The 2026 Nonprofit AI Adoption Report

A Benchmark Study of 346 Organizations. Published February 2026

1. Executive Summary

Fundraising has never been more complex. Donor expectations are higher. Data is overwhelming in volume and scattered in silos. Staff capacity is stretched. And now, AI tools promise to help, but for many teams, the path forward still feels fuzzy.

This benchmark study was designed to bring clarity. In December 2025, we surveyed 346 nonprofit organizations to understand not just whether nonprofits are using AI, but where they’re using it, what outcomes they’re actually seeing, and what’s preventing deeper progress.

What we found is a sector moving fast on adoption but slower on durable results. Most teams are gaining efficiency, while only a small percentage are building sustainable practices, such as governance, measurement, shared workflows, and cross-team alignment that turn AI from a personal shortcut into a true organizational capability. The following research and playbook maps the landscape, identifies the barriers at each stage, and outlines the structural moves to help organizations break through the efficiency plateau and build capacity in 2026.

The nonprofit sector adopted AI quickly in 2025. 92% of organizations now use AI tools in some capacity.

But adoption only tells part of the story. The survey found a significant gap between AI usage and AI impact. 

While nearly every organization has experimented with AI, only 7% report major improvements in their ability to achieve their mission. Most organizations function at the efficiency stage: faster drafts, quicker responses, and time saved on routine tasks. The real opportunity is building durable, organization-wide practices that outlast individual use cases and scale across teams.

Current State of AI Adoption in Nonprofits

Five findings define the current state:

1. High adoption, low transformation. 92% are using AI, but 65% describe their use as reactive and individual. Only 7% have embedded AI into goals, budgets, and strategy. Frequency of use has outpaced organizational readiness.

2. The efficiency plateau is real. 79% report small to moderate improvements. Only 7% report major improvements that change what the team can get done, like doubling prospect research capacity, personalizing donor communication at scale, or reallocating staff time from execution to relationship strategy. Most organizations are getting faster at existing tasks, but not meaningfully expanding what they can accomplish.

3. Readiness foundations are missing. 47% have no AI governance policy. 81% use AI on an ad hoc basis without documented workflows. By governance, we mean simple, shared guidance that answers three questions: what AI use is encouraged, what requires approval, and what is off limits, especially when donor data or confidential information is involved. These gaps keep organizations from moving beyond individual experimentation into repeatable, team-level capability.

4. Barriers shift as organizations mature. Organizations not yet using AI cite training and guidance needs. Organizations already using AI daily cite time, privacy concerns, and staff skepticism. The challenges evolve as adoption deepens, requiring different solutions at different stages.

5. A gap is forming. Organizations with basic readiness factors of governance, measurement, and systematic use are pulling ahead. Those without are plateauing or regressing. The pattern suggests this gap will widen throughout 2026.

These findings show up most clearly in fundraising, where speed without structure hits a ceiling.

What This Means for Fundraising Work

AI is moving from experiment to infrastructure. It is completely reasonable to move carefully, especially if you’ve had early experiences where AI output felt unreliable, ineffective, or out of step with your donor’s needs. However, the question is no longer whether to use AI, but how to use it in ways that strengthen trust, deepen relationships, and actually improve fundraising outcomes.

Organizations seeing the biggest outcomes: 

1. Assign a cross-functional AI team
2. Establish AI governance ownership
3. Create an acceptable use policy
4. Document what works
5. Pair the right tools with the right strategy
6. Measure results

Fortunately, these foundations take weeks to build, not months.

The data shows a clear choice ahead. Organizations can continue using AI reactively and accept moderate efficiency gains. Or they can invest in structure and longevity, pursuing outcomes that change fundraising capacity, like deeper prospect coverage, more relevant donor communication, and smarter allocation of staff time toward relationship strategy.

Most organizations are using scattered tools. What they lack is clarity on how to move from experimentation to capability, and a unified AI toolbox that supports a more personal, more trustworthy, more Responsive donor experience.

-Nathan Chappell and Gabe Cooper 

2. About the Study

Should you be using AI more? Different tools? With more structure? 

