How to Participate in AI Pilots and Research Studies
Tech companies, foundations, and academic institutions are actively seeking nonprofit partners for AI research and pilot programs. These partnerships offer access to cutting-edge technology, expert guidance, and often funding—but many nonprofits don't know these opportunities exist or how to pursue them. This guide shows you how to find, apply for, and maximize value from AI research collaborations.
The AI landscape for nonprofits has transformed significantly in recent years, and one of the most exciting developments is the growing ecosystem of research partnerships, pilot programs, and funded initiatives designed specifically for mission-driven organizations. OpenAI has committed $50 million to support nonprofits through its People-First AI Fund. The GitLab Foundation is awarding at least $4 million in grants for AI demonstration projects. Google.org's Accelerator has helped organizations build AI solutions expected to reach more than 30 million beneficiaries. The National AI Research Resource Pilot connects nonprofits to computational resources and AI expertise through partnerships with federal agencies and major technology companies.
Yet despite these opportunities, many nonprofits remain unaware of or intimidated by the prospect of participating in AI research. Common barriers include uncertainty about where to find opportunities, concerns about not having technical expertise, fear of the time commitment required, and questions about whether participation would actually benefit the organization. These concerns are understandable but often overstate the barriers and understate the benefits.
The truth is that researchers and technology companies need nonprofit partners as much as nonprofits need their expertise and resources. Academic institutions need real-world environments to test and validate AI approaches. Technology companies need mission-driven use cases that demonstrate AI's potential for social good. Foundations and government agencies need evidence that AI investments create genuine impact. This mutual need creates opportunities for nonprofits willing to engage—even those without technical backgrounds or prior research experience.
This article provides a comprehensive guide to participating in AI pilots and research studies. We'll explore where to find opportunities, how to prepare compelling applications, what to expect from the participation experience, and how to maximize value for your organization. Whether you're looking for funded pilot programs, academic research partnerships, or technology company beta programs, you'll find practical guidance for pursuing these opportunities effectively.
Types of AI Research Opportunities for Nonprofits
AI research opportunities for nonprofits come in many forms, each with different requirements, benefits, and levels of commitment. Understanding the landscape helps you identify which opportunities best match your organization's capacity, needs, and goals.
Funded Grant Programs
Financial support for AI implementation and experimentation
Several major technology companies and foundations offer grant programs specifically for nonprofits exploring AI. These programs typically provide direct funding, technology credits, training, and expert support. Grant amounts range from $25,000 for early-stage programs to $250,000 or more for demonstration projects.
Major Programs (2026)
- OpenAI People-First AI Fund ($50M total commitment)
- GitLab Foundation AI for Economic Opportunity ($4M+ in grants)
- Google.org Accelerator: Generative AI ($30M with support)
- Google.org AI Opportunity Fund ($17M for training)
What's Typically Included
- Direct financial grants ($25K-$250K+)
- Technology credits and platform access
- Pro bono technical assistance
- Cohort-based learning and peer networking
Academic Research Partnerships
Collaborations with universities on AI research projects
Universities are increasingly seeking nonprofit partners for AI research that addresses real-world social challenges. These partnerships connect your organization with faculty expertise, graduate student researchers, and academic resources. The National AI Research Institutes program alone connects over 500 institutions across the U.S., many actively seeking community organization partners.
- Research collaboration: Partner on faculty research projects studying AI applications in your sector
- Graduate student projects: Host students working on AI-related theses or capstone projects
- Data partnerships: Provide anonymized data for academic research in exchange for insights
- Regional AI alliances: Join coalitions connecting academic institutions with community organizations
Technology Company Beta Programs
Early access to new AI tools and platforms
Many technology companies offer beta testing programs for new AI features, often with preferential access for nonprofit organizations. These programs provide early access to capabilities before general release, opportunities to influence product development, and often discounted or free access during the beta period. Participation typically requires less formal commitment than research partnerships but still offers significant value.
