92% of Nonprofits Now Use AI: What the Latest Research Reveals About Adoption in 2026

The TechSoup/Tapp Network 2025 Benchmark Report, which surveyed more than 1,300 nonprofits, provides the most granular picture of how AI is being applied in practice. Content creation and marketing leads the way, with 33% of nonprofits using AI for content-related tasks. Grant writing assistance comes second at just under 25%, followed by predictive analytics at about 13%.

The Center for Effective Philanthropy's 2025 research adds another dimension: 35% of surveyed organizations use AI primarily for internal productivity (finance, HR, operations), 31% for marketing and communications, and 24% for development and fundraising. Notably, the majority of AI use falls into supporting functions rather than mission-critical applications. Nonprofits are automating back-office tasks and speeding up content production, but the deployment of AI in direct service delivery, program design, and outcome measurement remains relatively rare.

This pattern is both understandable and limiting. Starting with lower-stakes, back-office applications is a reasonable risk management strategy. But it also means that AI's potential to genuinely expand organizational capacity, to serve more clients, design better programs, and demonstrate greater impact, remains largely untapped at most nonprofits. The 7% who report transformational results are typically the organizations that have moved beyond administrative AI and begun deploying it in mission-relevant ways.

If the strategy gap explains why nonprofits plateau at the organizational level, the training crisis explains why the gap persists at the individual level. According to Bridgespan research, training represents only 1% of nonprofit technology budgets, with the remainder split across hardware, software, and services. An AI tool with no investment in training is like a car with no driver education: it may technically be available, but the organization doesn't get the full value from it.

The data on training is striking. The Virtuous 2026 report found that 48% of nonprofits not yet using AI cite lack of training as the primary barrier to adoption. Among organizations already using AI, 69% of nonprofit communicators using generative AI have not received formal training, according to the Nonprofit Perspectives on Generative AI Report. A Google.org survey of 4,600 nonprofits in 65 countries found that none of the surveyed organizations had achieved majority staff training in AI. Two in five had provided zero AI training to any staff.

The consequence is uneven, informal AI capability that compounds existing inequalities within organizations. Staff who are naturally curious about technology develop AI skills; others don't. Senior staff who might most benefit from AI-augmented decision support are often the least likely to have received training. Program staff who deliver direct services, where AI could meaningfully expand capacity, are frequently left out of technology initiatives that focus on administrative functions.

The organizations achieving the strongest AI outcomes have treated staff capability development as a first-class investment, not an afterthought. This doesn't mean formal training for every AI tool, but it does mean deliberate practices for sharing knowledge, building shared vocabulary, and ensuring the staff members who can create the most value from AI have the skills to do so. Our guide on AI knowledge management for nonprofits offers practical approaches to building and sharing AI capability across teams.

The fundraising applications of AI have generated some of the most compelling performance data in the nonprofit technology space. Platform benchmarks from Fundraise Up show that AI-powered donation forms achieve average one-time gifts of $161 compared to an industry average of $115, a 40% increase. Conversion rates from click to donation reached 29% on AI-optimized platforms versus a 12% industry average. These are significant figures, though they represent platform-specific performance data rather than randomized sector-wide research.

Broader sector research from Social Current finds that organizations using predictive analytics see 20 to 30% increases in donor response rates, and that AI automation saves organizations 15 to 20 hours weekly on administrative tasks. The Virtuous report itself found that 30% of nonprofits report AI boosted fundraising revenue in the past 12 months. These are meaningful benefits, but they remain concentrated in the organizations that have moved beyond ad hoc AI use to deliberate AI integration in their fundraising workflows.

One finding that deserves careful attention is how donors perceive nonprofit AI use. Research cited in the Nonprofit Tech for Good 2026 statistics aggregator found that 43% of donors say AI use would positively or neutrally affect their giving decision, while 31% would be less likely to donate if they knew AI was used in donor communications. This suggests that transparency matters: how nonprofits communicate about their AI use shapes donor trust as much as whether they use it. Organizations that are thoughtful and transparent about their AI applications in fundraising generally fare better than those that use AI silently or deceptively.

The 2026 research is also revealing about the risk landscape nonprofit leaders are navigating. According to data from the Nonprofit Perspectives on Generative AI Report aggregated by Nonprofit Tech for Good, 70% of nonprofit professionals worry about data privacy and security from AI tools, 63% worry about accuracy and reliability, and 57% express concern about bias in generative AI outputs. The Center for Effective Philanthropy adds that security, accuracy, staff expertise, bias, and data privacy consistently rank as the top five concerns across both foundations and nonprofits.

These concerns are legitimate and deserve organizational attention. Data privacy concerns are particularly well-founded, as nonprofits handle sensitive information about clients, donors, and vulnerable populations that should not be carelessly entered into public AI systems. Accuracy concerns reflect the real risk of AI hallucination, where confident-sounding but incorrect information makes its way into grant applications, donor communications, or program documentation. Bias concerns are especially important for organizations serving marginalized communities, where AI tools trained on historical data can reproduce and amplify systemic inequities.

The challenge is that these concerns sometimes block progress rather than shape it appropriately. A nonprofit that refuses to use any AI because of bias concerns misses the productivity and capacity gains available from careful, monitored AI use in lower-risk applications. A more productive approach is to assess risk by application rather than treating AI as uniformly risky, establish clear policies about what data can be used with AI tools, and invest in the human review processes that catch the errors and biases AI tools produce.

One area where nonprofit concerns are perhaps not adequately elevated: the organizational risks of falling behind on AI capability. As the sector bifurcates between AI-capable and AI-limited organizations, the competition for grants, talent, and public trust increasingly favors organizations that can demonstrate sophisticated AI use. The risks of not engaging thoughtfully with AI may ultimately be greater than the risks of engaging carefully.