The answers have been hard to find because nonprofit-specific data has been scarce. This benchmark provides that clarity. 

It shows how organizations across the sector are actually using AI in fundraising and operations right now.

Survey Design

This benchmark surveyed 346 nonprofit organizations in December 2025 about their AI adoption, impact, and readiness.

Participant Profile
Organizations ranged from under $1M to over $50M in annual revenue. 52.8% were mid-sized organizations ($1M to $20M). 

Key roles represented: fundraising leaders (31%), executive leadership (24%), operations (18%), and marketing (15%).

What We Measured
We looked at AI usage and frequency, organizational adoption level, impact achieved, use cases, barriers, governance, measurement practices, and data readiness.

How We Define AI
We focused on generative AI tools (ChatGPT, Claude, Copilot, Gemini) and AI-powered features in existing software. We excluded automation tools that can be connected with AI.

Why This Matters
The findings reveal both opportunity and caution. AI is delivering real value for fundraising work. But most organizations are leaving significant impact on the table by treating AI as a personal productivity tool rather than building it into how their teams work.

The Organizations Behind This Study

This report is presented by Virtuous and Fundraising.AI. 

Who is Virtuous? The only AI-powered Responsive Fundraising Platform, Virtuous is an integrated suite of enterprise-grade software built to connect personally with every donor, automate time-consuming workflows, and turn data into action. The Virtuous platform creates future-ready, data-powered teams, equipped for personal donor connections that grow giving. 

→ Learn more about Virtuous HERE.

Who is Fundraising.AI? Fundraising.AI is an independent collaborative focused on defining, promoting, and advancing responsible and beneficial use of AI in nonprofit fundraising, grounded in trust, ethics, privacy, and transparency. It equips fundraisers, nonprofit leaders, and policymakers with frameworks, resources, and governance practices to ensure AI strengthens charitable participation and community impact without compromising donor trust.

→ Learn more about Fundraising.AI HERE.

3. The Current AI Landscape in Nonprofit Fundraising

Adoption Has Moved Quickly

92% of organizations are using AI in some capacity.

This represents rapid adoption of a technology that became widely available less than two years ago. For context, only 8% of organizations report no AI use at all.

The sector has moved past the awareness phase. Nearly everyone is experimenting.

Depth of Use Varies Dramatically

When asked how AI is being used organizationally:

• 65% describe reactive, individual use (one-off prompts, personal experimentation)
• 18% describe operational use (team workflows, shared prompts, repeatable processes)
• 7% describe strategic use (embedded in goals, budget, and key productivity indicators)

*(Please note: In order to give an accurate picture of AI usage across the industry, participants had the opportunity to select multiple answers or no answer at all, so the percentage results will not always add up to 100.)

The gap between 92% adoption and 7% strategic integration reveals the current challenge. Organizations know how to try AI. They have not yet figured out how to build it into how they work.

The Efficiency Plateau Is Real

79% report small to moderate improvements. Only 7% report major strategic impact.

Impact breaks down as follows:

• No impact yet: 14%
• Small improvements in specific areas: 40%
• Moderate improvements in efficiency and quality: 39%
• Major improvements in capacity and strategic priorities: 7%

Organizations achieving moderate impact describe time savings, faster content creation, and improved draft quality. 

Organizations achieving major impact describe more exponential outcomes: doubled prospect research capacity, personalized communication at a large scale, and staff time reallocated from execution to strategy.

One fundraising leader captured the plateau: “We’ve been using AI for over a year now. Everyone on my team uses ChatGPT for drafts and research. We’re definitely faster. But if you asked me if we’ve fundamentally changed what we’re capable of as an organization, I honestly don’t think so. We’re just doing the same things more efficiently.”

What this means: Most organizations have reached the efficiency plateau. They are getting faster at existing work but not changing what they can accomplish. 

The 32-point gap between moderate impact (39%) and major impact (7%) represents the current sector challenge. 

Efficiency is valuable. But transformation requires moving beyond individual productivity to systematic team capability.