- Early feature access: Test new AI capabilities months before general availability
- Product influence: Provide feedback that shapes how features develop
- Direct vendor access: Build relationships with product teams
- Reduced costs: Often free or heavily discounted during beta periods
Government-Sponsored Research Initiatives
Federally funded programs supporting AI for social good
The National Artificial Intelligence Research Resource (NAIRR) Pilot represents a major federal investment in democratizing AI research access. Led by the National Science Foundation in partnership with 13 federal agencies and 28 nongovernmental partners, NAIRR provides computational resources, datasets, and expertise to organizations working on societal challenges. This initiative specifically emphasizes including community organizations and nonprofits in AI research.
NAIRR Pilot Resources
- Access to computational resources for AI research
- Open ecosystem of data, models, and evaluation tools
- Training and technical support from research partners
- Focus on use-inspired research addressing societal challenges
Finding AI Research Opportunities
AI research opportunities are scattered across multiple sources, and many aren't widely advertised. Developing a systematic approach to finding these opportunities ensures you don't miss relevant programs while avoiding the overwhelm of tracking too many sources.
Direct Sources
- Tech company philanthropy pages: Google.org, Microsoft Philanthropies, Salesforce.org, Meta for Good
- Foundation program pages: GitLab Foundation, Schmidt Futures, Patrick J. McGovern Foundation
- Federal research portals: NSF, NAIRR Pilot, grants.gov (for AI-related opportunities)
- University research offices: Contact local universities about partnership opportunities
Community Resources
- NTEN: Technology learning community for nonprofits with AI resources
- Fast Forward: AI for Humanity newsletter and tech nonprofit network
- TechSoup: Nonprofit technology resources including AI program listings
- Sector associations: Your field's professional associations often share relevant opportunities
Building Relationships That Lead to Opportunities
Many research partnerships emerge from relationships rather than formal applications. Building connections with researchers, technology professionals, and other nonprofits engaged in AI work creates pathways to opportunities that never appear in public announcements.
- Attend AI for social good conferences: Events like AI for Good Global Summit, nonprofit technology conferences
- Engage with local university AI programs: Attend public lectures, reach out to relevant faculty
- Join nonprofit technology communities: Online forums, Slack groups, LinkedIn communities focused on nonprofit AI
- Connect with corporate partners: Existing corporate relationships may have AI research arms seeking nonprofit partners
Consider designating someone on your team to monitor AI research opportunities systematically. This doesn't require significant time—setting up Google Alerts for relevant terms, subscribing to key newsletters, and checking major sources monthly can surface most relevant opportunities. The key is consistency rather than comprehensiveness; it's better to track a few high-quality sources reliably than to occasionally scan dozens of sources.
Preparing Strong Applications
Successful applications for AI research programs share common characteristics regardless of the specific opportunity. Understanding what funders and research partners are looking for helps you present your organization effectively and increases your chances of selection. Importantly, most programs explicitly welcome organizations without prior AI experience—they're looking for good mission fit and organizational capacity, not technical sophistication.
What Research Partners Look For
Mission Alignment
Clear connection between your mission and the research program's focus. Programs targeting economic opportunity, education access, or healthcare equity want partners whose core work addresses these challenges.
- • Compelling articulation of your mission and impact
- • Evidence of sustained commitment to your cause
- • Alignment with the program's stated priorities
Organizational Capacity
Ability to engage meaningfully in the research process. This doesn't mean technical capacity—it means having staff who can dedicate time, leadership support, and organizational stability.
- • Identified staff to participate in the program
- • Leadership buy-in for AI exploration
- • Organizational stability and track record
Clear Problem Definition
Well-articulated challenges that AI might help address. Researchers want partners who understand their problems deeply, even if they don't know the technical solutions.
- • Specific, concrete challenges you face
- • Understanding of why current approaches fall short
- • Vision for how AI might help (even if vague)
Potential for Impact
Opportunity to demonstrate meaningful results. Research programs want success stories they can share; partners serving large populations or addressing urgent needs are attractive.
