AI-Assisted Donations Average $161 vs. $115: What AI Fundraising Data Shows in 2026
The data on AI's impact in nonprofit fundraising is becoming clearer and more compelling. Organizations that move beyond ad hoc AI use to build systematic, governance-supported fundraising programs are pulling significantly ahead. Here is what the latest numbers reveal and, more importantly, what separates the organizations seeing results from those that are not.
For years, the case for AI in nonprofit fundraising rested largely on promise and anecdote. Early adopters reported efficiency gains and better donor segmentation, but rigorous outcome data was scarce, making it difficult for development directors to build the business case for AI investment with board members and executive leadership who wanted numbers before committing resources.
That data has now arrived. Fundraise Up, one of the leading AI-optimized donation platforms, reports that nonprofits using AI-powered donation forms see average one-time gifts of $161 compared to the industry average of $115, a 40 percent difference that compounds significantly at scale. For recurring gifts, the AI-assisted average climbs to $32 monthly compared to $24 for the industry average, a 33 percent improvement in the most valuable category of nonprofit revenue. These figures come from real transaction data across thousands of nonprofit donation flows, not from surveys about intention or satisfaction.
The 2026 Nonprofit AI Adoption Report from Virtuous and Fundraising.AI adds important context to these numbers. While 92 percent of nonprofits are now using AI in some capacity, only 7 percent describe their use as generating major improvements in organizational capability. The report identifies a clear pattern: organizations with formal AI governance, documented workflows, and systematic measurement are pulling ahead, while those using AI on an ad hoc basis with individual staff members are plateauing. Understanding what creates that gap, and how to close it, is the practical question that the fundraising data makes urgent.
The Data: What AI Is Actually Delivering in Fundraising
Before exploring strategy, it is worth examining the data landscape carefully. Not all AI fundraising statistics come from comparable sources, and the variation in reported outcomes reflects genuine differences in how AI is implemented, what tools are used, and what baseline each organization started from. The most reliable data comes from platform providers with large transaction samples and from independent research organizations tracking sector-wide trends.
Donation Form Optimization Results
Data from AI-optimized donation platforms across thousands of nonprofit transactions
- $161 average one-time donation with AI optimization vs. $115 industry average (40% improvement) Source: Fundraise Up
- $32 average monthly recurring gift with AI vs. $24 industry average (33% improvement) Source: Fundraise Up
- 10 to 15 percent additional revenue from AI-optimized forms compared to standard donation forms Source: Fundraise Up
- Smart suggested amounts increased average donation by 4.2 percent in 2025 A/B testing Source: Fundraise Up
- Donor acquisition more than doubled using AI-optimized conversion flows Source: Fundraise Up
Predictive Analytics and Major Gifts
AI-powered prospect research and major gift identification outcomes
- 85 percent increase in appeal response rates using AI prospect scoring Source: DonorSearch
- 20 percent increase in average gift size through AI-identified prospects Source: DonorSearch
- AI-identified major gift prospects with 20x greater lifetime value than average Source: DonorSearch
- 56 percent higher major gift donation rates in campaigns using predictive modeling Source: CCS Fundraising
- 30 percent boost in major gift identification and 20 percent reduction in prospect research time Source: National Multiple Sclerosis Society via CCS Fundraising
Retention and Recurring Revenue
AI impact on the metrics that drive sustainable nonprofit revenue
- 18 percent boost in donor retention using AI-powered retention tools Source: DonorPerfect
- 30 percent increase in donor retention rates using AI-powered chatbots for donor engagement Source: Charity: water
- 140 percent increase in the value of new monthly givers at International Justice Mission UK Source: Fundraise Up client data
- 264 percent increase in recurring donors at Animal Haven since 2019 using AI optimization Source: Fundraise Up client data
Campaign and Revenue Results
Broader campaign outcomes from AI-integrated fundraising programs
- 48 percent increase in donations and 64 percent increase in revenue at Canadian Red Cross using AI donation platform Source: Fundraise Up client data
- 71 percent increase in annual revenue at Community FoodBank of New Jersey Source: Fundraise Up client data
- 20 to 30 percent increases in donations through personalized AI-driven outreach across sector Source: 2026 Nonprofit AI Adoption Report
- 61 percent of nonprofits now using AI specifically for development and fundraising activities Source: 2026 Nonprofit AI Adoption Report
These numbers are real and meaningful, but they require important context. The strongest results come from organizations that have integrated AI systematically across multiple fundraising functions, not from those that have adopted a single tool and hoped for improvement. Platform-level data like Fundraise Up's $161 vs. $115 comparison reflects AI working well; it does not tell you what happens when organizations adopt AI tools without the governance infrastructure and measurement practices that make AI work well. That gap between adoption and impact is where most of the sector currently sits.