4. AI Adoption Today: Where AI Is Actually Being Used

Content and Communications Lead

The survey asked organizations to identify where they use AI. The results show that most teams use AI for donor communications, followed by marketing and data work. Here are the top areas:

• Donor communications and content: 62%
• Marketing and social media: 60%
• Email and creative work: 60%
• Data analysis and reporting: 42%
• Operations and internal automation: 24%

Fundraisers are using AI where it is easiest to start: content creation and communication. These use cases require minimal setup and produce immediate results.

More Complex Use Cases Lag Behind

The lower adoption in data work (42%) and operations (24%) is notable. These areas could deliver more strategic value but require better data quality and more systematic implementation.

The pattern reveals both progress and limitation. Organizations are successfully using AI for routine content tasks. They have not yet extended AI to more complex fundraising challenges like personalization at a large scale, prospect scoring, gift modeling, or strategic planning.

Most Use Remains Ad Hoc

81% use AI individually and on an ad hoc basis. Only 4% have documented, repeatable workflows.

This represents the most significant operational gap revealed by the survey: Organizations have moved from “no one using AI” to “everyone using AI their own way” without capturing what works.

Organizations described discovering that individual team members had developed their own AI approaches independently, with limited team communication around these approaches. The result: multiple people solving the same problems in isolation, reinventing solutions others had already found.

One responder highlighted the fragmentation: “AI needs to be integrated more across systems. Right now, it’s hard to automate because different AIs are working in different systems.”

The pattern creates predictable problems:

• Knowledge walks out the door when people leave
• Teams solve the same problems independently
• Successful approaches remain invisible to others
• New staff start from zero rather than building on existing knowledge

What this means: Individual experimentation is appropriate at the early stage. But the transition from experimentation to capability requires capturing knowledge.

The gap between 81% ad hoc and 4% systematic represents the primary opportunity for the sector. Most organizations have spent a year experimenting. Now, the opportunity lies in documenting what they learned and sharing it across their teams.

5. Barriers and Bottlenecks to AI Progress

Barriers Shift as Organizations Mature

The biggest barriers differ for organizations at different stages.

For organizations not yet using AI, the top barriers are:

• Lack of training: 48%
• Need for guidance on getting started: 44%
• Capability concerns: 44%

For organizations already using AI regularly, different barriers emerge:

• Time and capacity constraints: 31%
• Privacy and security concerns: 32%
• Staff skepticism based on experience: 19%

The evolution follows a predictable pattern. Organizations initially worry about technical capability. After gaining experience, those concerns fade, replaced by structural challenges: time to use AI properly, clarity on use case priorities, and governance needs. The barriers shift from perceptual to operational.

Early Barriers Are Perceptual

Organizations overestimate the difficulty of starting. The 48% citing lack of training is notable, especially because AI tools are designed to be accessible without technical training. The barrier is not technical capability but organizational clarity about what to try and how to evaluate results.

Later Barriers Are Structural

Once organizations start, they discover real structural barriers: time to use AI well, data privacy questions, and staff concerns about quality and appropriateness.

What this means: Training and getting-started guides help early adopters. But organizations using AI regularly need different support: governance frameworks, measurement systems, and strategies for systematic implementation.

Size Creates Different Challenges

Small organizations face capability challenges. Large organizations face strategy and coordination challenges.

When broken down by size, barriers diverge:

Small organizations (under 50 staff):

• Capability and training: 35% (vs. 21% for large orgs)
• Budget constraints: 32% (vs. 24% for large orgs)

Large organizations (50+ staff):

• Strategy and prioritization: 27% (vs. 18% for small orgs)
• Privacy or security concerns: 44% (vs. 25% for small orgs)

What this means: The same technology creates different organizational challenges based on size and structure.

Small organizations need practical examples and simple frameworks they can implement without deep technical resources. Their barriers center on capability and confidence: knowing where to start and whether they’re using AI appropriately.

Large organizations need strategic clarity and coordination mechanisms to prevent fragmented adoption across departments. Their challenge is not capability but coherence: aligning multiple teams securely while using different tools toward shared goals.

