What Funders Now Expect from AI-Enabled Nonprofits
Understanding the evolving landscape of foundation and donor expectations around AI—and how to position your organization for success in an increasingly technology-focused philanthropic environment.
The philanthropic landscape is shifting beneath nonprofits' feet. Ten of America's most influential foundations—including MacArthur, Ford, Omidyar Network, Mellon, and Packard—recently announced Humanity AI, a $500 million initiative dedicated to ensuring AI delivers for people and communities. OpenAI's Foundation has distributed $40.5 million in unrestricted grants to 208 nonprofits through their People-First AI Fund. Major donors increasingly want to know not just what you're doing, but how technology amplifies your impact. The question facing nonprofit leaders isn't whether funders care about AI—it's understanding exactly what they expect and how to demonstrate you meet those expectations.
This shift creates both opportunities and challenges. Organizations that demonstrate thoughtful AI adoption may find themselves more competitive for funding, as funders increasingly favor initiatives with long-term relevance and adaptability in an uncertain technological future. Yet nearly 90 percent of foundations currently provide no AI implementation support, and fewer than 15 percent plan to increase this in the next three years. This disconnect means nonprofits must often navigate AI adoption without funder guidance while still meeting evolving expectations about how they use technology to advance their missions.
This guide unpacks what foundations and major donors now expect from AI-enabled nonprofits. We'll explore the key questions funders are asking, how they evaluate AI-inclusive proposals, the transparency and disclosure requirements emerging in the sector, how different funders approach AI-related giving, and practical strategies for positioning your organization to meet these expectations. Whether you're applying for grants, cultivating major gifts, or simply trying to understand where philanthropy is headed, this comprehensive overview will help you navigate funder expectations with confidence.
Understanding these expectations isn't just about winning grants—it's about aligning your organization with the evolving standards of effective, responsible practice that funders are increasingly using to evaluate impact across their portfolios.
The Shifting Funder Landscape in 2026
The funder community's relationship with AI is evolving rapidly, though not uniformly. Research from the Center for Effective Philanthropy reveals that almost two-thirds of nonprofits and foundations report that none or just a few of their organization's staff members have a solid understanding of AI and its applications. This creates a challenging dynamic: funders increasingly expect nonprofits to leverage technology effectively, yet many lack the expertise to evaluate AI proposals or provide meaningful implementation support.
Two distinct approaches characterize foundation AI-related work. The first is more forward-looking and conceptual—seeking to preserve worker dignity and stave off AI risks. Funders in this category, including the Patrick J. McGovern Foundation, Eric and Wendy Schmidt, Omidyar Network, and participants in Humanity AI, focus on the broader implications of AI for society. The second bucket focuses on more tangible, short-term applications of AI to advance specific priority areas like global development, medical research, education, and economic mobility. Understanding which approach your funders favor helps you frame AI discussions appropriately.
The funding gap is real. Only 20 percent of funders currently provide grantees with money for technology tools and resources, and just 11 percent of nonprofits say foundation grants contribute significantly to their technology budgets. This means organizations often pursue AI adoption through operational budgets, unrestricted gifts, or specialized technology grants. The disconnect between funder expectations for technology adoption and funder support for technology investment creates genuine challenges for resource-constrained nonprofits.
Yet opportunities are expanding. Major AI-focused funding initiatives are emerging, including KPMG Foundation's $6 million commitment to help nonprofits integrate AI, GitLab Foundation's $250,000 grants with technical support, and sector-specific AI funds targeting education, healthcare, and social services. The AI for Nonprofits Sprint aims to bring 100,000 nonprofit staff to baseline AI literacy in 2026. Funders increasingly recognize that technology capacity is essential to impact, creating new funding streams for organizations positioned to take advantage of them. Understanding how to navigate funding uncertainty becomes critical in this environment.
