This article is based on a recent report published in NonProfit PRO, drawing on the results of a study issued by Virtuous and Fundraising.AI titled The 2026 Nonprofit AI Adoption Report, with an accurate translation that preserves the presentation sequence and meaning, and a light editorial style suitable for knowledge publication.

The report reviews the reality of AI usage in nonprofit organizations, revealing a notable gap between the widespread adoption of technology and the limits of its actual impact, providing a reliable reading of sector trends in the upcoming phase, while fully preserving the literary rights of the source and the entities producing it.

The adoption of artificial intelligence (AI) in the nonprofit sector is nearly universal, but true and tangible transformation remains rare.

A new benchmarking study released by Virtuous and Fundraising.AI indicates that 92% of nonprofit organizations use AI tools to some extent, but only 7% report a significant improvement in their organizational capabilities, a gap described as the "efficiency plateau".

The researchers behind the "The 2026 Nonprofit AI Adoption Report" surveyed 346 nonprofit organizations in late 2025, aiming to assess how AI is used and whether this usage translates into measurable gains in fundraising.

Gabi Cooper, CEO and founder of Virtuous, stated: "The question is not whether nonprofits should use AI; I believe this debate has largely been settled. The real question is how quickly nonprofit teams will adopt AI and radically rethink their workflows as our data shows that most organizations are still in the very early stages with AI: one person is using ChatGPT to help craft a donation request, while the rest of the team is still bogged down in manual processes and unconnected systems, and this is not a complete strategy; it is a temporary solution."

Widespread AI, Limited Transformation

Nearly four out of five respondents report small to medium improvements as a result of using AI, with these gains tending to manifest in speeding up draft preparation, accelerating research, and improving content quality, which are valuable efficiencies for teams pressed for time, but are largely incremental increases.

Only 7% say that AI has led to a significant strategic impact, such as doubling the ability to research potential donors or reallocating staff time from execution to strategic work.

This data reflects what could be termed the "efficiency plateau", where 79% of nonprofit organizations report achieving small to medium improvements as a result of AI usage, compared to only 7% that see significant strategic impacts. A detailed effect shows that 40% of these improvements are concentrated in specific areas, and 39% in efficiency and quality enhancement, while still 14% of organizations report no notable impact.

The way organizations describe their AI usage helps explain this plateau as 65% characterize their AI usage as interactive and individual, such as one-off commands and personal experiments, while only 18% report operational use across team workflows, and just 7% say that AI is integrated into goals, budgets, and performance indicators.

Readiness Gaps for AI Limit Impact

If adoption is high but transformation is limited, the difference seems to stem from the organization’s readiness as the barrier does not lie in accessing AI tools, but rather in the absence of shared systems surrounding them.

81% of organizations report using AI individually and on an irregular basis, while only 4% say they have a documented and repeatable workflow. In practice, the experience remains often more personal than institutional as knowledge stays with specific individuals rather than becoming part of the organization's way of working.

Governance gaps exacerbate this issue as nearly half of participants report the absence of an official AI policy and without clear guidelines on what is encouraged, what requires approval, and what is prohibited, especially when dealing with donor data, leaders may find it challenging to scale the experience safely across teams.

Measurements are likewise limited, with the report describing outcome tracking as "very rare", as most organizations rely on informal observation rather than systematic metrics. Without specific standards, nonprofits cannot easily determine whether AI is expanding their fundraising capacity or merely accelerating the execution of existing tasks.

This data reflects a clear gap in AI governance within nonprofit organizations, as 48% report no formal policy, compared to 23% adopting a cautious approach, and 19% leaning towards empowerment, while smaller percentages indicate uncertainty (4%) or restriction (6%), highlighting the absence of a unified framework guiding the use of this technology.

When looking at the results collectively, they indicate a readiness gap, with about one-fifth of organizations having essential elements in place, including some governance, documentation, and measurement, while another fifth is in the early testing stage and at risk of hitting the "efficiency plateau" without intentional systems, and the majority actively uses AI but lacks the necessary structure to scale effectively.

Nathan Chapel, head of AI at Virtuous, stated that the difference ultimately comes down to integration.

He said in a statement: "What we see is that AI only achieves meaningful impact when nonprofits rethink how work gets done, not when it is treated as a side experiment managed by individuals separately. The teams that move forward are those willing to clarify their strategy, establish simple controls, and intentionally integrate AI into how decisions are made. When AI becomes a part of the way the organization thinks and not just a tool they use, that’s when the ability to scale starts."

Size Is Not the Advantage You Might Expect

The data also challenges assumptions about the size of the organization.

Smaller organizations, defined as those with fewer than 50 employees, report a medium impact at slightly higher rates than larger organizations (41% vs. 34%). While larger nonprofits may have more resources, they also face greater complexities in coordination and compliance.

The barriers evolve as adoption deepens. Among nonprofits that have not yet begun to use AI, 48% cite lack of training, while 44% state they need guidance on how to get started, and for organizations that regularly use AI, concerns shift to privacy and security (32%), along with time constraints (31%).

In other words, the challenge evolves from "How do we start?" to "How do we scale this responsibly?"

As AI adoption continues to rise in the nonprofit sector, the differentiating factor now lies in how intentionally and collectively it is integrated into the nonprofit work. Whether organizations remain at the "efficiency plateau" or build systems that expand their fundraising capabilities will determine how AI impacts the sector in 2026 and beyond.

In light of what the report published in NonProfit PRO reveals, the real challenge appears not in the adoption of AI, but in converting it into measurable organizational value. Between widespread use and limited impact, the gap is determined by the depth of integration and the quality of deployment, and the next phase will not be measured by how much these tools are used, but by the organizations’ ability to reshape their work around them in a disciplined and conscious manner. It is worth noting that this material is translated from the original source and is not exclusive.