The data also reveals a surprising finding: Small organizations report achieving moderate impact at slightly higher rates than large organizations (41% vs. 34%), despite facing more resource constraints. This suggests that organizational complexity, not resources, may be the bigger barrier to AI impact.

6. AI Readiness: Where Nonprofits Stand Today

The Governance Gap

47% of organizations have no AI governance policy.

Among organizations using AI regularly, governance approaches are as follows:

• No policy yet: 48%
• Cautious policies (allowed with approvals or limited scope): 23%
• Enabling policies (clear guardrails and encouraged use): 19%
• Restrictive policies (most uses limited or prohibited): 6%
• Not sure: 4%

Most organizations are operating without clear boundaries about appropriate AI use. Staff are uncertain what’s allowed, especially with donor data or confidential information. Leadership cannot encourage broader use when the rules are unclear. The lack of governance keeps AI at the individual experimentation stage.

What this means: Most organizations are operating without clear boundaries about appropriate AI use. This creates two problems:

1. It slows adoption. Staff are uncertain about what is allowed, especially with donor data or confidential information. Without guidance, the default response is caution.
2. It prevents confident scaling. Leadership cannot encourage broader use when the rules are unclear.

The distinction between restrictive and enabling governance matters. Organizations with enabling policies (clear about what is encouraged, what requires approval, and what is prohibited) achieve major impact at higher rates as opposed to organizations with restrictive policies or no policies at all.

Governance does not need to be complex. Organizations achieving impact often describe simple, one-page guidelines created in a single meeting.

The Measurement Problem

Most organizations are not systematically tracking whether AI is helping.

The survey found varied approaches to measurement:

• Not measuring impact yet: High percentage across all maturity levels
• Informal observation: Most common approach
• Time savings tracking: Small minority
• Outcome measurement: Very rare

Organizations cannot optimize what they do not measure. Without measurement, decisions about AI are based on intuition and anecdote rather than evidence.

The absence of measurement has three consequences: 

1. Organizations cannot prove ROI when leadership asks. 
2. They cannot identify which use cases actually work and which just feel productive. 
3. They cannot build momentum for investment because results remain unclear.

Measurement does not need to be sophisticated. Organizations achieving impact often start with simple approaches: tracking time saved on specific tasks, comparing output quality, or counting use cases that become standard practice.

Three Readiness Patterns Emerge

The data reveals three groups based on readiness factors:

Organizations with foundations (estimated 20%):

• Have some governance (even informal)
• Measure results in some way
• Document or share successful approaches
• Are positioned to scale and report accelerating returns

Organizations at decision point (estimated 60%):

• Use AI regularly
• Have not yet systematized their approach
• Risk plateau without intentional structure

Organizations without foundations (estimated 20%):

• Experiment with AI, but lacking bigger-picture structures
• Lack governance, measurement, and documentation
• Likely to plateau or regress

One responder identified the path forward: “Create Efficient Workflows and Document Processes.”

Organizations that have begun creating efficient workflows and documenting processes describe accelerating returns: new staff get trained quickly, everyone uses proven approaches, and teams iterate rather than starting from scratch. 

7. The Future of AI in Nonprofits

Frequency Does Not Equal Maturity

Organizations using AI daily are not necessarily more mature in their approach than organizations using it weekly.

The survey found many organizations where everyone uses AI constantly (for drafts, for brainstorming, for research), but the organization has no strategy, no governance, no measurement, and no documentation. Usage is high. Organizational capability is minimal.

This challenges the assumption that the path forward is simply “use AI more.” Frequency of use has already outpaced organizational readiness for most of the sector.

What Separates Organizations Achieving Major Impact

Organizations reporting major improvements share a pattern. They moved beyond treating AI as a personal productivity hack and built it into team operations.

The investment required is smaller than most assume. Organizations achieving transformation describe simple interventions: 

• A shared document with five proven prompts 
• A one-page policy created in a single meeting
• Tracking time saved for clear metrics on results across departments

These are structural decisions that take just days to implement and will compound over time.