Major AI Funding Initiatives (2026)
- Humanity AI: $500M from 10 major foundations for responsible AI implementation
- OpenAI People-First Fund: $40.5M in unrestricted grants to 208 nonprofits
- KPMG Foundation: $6M to help nonprofits integrate AI into operations
- GitLab Foundation: $250K grants plus technical support and API credits
Challenges in the Current Landscape
- 90% of foundations provide no AI implementation support
- Only 20% of funders provide technology funding to grantees
- Two-thirds of funders lack confidence evaluating AI proposals
- Only 9% of foundations have AI advisory groups
The Key Questions Funders Are Asking
When foundations evaluate AI-inclusive proposals in 2026, they're looking for organizations that demonstrate thoughtful integration of technology with mission. Two questions dominate: First, how does AI amplify your impact rather than simply automate tasks? Funders want to see that technology serves mission advancement, not efficiency for its own sake. Second, what safeguards ensure AI enhances rather than replaces human judgment and relationships? The nonprofits that thrive are those that can articulate how AI enables more meaningful human work, not less of it.
Beyond these foundational questions, funders increasingly probe specific aspects of AI implementation. They want to understand your organization's AI governance—the policies, oversight structures, and accountability mechanisms that ensure responsible use. They ask about data practices—how you collect, protect, and use information in AI systems. They inquire about staff capacity—whether your team has the skills to implement AI effectively or whether you've planned for training and development. And they assess organizational readiness—whether AI adoption fits your current infrastructure and strategic priorities.
Critically, funders are interested in long-term sustainability, not just initial implementation. They want to see that AI adoption plans include ongoing maintenance, continuous learning, and adaptation as technology evolves. Proper AI implementation entails setting up the right infrastructure, training staff, and maintaining an ongoing cycle of data collection, analysis, and improvement. Organizations that present AI as a one-time project rather than an ongoing capability may find funders skeptical of their long-term success.
The ability to articulate clear answers to these questions requires the kind of strategic thinking outlined in developing a strategic AI plan for your organization.
Questions to Prepare For in Funder Conversations
What foundations want to understand about your AI approach
Mission Alignment
- • How does AI specifically advance your mission outcomes?
- • What problems are you solving with AI that couldn't be addressed otherwise?
- • How do beneficiaries experience AI-enhanced services?
Responsible Implementation
- • What governance structures oversee your AI use?
- • How do you protect privacy and ensure data security?
- • What steps address potential bias in AI systems?
Capacity and Sustainability
- • What training have staff received for AI implementation?
- • How will you maintain and update AI systems over time?
- • What's your plan if specific AI tools become obsolete?
Impact Measurement
- • How do you measure AI's contribution to outcomes?
- • What would success look like with this AI investment?
- • How will you know if AI implementation isn't working?
AI Governance and Policy Expectations
The governance gap in nonprofit AI adoption is stark: 76% of nonprofits do not have an AI policy, yet funders increasingly expect evidence of responsible oversight. While only 30% of foundations currently have AI policies themselves, the trend toward formalized governance is clear. Organizations that can demonstrate thoughtful AI policies position themselves as responsible, forward-thinking partners worthy of investment—particularly as funders themselves develop more sophisticated expectations around technology governance.
What constitutes adequate AI governance varies by organization size and AI usage sophistication, but certain elements appear consistently in funder expectations. A clear acceptable use policy that defines what AI tools staff can use, for what purposes, and with what oversight is increasingly table stakes. Data protection protocols that ensure AI systems don't compromise privacy or security demonstrate organizational maturity. Accountability structures—who is responsible for AI decisions, how are errors handled, what recourse exists for those affected—signal serious engagement with the ethical dimensions of technology adoption.
Some funders are explicitly requiring AI governance documentation. Grant applications increasingly include questions about technology policies, data practices, and ethical frameworks. While these requirements aren't yet universal, their frequency is increasing. Organizations with established policies can respond confidently to these requirements; those without may find themselves scrambling to create documentation under deadline pressure or, worse, losing funding opportunities to better-prepared competitors.
Developing comprehensive AI governance doesn't require reinventing the wheel. Resources like AI acceptable use policy templates and guidance on sector-specific policy frameworks can accelerate your organization's governance development while ensuring you address the elements funders most commonly evaluate.
