nonprofit

Limited Resources: “[We feel] uninformed and lack [the] time to research while maintaining current systems.” -Professional Working Across Roles at a Large Nonprofit

These barriers are constraining some organizations to superficial use. Less than half of professionals report using AI for tasks beyond basic content creation—such as data analysis, operational decision‑making, or strategic planning. Organizations are stalling in the generative AI space, missing out on opportunities for transformative use.

Meanwhile, many professionals report feeling hopeful that a strategic use of AI will save them more time to focus on their mission and building relationships:

“I hope that … AI can help employees save time and focus more on the innovative aspects of their jobs.”
– Professional Working in Operations at a Mid-Size Nonprofit

“We hope AI can analyze data quickly and help us make better decisions.”
– Professional Working Across Multiple Roles at a Mid-Size School

“[My biggest hope for AI at my organization is that it will] take some of the menial tasks that take up time, and automate them, or allow us to get out with donors to really grow those relationships.”
– Professional Working in Development and IT/Data at a Mid-Size School

We see AI-Adaptive organizations leading with advantage:

• These organizations are more likely to have invested in dedicated data staff, increasing their AI readiness (avg. 6 people vs. ~1 elsewhere).

• 81% have experimented with AI to improve data quality.

Recent Risk Readiness research from the Blackbaud Institute consistently shows that proactive organizations invest in data, staff, reporting, and decision infrastructure. AI amplifies this dynamic.

It’s critical to consider how increased investment can meet evolving needs—whether that be an AI solution that can improve data quality, or resourcing to flexibly meet the most pressing needs as they arise.

AI amplifies existing data conditions—for better or worse.

Organizations that delay data readiness delay AI value.

Generational differences show Millennials leading the charge on AI use, with Baby Boomers as the most hesitant:

This comfort is also tied to their own personal experience and adoption. In our survey, younger generations, those who are more likely to be using AI personally, showed that they’re more comfortable with social impact organizations’ use of AI.

Donors also tend to be more optimistic about the use of AI for social impact than even professionals in the field! They see how AI use can fit into the model of social impact growth and find benefits for AI to help nonprofits work faster and more efficiently.

They also see benefits in outreach and data health to increase effectiveness and impact reporting.

“[AI] could make it easier for [organizations] to communicate with the public and perhaps increase the amount of support that they are getting.”
 -Baby Boomer Volunteer

“The primary benefit is that AI handles the administrative burden, allowing human staff to focus on high value work like building relationships and direct mission delivery.”
-Gen Z Donor and Volunteer

The greatest gap between donors’ and organizations’ priorities lie in best practices and transparency. Consistently, organizations are behind on donors’ expectations around security, policies around human input, disclosure, and more.

As shown in the chart above, there are substantial gaps between donor priorities and the actions that organizations are taking. For example, 68% of donors say it is very important that organizations protect their sensitive personal data within AI use, but only 36% of organizations are taking the right steps to do so.

With Donors, Transparency is Key

There is a net positive impact on donor trust across all generations when AI use is communicated by an organization.

For existing donors, a public statement about responsible AI use has a positive impact on retention, particularly among younger donors and those who give over $500 a year.

The uneven realization of AI value in 2026 has implications beyond individual organizations. It shapes:

• Financial resilience across the sector

• Donor confidence and expectations

• The sector’s ability to respond to growing needs with constrained resources

Why does this matter now? Over the past two years, Blackbaud Institute research has consistently shown that sector growth is uneven and confidence in the future remains fragile for at-risk organizations.

In the 2025 Risk Readiness study:

• Only 56% of organizations reported confidence in their ability to deliver on their mission over the next three years, with a notable drop in those who felt very confident long‑term.

• Organizations classified as Proactive—those pairing lower concern with higher preparedness—stood apart. They were significantly less likely to anticipate revenue declines, layoffs, or program cuts and far more likely to invest in technology integration, data quality, and forward‑looking planning rather than short‑term retrenchment.

• Readiness, not size or budget, emerged as the defining factor separating organizations positioned for growth and proactive risk-readiness.

That readiness gap is mirrored in the broader giving environment. The 2025 Trends in Giving spotlight showed that growth was concentrated among large organizations and mid to major gift revenue, while small organizations and low level giving lagged or declined. Overall, the sector continued to grow in 2025 despite a cooling economy, but that growth was concentrated among organizations and donors with greater capacity, higher donor intelligence, and key timing.

At the same time, donor trust is becoming a central determinant of long‑term health, particularly as younger and first‑time donors make up a growing share of the giving population. Blackbaud Institute research shows that Gen Z donors prioritize transparency, ethical data use, and authenticity, viewing trust as foundational to whether an organization deserves their support. Building trust with Gen Z and Millennial donors is one key to longevity.