Two Paths Forward

Organizations face a clear choice in 2026:

1. The first path: Continue using AI reactively. Accept moderate efficiency gains. Individuals get faster at existing tasks. The organization’s capacity stays roughly the same.
2. The second path: Invest in a small amount of structure. Document what works. Establish clear boundaries. Measure results. Aim for outcomes that change fundraising capacity: deeper prospect coverage, more relevant donor communication, smarter allocation of staff time toward relationship strategy.

Both paths are legitimate. But the data shows which organizations will pull ahead.

The Window for Action Is Now

The opportunity in 2026 is to move from “we use AI” to “AI is built into how we work.”

Once organizational patterns solidify (“this is just how we use AI”), they become harder to change. The sector is still in the experimentation phase. Teams are still learning. Knowledge has not yet calcified into habit.

This fluidity creates opportunity. Organizations can still shape how AI becomes part of their work. They can document what is working, establish smart boundaries, and build measurement into their practice before patterns lock in.

The path each nonprofit chooses could define fundraising capacity for years to come.

8. Next Steps: Moving from Experimentation to Capability

The data is clear. Organizations achieving impact have moved beyond individual experimentation to build AI into how their teams work.

The good news: You do not need sophisticated technology or large budgets to make this shift. You need clarity and structure.

Six Actions to Get Started

1. Assign a cross-functional AI team

Begin by naming a small, cross-functional group responsible for guiding how AI is used across the organization. This is the first step in moving from experimentation to capability. The purpose of this team is governance and alignment, ensuring that AI use reflects organizational values, donor trust, and mission priorities before it scales.

AI is not an IT initiative. It is a multi-stakeholder, culture-shaping process. The team should include people close to day-to-day fundraising work, operations, data, and leadership. Their role is practical and focused: identify high-yield, low-risk problems where AI can support the work, test approaches intentionally, and document what delivers real outcomes. As these efforts succeed, the organization gains a repeatable foundation it can build on, rather than a collection of isolated experiments.

2. Establish AI governance ownership

With a team in place, define how AI decisions get made and revisited over time. Governance is about ownership, accountability, and direction. This includes who evaluates new AI tools, who decides when experiments become standard practice, and how impact, risk, and priorities are reviewed at the organizational level.

Effective governance creates continuity and prevents AI strategy from living only with individuals or disappearing when roles change. In practice, this is often a lightweight charter that defines decision rights, review cadence, and success criteria. 

Instead of starting from scratch, use our pre-made AI Governance Policy Prompt Template HERE, which you can copy and paste into your preferred AI tool.

3. Create an acceptable use policy

Governance sets direction. Acceptable use makes that direction actionable. An acceptable use policy translates governance into clear, day-to-day guidance staff can rely on. Its role is to remove uncertainty, not slow people down. When expectations are shared and explicit, teams use AI more confidently and more responsibly.

Gather your AI team for one focused hour and decide three things:

• What AI use is encouraged
  Drafting, research, internal brainstorming, and early-stage analysis.
  
• What requires approval
  Donor-facing communications, grant writing, public messaging, or any output that represents the organization externally.
  
• What is prohibited
  Use of confidential data, sensitive donor information, or final outputs without human review.
  

Write this guidance in plain language. Share it with staff. Revisit it as your practices evolve. 

Start with our Acceptable Use Policy Prompt Template HERE, which you can copy and paste into your preferred AI tool and adapt to your organization.

4. Document what works

Spend 30 minutes capturing the five AI approaches your team uses most often. Have your AI team create a shared document. Make it accessible to everyone. When someone discovers something new that works, add it.

This is not complex documentation. It is basic knowledge capture that prevents teams from solving the same problems independently.

5. Pair the right tools with the right strategy

Not all AI tools solve the same problems. Once priorities are clear, choose tools that reinforce the outcomes you want to achieve, not just general productivity gains. The goal is to move beyond experimentation and give teams consistent, repeatable support in their daily work.