Policy Essentials
- Acceptable use guidelines for staff
- Data protection and privacy protocols
- Approved AI tools and purposes
- Human oversight requirements
Ethical Framework
- Bias monitoring and mitigation
- Transparency with beneficiaries
- Equity considerations in AI use
- Mission alignment verification
Accountability Structures
- Designated AI oversight role
- Board-level technology reporting
- Error handling and correction procedures
- Regular policy review schedule
Transparency and Disclosure Requirements
The question of disclosing AI use to funders is increasingly complex. Research indicates that 23% of foundations will not accept grant applications with content created by generative AI, while only 10% explicitly accept AI-assisted applications. A substantial 67% remain undecided. This ambiguity creates risk for nonprofits unsure whether to disclose AI use in their applications or how extensively they can leverage AI tools in grant writing processes.
The emerging consensus favors transparency. Many foundations believe the broader use of generative AI is inevitable and that, as long as grantees accurately describe their mission and their work, it is ultimately of little consequence whether AI assisted in drafting materials. However, misrepresenting AI involvement—or using AI in ways that produce inaccurate descriptions of programs or capabilities—risks serious damage to funder relationships. The safest approach is proactive disclosure, presented matter-of-factly as a tool that enhances productivity while maintaining accuracy and authenticity.
Beyond grant applications, funders increasingly expect transparency about AI use in programs and operations. Disclosing your use of generative AI fosters trust and promotes ethical practices. Unlike private-sector organizations, nonprofits are accountable to donors, boards, constituents, and the public. Transparency around how you use AI—and what you don't use it for—builds trust with all stakeholders, including funders evaluating your organization's integrity and judgment.
Practical approaches to disclosure include publishing a public-facing statement on AI usage that outlines your commitment to ethical, secure, and responsible AI practices. This demonstrates proactive governance rather than reactive compliance. The guidance on disclosing AI use to funders provides detailed frameworks for navigating these conversations effectively.
Best Practices for AI Disclosure to Funders
Navigating transparency in funder relationships
In Grant Applications
If AI assisted in drafting your application, consider a brief, matter-of-fact disclosure: "This proposal was drafted with AI assistance for efficiency, with all content reviewed and verified by staff for accuracy." This demonstrates transparency without being defensive.
In Program Descriptions
When AI is part of your program delivery, explain clearly how it enhances services while maintaining human oversight. Frame AI as a tool that enables staff to focus on relationship-building and judgment-intensive work.
In Impact Reporting
If AI tools help analyze data or generate reports, note this in your methodology. Funders increasingly appreciate efficiency gains, especially when paired with evidence of human verification and meaningful interpretation.
Proactive Communication
Don't wait for funders to ask. Include brief AI usage summaries in regular funder updates, demonstrating that responsible AI governance is part of your organizational culture, not just a compliance checkbox.
Understanding Individual Donor Expectations
Donor attitudes toward nonprofit AI use vary significantly, creating a nuanced landscape for organizations to navigate. Research reveals that 43% of donors say AI use would have a positive or neutral effect on their giving, while 31% say they would be less likely to donate to organizations using AI. This split underscores the importance of understanding your specific donor base rather than assuming universal reactions. Notably, the more generous the donor, the more likely they are to support AI-enabled nonprofits: 30% of high-value donors express support compared to 19% of medium donors and just 13% of small donors.
Major donors increasingly want to know more about where their donations go, and insights driven from machine learning can provide the proof points they seek. These donors often appreciate the efficiency and impact measurement capabilities that AI enables. They understand that technology investments can multiply their philanthropic impact and are often sophisticated enough to recognize when AI is being used responsibly versus inappropriately. Transparency about AI use, framed in terms of enhanced impact measurement and operational efficiency, tends to resonate with this segment.
Smaller donors may have more concerns about AI, often related to fears about impersonal communication, job displacement among nonprofit staff, or general discomfort with technology. When communicating with broader donor audiences, emphasize the human elements that AI enables—more personal outreach, better matching of donors to causes they care about, more time for staff to build relationships. AI can also support financial and impact transparency by automatically generating donor reports or visualizing campaign data, strengthening donor trust when paired with human oversight and clear communication.