What AI Actually Does in Fundraising: The Six Core Capabilities
Understanding which AI capabilities drive fundraising results helps organizations make smarter investment decisions. Not all AI fundraising applications deliver equal returns, and some capabilities are far more accessible to resource-constrained nonprofits than the vendor marketing literature suggests. The following six capabilities account for most of the performance improvements showing up in the data.
1. Donation Form Optimization
The most immediately accessible AI capability, with the clearest impact data
AI-powered donation forms use machine learning to optimize the giving experience in real time, adjusting suggested donation amounts, form layout, and giving prompts based on what has worked most effectively with similar donors. The mechanism is straightforward: rather than presenting every visitor with the same three or four suggested amounts based on historical averages, AI analyzes contextual signals to present suggestions more likely to convert at a higher level.
The $161 vs. $115 comparison from Fundraise Up demonstrates what consistent, data-driven optimization produces at scale. The key distinction from manual form optimization is speed and continuity: AI tests and adjusts continuously, running thousands of micro-experiments that no development team could replicate manually. The 4.2 percent improvement from smart suggested amounts alone may sound modest, but across thousands of donations annually, it compounds into significant additional revenue.
- Suitable for organizations at any AI maturity level, often requiring minimal setup
- Works on donation volume; smaller organizations see proportionally similar improvements
- No donor PII required for the most effective optimization approaches
2. Predictive Analytics for Major Gift Identification
Finding your next major donors before they self-identify
Predictive analytics models analyze donor behavior patterns, giving history, capacity indicators, and demographic information to identify which donors in your database have the highest potential for major gifts. This is fundamentally different from traditional prospect research, which relies on manual research and the professional judgment of development staff working through lists sequentially. AI can analyze your entire donor database simultaneously, surfacing patterns that would take months to identify manually.
The DonorSearch data showing 85 percent increases in appeal response rates and prospects with 20 times greater lifetime value reflects what happens when development officers focus their time on the donors most likely to make major gifts rather than working from alphabetical lists or recency-frequency-monetary (RFM) scores alone. The 56 percent higher major gift donation rates in campaigns using predictive modeling, from CCS Fundraising research, tells a similar story: attention directed by AI to the right donors at the right time produces fundamentally better results than undifferentiated solicitation.
- Most valuable for organizations with databases of 5,000 or more donors
- Requires clean donor data and consistent data hygiene practices to work effectively
- Works best when integrated with your CRM rather than as a standalone tool
3. Personalized Donor Communications
Delivering the right message to the right donor at the right moment
Personalization has been a fundraising aspiration for decades, but true segmentation at scale has been beyond the reach of most nonprofits operating with small development teams. AI changes that equation. Natural language processing models can analyze previous donor communications and identify which messaging approaches resonate with different donor segments. Machine learning can predict optimal send times for email campaigns based on individual recipient behavior. Generative AI can assist in drafting personalized donor appeals that incorporate each donor's giving history, stated interests, and geographic connection to your mission.