For example, Virtuous Momentum is designed to help fundraisers translate AI insights into action. Rather than asking gift officers to decide where to focus or how to start, Virtuous Momentum provides a daily outreach plan, surfaces donors ready to re-engage, and drafts personalized messages in the fundraiser’s own voice…all using AI. This structure is especially powerful for teams managing large portfolios, onboarding new fundraisers, or trying to re-activate dormant donors without adding manual work.

The Life You Can Save used Virtuous Momentum to equip one of their fundraisers with clear daily priorities, personalized outreach support, and data-driven insight into which donors needed attention. Within five months, he reconnected with hundreds of inactive donors and raised over $75,000, while spending more time on meaningful donor conversations instead of guesswork. Read more about his story HERE. 

6. Measure results

Pick one metric to track for 30 days:

• Time saved on specific tasks
• Quality improvements in outputs
• Number of use cases that become standard practice

Start simple. The goal is to move from intuition to evidence (not sophisticated analytics).

These six actions take less than a week of calendar time, yet they can help unlock the path from efficiency to transformation.

9. Where Virtuous Can Help: Fundraising Products at the Cutting Edge of AI

At Virtuous, we build AI tools designed specifically for nonprofit fundraising. Our approach focuses on AI that strengthens donor relationships and improves fundraising outcomes, not just productivity. 

Virtuous Momentum

Virtuous Momentum, our AI-powered fundraising agent, helps fundraisers prioritize outreach, draft personalized messages in their voice, create and update donor plans, and automatically log activity back to the CRM.

“[Virtuous Momentum] keeps me going, gives me a plan in the morning, allows me to plan for those donor phone calls that I need to make…” — Pam Colbert, Former Mid-Level Gift Officer, Metropolitan State University of Denver

“For a recent email campaign, I set aside six hours, expecting to need all of that time, but with [Virtuous] Momentum the entire process only took 20 minutes. The ease and efficiency were incredible.” — Gerad Borrego, Director of Development, Koinonia Family Services

To see Virtuous Momentum in action, schedule a demo now.

[Book a Momentum Demo]

Virtuous CRM+ & Virtuous Insights

Virtuous Insights, our AI-powered prospecting tool within Virtuous CRM+, uses machine learning to bring first- and third-party data together into a single, predictive view of donor behavior. By combining your CRM data with wealth, demographic, and engagement signals, Insights reveals patterns that static reports and manual research cannot surface, helping fundraisers see where giving is most likely to grow.

“Virtuous has opened up opportunities to try new things that previously felt too difficult or time-intensive. Tasks and initiatives that once seemed overwhelming now feel possible and often surprisingly simple.” 

— Katy Dorais, Director of Annual Giving and Grants, Alive

Book a demo to see it for yourself. 

[Book a Virtuous Insights Demo]

____

The rules of fundraising have changed. Donor expectations are evolving quickly, and teams face more pressure than ever to keep up. 

AI has only accelerated these pressures. Used well, it can help teams work more efficiently and prioritize the tasks requiring a human touch; without structure, it often adds noise instead of clarity. The real challenge is learning how to use this new technology in ways that strengthen donor relationships and support the work fundraisers do every day.

At Virtuous, we build tools for modern fundraisers navigating this shift. Our products are designed to create future-ready, data-powered teams that grow giving in a personalized way. AI is embedded where it supports better decisions, more personal engagement, and consistent follow-through, always with trust and accountability at the center.

You do not have to navigate this change alone. We are honored to provide the tools and structure fundraisers need to stay human-first and AI-forward. 

About This Research

This benchmark research was conducted by Virtuous in December 2025 as part of our commitment to helping nonprofits understand and navigate the changing landscape of fundraising technology.

Report design and analysis: Virtuous Team
Published: February 2026

Scott Holthaus

Scott Holthaus is the Partner Marketing Manager at Virtuous, where he helps nonprofits grow generosity and implement responsive fundraising. Before joining the Virtuous team, Scott spent 15+ years working in local nonprofits and ministries in a variety of roles, including marketing and communications. Scott lives in the Chicago area with his wife, three kids, and Great Dane/English Mastiff, Bertie.

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