Regardless of donor segment, the principle of donor-centric communication applies. Frame AI in terms of what it enables for donors and beneficiaries, not in terms of organizational efficiency alone. The guidance on navigating the donor AI paradox provides deeper insights into managing these varied expectations effectively.
What Major Donors Appreciate
- Enhanced impact measurement and reporting capabilities
- Operational efficiency that maximizes programmatic spending
- Forward-thinking approach to organizational sustainability
- Sophisticated use of data to demonstrate results
- Evidence of thoughtful, responsible implementation
Addressing Common Donor Concerns
- "AI will make communications impersonal" — Show how AI enables more personalized, timely outreach
- "AI will replace staff" — Explain augmentation vs. replacement model
- "My data isn't safe" — Detail privacy protections and data governance
- "AI is bias" — Describe oversight and equity-focused implementation
Demonstrating AI's Impact to Funders
Funders ultimately care about outcomes—whether AI investments translate into greater mission impact. The ability to demonstrate clear connections between AI adoption and improved results differentiates organizations that receive continued or increased funding from those that struggle to justify technology investments. This requires thinking carefully about metrics, documentation, and storytelling before implementation begins.
Start with clear baseline measurements. Before implementing AI tools, document current performance on relevant metrics: staff time spent on administrative tasks, response times to client inquiries, accuracy rates in data processing, or whatever measures most directly relate to your planned AI use. These baselines enable compelling before-and-after comparisons that demonstrate value. Without baselines, you're left with claims that sound impressive but lack evidence.
Connect efficiency gains to mission outcomes. Funders often accept that AI can save time, but they want to know what happens with that saved time. If AI reduces grant reporting time by 50%, did staff redirect those hours to client services? Did caseloads increase? Did program quality improve? The most compelling AI impact stories show chains of causation: AI automated X, which freed staff for Y, which resulted in outcome Z. This narrative demonstrates that AI serves mission rather than existing for its own sake.
Be honest about limitations and learning. Funders appreciate candor about what worked, what didn't, and how you're adjusting. The organization that reports "our AI chatbot initially struggled with complex client questions, so we added human escalation protocols and saw satisfaction scores improve" appears more credible than one claiming flawless implementation. Understanding how to measure AI success helps you tell compelling stories grounded in evidence.
Impact Metrics That Resonate with Funders
What to measure and report about your AI investments
Efficiency Metrics
- Time saved on specific administrative tasks
- Cost reduction in specific operations
- Error rate improvements in data processing
- Response time improvements for inquiries
Impact Metrics
- Increased clients served with same resources
- Improved service quality indicators
- Enhanced outcomes measurement capability
- Better donor/client retention rates
Positioning for AI-Focused Grants
The emergence of dedicated AI funding streams creates opportunities for organizations positioned to take advantage of them. OpenAI explicitly welcomes applications from organizations at every stage of AI adoption—from exploration to pilots and active deployment. Applicants don't need previous AI experience to be competitive. This openness to nascent AI users reflects funders' recognition that many impactful organizations are just beginning their AI journeys.
Multi-partner proposals are increasingly favored for AI grants. Foundations favor proposals that bring together nonprofits, universities, and tech organizations—sharing both resources and expertise. If your organization lacks internal AI expertise, consider partnerships with technology companies, academic institutions, or consultants who can strengthen your application and implementation capacity. These partnerships also demonstrate the collaborative, learning-oriented approach funders want to see.
Funders can address technology gaps with flexible, multiyear, unrestricted funding that covers the full lifecycle of adoption. When applying for AI grants, acknowledge this reality: effective statements of work are co-developed with funders to define needs, implementation plans, and adoption supports with clear accountabilities, including training and continued support after launch. Proposals that address only tool procurement without accounting for training, maintenance, and organizational change management may appear naive about what successful AI implementation actually requires.
The nonprofit AI catch-22 is real: organizations often need early capital to prove their ideas but can attract funders only after results are visible. Reports urge donors to break that cycle with earlier, flexible support. Frame your proposals around learning and iteration, not just execution. Funders supporting AI innovation understand that some experiments won't succeed, and honest acknowledgment of uncertainty may be more compelling than overconfident projections. Resources on funder research and strategy can help identify the right opportunities for your organization.