The distinction between AI-assisted personalization and manual segmentation is not just scale but adaptiveness. Traditional segmentation divides donors into fixed groups (major, mid-level, annual fund) with different communication tracks. AI personalization treats each donor as an individual whose preferences, engagement patterns, and giving potential are continuously updated based on new information. The practical result is that donors receive communications that feel more relevant and less generic, which translates directly into engagement rates and, ultimately, conversion.
- Start with subject line personalization and optimal send time prediction; both have high ROI with low complexity
- Maintain human review of AI-generated content to preserve authentic organizational voice
- Test AI-personalized communications against control groups before full rollout
4. Donor Lifecycle Management and Churn Prevention
Identifying at-risk donors before they lapse and re-engaging them proactively
Donor retention is one of the most valuable levers in nonprofit fundraising. Acquiring a new donor costs significantly more than retaining an existing one, and most organizations lose a substantial portion of first-time donors before they make a second gift. AI changes the retention equation by making it possible to identify which donors are at risk of lapsing before they actually stop giving, allowing development teams to intervene with targeted re-engagement while the relationship can still be salvaged.
The 18 percent retention improvement from DonorPerfect's AI tools and the 30 percent retention increase at Charity: water reflect what proactive, data-informed retention looks like in practice. These organizations are not just reaching out to lapsed donors; they are identifying the signals that precede lapsing and responding before the donor actually stops giving. Declining email open rates, reduced website visits, changes in giving patterns, and missed renewal dates all feed into AI models that flag donors for personalized outreach before they disengage completely. The article on AI knowledge management explores how comprehensive donor data management supports these systems.
- Focus initial retention AI on recurring donors, where the lifetime value of each saved relationship is highest
- Build workflows that automatically trigger outreach when AI flags at-risk donors
- Measure retention rates before and after AI implementation to document your organization's specific ROI
5. Upgrade and Upgrade-Potential Identification
Finding the donors in your base who are ready to give significantly more
Every nonprofit has donors giving below their potential. Some have increased their capacity since their last gift but have not received a cultivation strategy that reflects their new circumstances. Others have been systematically undercultivated because they did not appear in prospect research until AI could identify the behavioral and engagement signals that precede significant gift upgrades. These "hidden major donors" in your existing database represent some of the highest-ROI opportunities in nonprofit fundraising.
AI models trained on giving behavior can identify donors whose engagement patterns, gift timing, and relationship depth suggest they are ready for a major gift ask even when their current giving level would not ordinarily trigger major donor cultivation. The stat from DonorSearch showing AI-identified prospects with 20 times greater lifetime value than average is largely a function of this capability: AI identifies the high-potential donors that manual processes miss, allowing development officers to invest cultivation time where it will yield disproportionate returns.
- Run AI prospect scoring against your existing mid-level giving base as a first step
- Ensure development officers have capacity to follow up on AI-identified prospects before scaling volume
- Track upgrade rates before and after AI implementation to validate the model's accuracy for your donor base
6. Content Generation and Campaign Support
Using generative AI to increase development team throughput without sacrificing quality
Generative AI tools have become standard productivity tools for development teams that know how to use them well. Creating first drafts of donor appeals, generating subject line options for A/B testing, drafting grant narratives, and producing social media content all take significantly less time when AI can produce quality starting points that development professionals then edit and refine. The key word is "first drafts": organizations that treat AI output as finished content suffer quality and authenticity problems, while those that use AI to eliminate the blank-page problem and accelerate the drafting process report significant productivity gains.
The most valuable application of generative AI in fundraising communications is segmentation at scale. Rather than sending a single mass email to your entire donor list, AI can help create multiple versions of the same appeal tailored to different donor segments, geographic regions, program interests, or giving levels, allowing your one development writer to effectively produce the segmented communications that a team three times larger would previously have required.
- Always have a human development professional review and substantially edit AI-generated fundraising content
- Create detailed organizational voice guides and donor persona documents to improve AI output quality
- Track response rates for AI-assisted versus manually written communications to identify where AI adds the most value
Why Most Organizations Are Not Seeing These Results
The data makes AI's fundraising potential clear, but the 2026 adoption report paints an equally clear picture of why most organizations are not accessing that potential. While 92 percent of nonprofits use AI in some capacity, and 61 percent specifically use it for fundraising, only 7 percent describe their use as generating major improvements. That gap between widespread adoption and significant impact is not primarily a technology problem. It is an organizational readiness problem.