- • Scale of potential impact (people served)
- • Measurability of outcomes
- • Potential for insights that benefit the broader sector
Application Best Practices
- Lead with mission, not technology: Start with the problem you're trying to solve and the people you serve, not with technical aspirations or AI buzzwords
- Be specific about your challenges: Vague statements about "improving efficiency" are less compelling than concrete examples like "spending 15 hours weekly on data entry that delays client intake"
- Acknowledge what you don't know: Programs value honesty about limitations over inflated claims of AI readiness; saying "we're not sure how AI could help, but we're eager to explore" is often more effective than overpromising
- Demonstrate commitment: Show that you've thought about how participation fits your organization's priorities and that leadership supports the investment of time and energy
- Highlight your unique perspective: What insights can your organization offer that researchers couldn't get elsewhere? Your deep understanding of your community and sector is valuable
- Include evidence of past adaptability: Examples of how you've adopted new technologies or processes in the past demonstrates capacity for change
Common Application Mistakes to Avoid
- Overstating technical readiness: Claiming more AI experience than you have creates unrealistic expectations and can lead to poor fit
- Generic applications: Submitting the same application to multiple programs without customizing for each program's specific focus
- Focusing on technology wants: Emphasizing what AI tools you want rather than what problems you need to solve
- Underestimating time commitment: Agreeing to participation requirements you can't actually meet leads to poor outcomes for everyone
Maximizing Value from Participation
Getting selected for a research program or pilot is just the beginning. How you engage during the program determines whether participation becomes a transformative experience or just another initiative that fails to gain traction. Organizations that approach these opportunities strategically extract significantly more value than those who participate passively.
Internal Preparation
- Designate a champion: Identify a staff member who will own the relationship and ensure follow-through
- Brief leadership: Ensure executives understand the commitment and are prepared to remove obstacles
- Engage stakeholders early: Involve staff who will use resulting tools from the beginning
- Document starting point: Measure baseline metrics so you can demonstrate impact later
During Participation
- Engage actively: Attend all sessions, complete assignments, provide thoughtful feedback
- Ask questions: Research partners want to help; don't hesitate to seek clarification or additional support
- Build peer relationships: Other participants face similar challenges; these connections often outlast the program
- Document learnings: Keep notes on insights, challenges, and solutions for future reference
Building on Program Outcomes
The end of a formal program shouldn't be the end of your AI journey. Successful organizations use participation as a launching point for sustained AI development, leveraging what they've learned and the relationships they've built.
Immediate Follow-up
- • Synthesize learnings into actionable recommendations
- • Brief board and leadership on outcomes and next steps
- • Identify quick wins you can implement immediately
- • Request continued connection with program alumni
Long-term Integration
- • Incorporate learnings into your AI strategy
- • Maintain relationships with research partners for future collaboration
- • Share experiences with peer organizations
- • Apply for follow-on programs building on demonstrated progress
Organizations that participate successfully in one program often become more attractive candidates for future opportunities. Program alumni frequently receive invitations to subsequent cohorts, referrals to other research partnerships, and opportunities to share their experiences at conferences or in publications. Building a reputation as a thoughtful, engaged research partner opens doors that formal applications alone cannot.
Developing Academic Research Partnerships
Academic partnerships deserve special attention because they offer sustained engagement rather than one-time programs. Universities are increasingly seeking community organization partners for AI research that addresses real-world challenges, and these partnerships can provide ongoing value that extends far beyond a single project. The Connecticut AI Alliance, for example, brings together 16 academic institutions and six community organizations to collaborate on shared research, educational programs, and workforce training—a model being replicated across the country.
How to Approach Universities
- Start with the research office: Most universities have offices dedicated to community partnerships; these teams can help identify appropriate faculty and programs
- Identify relevant departments: Computer science, data science, and business schools often have AI-focused research; social work, public policy, and sector-specific schools (education, health) may have researchers studying AI applications in your field
- Attend public events: Lectures, symposiums, and research presentations provide opportunities to meet faculty and understand their interests
- Leverage board connections: Board members with academic connections can make introductions that cold outreach cannot
- Consider graduate programs: MBA, MPA, and data science programs often seek nonprofit partners for capstone projects
Structuring Productive Partnerships
Successful academic partnerships require clear agreements about expectations, deliverables, and intellectual property. Universities and nonprofits operate on different timelines and with different incentives; addressing these differences upfront prevents misunderstandings later.
Key Discussion Points
- • Who owns resulting intellectual property?
- • What data will be shared, and how will it be protected?
- • What time commitment is expected from your staff?
- • How will research findings be published or shared?
- • What happens when the formal project ends?