Strengthening AI Grant Applications
- Connect AI investment clearly to mission outcomes
- Demonstrate organizational readiness and governance
- Include comprehensive training and support plans
- Identify partnership opportunities to strengthen capacity
- Address sustainability beyond initial funding period
Common Application Weaknesses
- Vague connections between AI and mission impact
- Focus on tools without addressing organizational change
- Inadequate budget for training and ongoing support
- Overconfident projections without acknowledging learning curves
- No plan for measuring and demonstrating impact
Building Funder Confidence in Your AI Approach
Beyond specific grant applications, building funder confidence in your AI approach requires consistent demonstration of thoughtful, responsible practice. This means proactively sharing AI updates with funders rather than waiting to be asked, including AI progress in regular reporting and conversations. It means acknowledging mistakes and course corrections openly, demonstrating that you're learning and adapting. It means connecting AI investments to outcomes funders care about, telling compelling stories about how technology serves mission.
Consider inviting funders into your AI journey. Some organizations have successfully engaged foundation program officers in site visits focused on technology implementation, giving funders firsthand exposure to how AI works in practice. Others include technology updates in board reports shared with major funders, normalizing AI as part of organizational operations. These approaches build understanding and confidence over time, making future AI-related requests feel less novel and more like natural extensions of proven capability.
Address funder concerns preemptively. If you know a foundation prioritizes data privacy, proactively share your security protocols. If a donor has expressed skepticism about AI, acknowledge the concern and explain your human-centered approach. Research indicates that nonprofit leaders believe none or just a few of their foundation funders understand their organization's AI-related needs or concerns—you can differentiate yourself by making funders feel genuinely informed and involved rather than kept at arm's length.
The goal is for funders to see your organization as a thoughtful, responsible AI adopter whose technology investments they can confidently support. This reputation builds over time through consistent demonstration of the principles outlined in this guide: mission alignment, responsible implementation, transparency, governance, and measurable impact.
Ongoing Funder Engagement Strategies
Building confidence through consistent communication
- Quarterly updates: Include brief AI progress summaries in regular funder reports, normalizing technology as part of operations
- Site visit opportunities: Offer technology-focused visits that show AI implementation in practice
- Honest progress reports: Share what's working, what isn't, and how you're adjusting—funders appreciate candor
- Impact storytelling: Connect AI efficiency gains to mission outcomes with specific examples
- Governance sharing: Proactively share AI policies and oversight structures to demonstrate responsibility
- Learning community participation: Share insights with peer organizations and funders, positioning yourself as a thought leader
Meeting the Moment in Funder Relations
The philanthropic landscape's relationship with AI is evolving rapidly. Funders' expectations are becoming more sophisticated as they gain experience evaluating AI-enabled organizations. The gap between what funders expect and what funders support remains real, but organizations that demonstrate thoughtful AI adoption are increasingly well-positioned for funding success. Understanding these dynamics helps you navigate conversations confidently and position your organization effectively.
The core message from funders is consistent: AI should amplify mission impact, not automate for its own sake. Organizations that can articulate how technology serves beneficiaries, demonstrate responsible governance, maintain transparency with stakeholders, and measure genuine outcomes will thrive in this environment. Those that treat AI as a buzzword or pursue technology without clear mission connection will find funders increasingly skeptical.
Your path forward involves preparation—developing governance frameworks, establishing impact metrics, and building narratives that connect technology to mission. It involves transparency—being honest about AI use, limitations, and learning. It involves relationship building—engaging funders in your AI journey rather than presenting finished solutions. And it involves continuous learning—staying current with evolving funder expectations and adapting your approach accordingly.
The organizations that succeed won't necessarily be those with the most sophisticated AI implementations. They'll be those that integrate technology thoughtfully into their missions, communicate effectively with funders about their approach, and demonstrate the kind of responsible innovation that builds confidence over time. By understanding and meeting funder expectations, you position your organization for sustained support in an increasingly technology-focused philanthropic environment.
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