Why AI Fundraising Underperforms
- Ad hoc use without documentation: 81 percent of nonprofits use AI individually without shared workflows, meaning institutional knowledge about what works never develops
- No measurement framework: Organizations that do not establish baseline metrics before AI implementation cannot demonstrate ROI or identify which applications are working
- Siloed tool adoption: Using a single AI tool for email subject lines or one prospecting platform without integrating AI across the donor lifecycle produces fragmented gains
- Poor data quality: AI models trained on inconsistent, incomplete, or outdated donor data produce unreliable outputs; garbage in, garbage out applies with particular force to predictive analytics
- Insufficient staff training: Development staff who do not understand what AI can and cannot do will either underuse capable tools or over-rely on AI outputs without appropriate judgment
What High-Impact Organizations Do Differently
- Formal governance: Written AI policies, documented use cases, and clear accountability for AI-related decisions create institutional foundation for consistent results
- Shared workflows: AI is embedded in team processes, not individual staff habits, ensuring that insights and learning accumulate rather than sitting with individual employees
- Systematic measurement: Before-and-after tracking against defined metrics makes it possible to know which AI applications are generating returns and which need adjustment
- Cross-functional alignment: Fundraising, marketing, and program teams share AI tools and insights, avoiding situations where each department uses AI in isolation
- Predictive plus generative combination: The highest performers use predictive insights to inform generative AI outputs, connecting data-driven prospect analysis to personalized outreach
The pattern in the data is consistent: organizations with basic readiness factors in place, meaning governance, measurement, and documented shared workflows, are seeing impact that compounds over time. Those without these factors are plateauing or actually regressing despite continued AI adoption. The 2026 report suggests this divergence will widen throughout the year, making now the critical window to build the organizational infrastructure that allows AI to deliver its fundraising potential.
The Donor Trust Factor: What the Data Says About Disclosure
The fundraising data makes a compelling case for AI investment, but there is a critical variable that pure performance metrics do not capture: donor trust. Research from Fundraising.AI and the Association of Fundraising Professionals reveals that donor attitudes toward AI use by nonprofits are nuanced and consequential for organizations deciding how to communicate about their AI practices.
What Donors Think About Nonprofit AI Use
2025 donor perception data that every development director should know
Positive Signals
- 74 percent of online donors think nonprofits should use AI for marketing, fundraising, and administrative tasks (Fundraising.AI, 2025)
- 43 percent of donors say AI use would have a neutral or positive effect on their giving behavior
- Donor familiarity with AI increased 10 percentage points from 2024 to 2025, moving toward conditional optimism
Critical Concerns
- 31 percent of donors say they would be less likely to donate if they knew AI was used
- 34 percent cite AI bots portrayed as human fundraisers as their top concern
- 67 percent express concerns about data privacy and security in AI use
- 92 percent of donors say it is important for nonprofits to plainly disclose where and why AI is used
The most striking finding in the donor perception data is the 92 percent figure demanding plain disclosure. This near-universal expectation of transparency creates both a risk and an opportunity. Organizations that use AI without disclosure face reputational risk if donors discover it later and feel deceived. Organizations that proactively communicate their AI use, explaining the benefits it creates for donors (more relevant communications, less wasted time) and demonstrating the guardrails that protect donor data, can actually build trust through transparency.
The 34 percent concern about AI bots portrayed as humans is the most actionable finding. The clearest boundary in AI-assisted fundraising is between using AI to help human fundraisers work more effectively and using AI to impersonate human relationships with donors. The former is widely acceptable to donors; the latter undermines the authenticity that sustains long-term giving relationships. Development officers assisted by AI that identifies the right donor to call and the best talking points to use are augmenting human relationships, not replacing them. Chatbots presenting as individual major gift officers are crossing the line donors have identified as unacceptable.