As organizations increasingly rely on automation and AI to operate efficiently, these findings underscore a critical reality: technology that is not governed, transparent, and aligned to donor expectations risks eroding trust precisely when the sector can least afford it. Responsible AI—grounded in strong data practices and accountability—is therefore not simply an innovation strategy, but a prerequisite for sustaining donor confidence and long‑term sector resilience.

The strongest organizations are not approaching AI as a productivity tool; they are moving forward with intention, establishing their AI strategy as critical to their mission delivery. They are building clarity, trust, and confidence over time. The organizations that are intentionally enacting governed AI usage are seeing tangible benefits. This is something we call the AI Maturity Dividend.

The AI Maturity Dividend:
The compounded value organizations gain when they move beyond ad hoc AI use and apply AI intentionally across the organization. That return comes not just from time savings, but from using AI to increase revenue, strengthen mission delivery, and reduce risk.

How Adoption Can Transform into Tangible Impact:

Driver 1: Time and Cost Savings

Organizations start with measurable efficiency gains: the average organization saves about $500 per employee per week using AI—and that figure rises to $621 among AI-Adaptive organizations. The difference is not simply greater use. It is maturity: governance, data readiness, and consistent practices that make time savings more reliable and scalable. For example, Boys and Girls Club of the Tennessee Valley used their integrated AI chat to identify five potential planned giving prospects with higher-than-average wealth ratings who would have otherwise not been flagged in their system. These prospects were identified not because of past giving history or actions but because of clean data and targeted AI prompts. This cleared the way for fundraisers to focus on personal outreach and relationship building.

Driver 2: Reinvestment

The use of saved time differs dramatically. In AI‑Emerging organizations, time savings often remain diffuse—helpful to individuals but not consistently translating into organizational health or mission impact. In contrast, AI‑Adaptive organizations are better positioned to reinvest capacity into work that compounds results: improving data quality, strengthening donor trust through transparency, and building repeatable, organization-wide AI practices. For Fort Collins Habitat for Humanity, that meant giving its chief development officer more time to cultivate major donors instead of spending hours identifying and prioritizing prospects in the database.

Driver 3: Strategic Application

AI-Adaptive organizations do more than save time; they apply AI in ways that advance core organizational goals. These organizations are more likely to use AI beyond basic content creation for higher-value work such as data analysis, operational decision-making, and strategic planning. That shift allows AI-enabled capacity to support revenue, mission delivery, and risk reduction. For example, ZERO Prostate Cancer used an AI-powered SMS texting system to deliver reliable, compassionate, and actionable information to patients, caregivers, and families at pivotal moments in their journey—directly supporting its mission through advocacy, awareness, education, and support.

Driver 4: Performance Improvements

Where the story becomes meaningfully different is in the outcomes leaders care about. AI‑Adaptive organizations consistently outperform their peers across every major area measured and are more likely to report results tied directly to sustainability, including increased overall revenue, improved fundraising revenue, stronger donor retention, and greater staff productivity and capacity. Vecova, for example, used AI-powered donor insights to qualify 72 major gift prospects within a few months and identify donors willing to make matching gifts for its GivingTuesday campaign—helping translate data into stronger fundraising performance.

Next Steps: What Organizations Should Do Now

The path forward is not to adopt more AI for its own sake, but to close the gaps that prevent adoption from translating into organization-level results. For organizations looking to move from fragmented experimentation to durable impact, the next step is to take targeted action against each of the four gaps identified in this report.

• Close the Effectiveness Gap: Start with a small set of high-value use cases tied to clear organizational goals. Define where AI should support workflows, assign ownership, and measure success based on outcomes such as time saved, revenue supported, or mission capacity created.

• Close the Transparency Gap: Establish a clear, public-facing explanation of how your organization uses AI, where human oversight remains in place, and how donor and constituent data is protected. Transparency should be treated as a trust-building practice, not a compliance afterthought.

• Close the Infrastructure Gap: Move from unmanaged individual use to shared, governed systems. That means evaluating enterprise tools, setting policies for approved use, and creating guardrails that reduce security, privacy, and operational risk while enabling responsible scale.

• Close the Data-Readiness Gap: Invest in the quality, stewardship, and accessibility of your data. Strengthening data practices now will improve the reliability of AI outputs later, while also building a stronger foundation for reporting, decision-making, and long-term growth.

The future health of the social impact sector depends on closing the gap between the AI-Adaptive and AI-Emerging organizations.