Benefits for Both Parties
- • For you: Access to expertise, students, computational resources
- • For researchers: Real-world data, validation of approaches, impact evidence
- • For students: Meaningful projects, resume-building experience
- • For both: Publishable results, grant application strength
Academic partnerships work best when both parties approach them as genuine collaborations rather than one-way transfers of expertise. Researchers bring technical knowledge; you bring deep understanding of the problem domain, relationships with affected communities, and insights about what will actually work in practice. The most successful partnerships leverage both forms of expertise to create solutions that are technically sound and practically useful.
For more guidance on building these relationships, see our article on collaborating with universities on nonprofit AI research.
Addressing Common Concerns
Nonprofits considering AI research participation often have legitimate concerns that deserve direct address. Understanding these concerns and how to mitigate them helps organizations make informed decisions about whether participation is right for them.
"We don't have technical expertise"
This is perhaps the most common concern—and the most overstated barrier. Most AI research programs explicitly welcome organizations without technical backgrounds. OpenAI's People-First AI Fund states clearly that applicants "don't need to be currently using AI tools and don't need previous AI experience." Research partners provide the technical expertise; what they need from you is deep understanding of the problems worth solving and the communities you serve.
Your job isn't to be a technologist—it's to be an expert in your mission. That expertise is precisely what researchers need and often cannot find elsewhere. Building basic AI literacy among your team is helpful but not required for most programs.
"We can't spare the time"
Time commitment is a real consideration, and programs vary significantly in their demands. Some beta testing programs require only a few hours monthly; intensive accelerator programs might require 10-15 hours weekly during peak periods. Understanding the specific commitment before applying ensures you can meet expectations.
Consider whether participation time might actually save time overall. Programs that help you implement AI tools effectively can reduce administrative burden for years to come. The question isn't just "can we afford the time?" but "can we afford not to invest in learning that could transform our operations?"
"What about data privacy and ethics?"
Protecting the data and dignity of the people you serve is non-negotiable. Legitimate research programs understand this and have established protocols for data protection, informed consent, and ethical review. Before participating, understand exactly what data will be shared, how it will be protected, and what ethical oversight is in place.
Ask potential partners about their IRB (Institutional Review Board) processes, data security measures, and approaches to community consent. Walking away from programs that can't provide satisfactory answers protects your organization and the people you serve. For guidance on these issues, see our article on data privacy and AI security.
"Will this actually benefit our organization?"
Not every opportunity is right for every organization. Before applying, assess whether the program's focus aligns with your actual needs. A program focused on AI for fundraising won't help if your real challenge is service delivery. Honest assessment of fit prevents wasting time on poorly matched programs.
Even programs that don't result in immediate tool implementation provide value through learning, relationships, and clarity about your organization's AI readiness. Some participants discover that they're not ready for AI adoption—valuable insight that prevents wasted future investment. Others discover opportunities they hadn't considered.
Taking the First Step
The ecosystem of AI research opportunities for nonprofits has never been richer or more accessible. Major technology companies, foundations, universities, and government agencies are actively seeking nonprofit partners who can help ensure AI development serves the public good. These partnerships offer access to expertise, resources, and technology that would otherwise be out of reach for most mission-driven organizations.
The barriers that prevent most nonprofits from pursuing these opportunities—perceived lack of technical expertise, uncertainty about where to look, concerns about time and resources—are real but often smaller than they appear. Programs are designed to meet organizations where they are, not where they think they need to be. The most important qualifications are mission commitment, organizational capacity, and genuine interest in exploring how AI might advance your work.
Start by identifying one or two opportunities that align with your organization's focus and capacity. Subscribe to newsletters from Fast Forward, NTEN, and relevant tech company philanthropy programs. Reach out to your local university's research partnership office. Talk to peer organizations that have participated in AI programs. Small steps compound over time, building the relationships and knowledge that lead to meaningful opportunities.
The organizations that will benefit most from AI in the coming years are those that engage now—not because they have all the answers, but because they're willing to learn. AI research programs exist precisely to help organizations like yours navigate this transition. The question isn't whether you're ready, but whether you're willing to begin.
Ready to Explore AI Research Opportunities?
We help nonprofits identify and pursue AI research partnerships, pilot programs, and funded initiatives that align with their mission. From opportunity identification to application support, we can guide your organization toward productive AI collaborations.