Building Your AI Fundraising Strategy: A Practical Framework
The data makes clear that AI's fundraising potential depends more on organizational readiness than on which specific tools you use. Building that readiness requires a systematic approach that sequences investments to create compounding returns rather than investing in tools before the infrastructure to use them effectively is in place.
Stage 1: Foundation Building (Months 1 to 3)
Before investing in new AI tools, ensure the organizational infrastructure exists to use them effectively. This stage focuses on data quality, governance, and measurement baseline.
- Audit your donor data: Identify gaps, inconsistencies, and outdated records. AI models are only as good as the data they train on. Clean data is the single most important prerequisite for AI fundraising success.
- Document your current baselines: Record your current donor retention rate, average gift size, recurring donor count, and email engagement metrics. Without these baselines, you cannot measure AI's impact.
- Draft a basic AI governance policy: Establish what AI tools your fundraising team can use, how donor data will be handled, and what human review is required before AI-generated content is sent to donors.
- Develop your transparency approach: Decide how you will disclose AI use to donors in your communications and on your website, addressing the 92 percent expectation of plain disclosure.
Stage 2: High-ROI Tool Implementation (Months 4 to 9)
With foundations in place, implement AI tools in the highest-ROI categories first. The data suggests donation form optimization and email personalization deliver accessible, measurable returns with relatively low implementation complexity.
- Migrate to or optimize your current donation platform with AI-powered form features; track average gift size before and after
- Implement AI subject line optimization and send time personalization in your email platform; measure open and click rates against previous campaigns
- Begin using generative AI for first drafts of donor communications with defined review processes and quality standards
- Run basic predictive scoring on your existing donor database to identify major gift prospects; assign cultivation priorities based on AI scores
Stage 3: Integration and Optimization (Months 10 to 18)
With early tools in place and data accumulating, expand AI integration across the donor lifecycle and begin combining predictive and generative capabilities for compound effects.
- Implement AI retention modeling to identify at-risk donors before they lapse; build automated outreach workflows triggered by AI-generated risk scores
- Connect predictive prospect data with personalized appeal generation; development officers present donors with AI-informed briefings before major conversations
- Conduct a systematic review of what has worked, document institutional learnings, and build shared workflows that preserve knowledge when staff turn over
- Report AI fundraising results to board, including before-and-after metrics, to secure ongoing investment in AI capacity building
Organizations that follow this sequence, building the governance and data foundations before layering in AI tools, are the ones showing up in the high-impact cohort in sector research. The tools themselves are widely available and in many cases affordable even for smaller organizations. The differentiating factor is the organizational readiness that allows tools to deliver on their potential. Reviewing your broader AI strategy alongside your fundraising-specific approach ensures these investments connect to your organization's overall direction.
Conclusion
The fundraising data tells a clear story: AI is delivering measurable improvements in donation size, retention, and major gift identification for organizations that deploy it with the right infrastructure. A 40 percent increase in average donation size and a 56 percent improvement in major gift rates are not marketing claims; they are documented outcomes from real nonprofit fundraising programs. The organizations achieving these results have demonstrated that AI's fundraising potential is real and accessible.
What the data also reveals is that these results are not automatic. The 93 percent of nonprofits using AI without seeing major impact are not failing because AI does not work; they are falling short because they have adopted tools without the governance, data quality, measurement practices, and shared workflows that allow AI to compound its returns over time. Closing that gap is the practical fundraising challenge that the 2026 data makes impossible to ignore.
The window for building competitive AI fundraising capability is still open, but the 2026 report is clear that the organizations pulling ahead now will maintain those advantages as their AI systems accumulate learning and their teams develop expertise that is increasingly hard to replicate quickly. For development directors and nonprofit leaders, the choice between investing in that infrastructure now and waiting for more certainty is itself a strategic decision with direct revenue implications.
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