One Hundred Nights
    Back to Articles
    Technology & Innovation

    How Nonprofits Can Share AI Resources and Build Cooperative Tech Infrastructure

    Individual nonprofits often lack the budget, expertise, or infrastructure to implement AI effectively on their own. But what if organizations could pool resources, share technology platforms, and collectively build the AI infrastructure the sector desperately needs? Cooperative technology models offer a powerful alternative to going it alone—reducing costs, increasing capabilities, and accelerating adoption across the nonprofit sector. This article explores how organizations can collaborate to share AI resources through shared services centers, group purchasing arrangements, open-source initiatives, and collaborative governance models that benefit everyone involved.

    Published: January 10, 2026•12 min read•Technology & Innovation
    Nonprofits collaborating on shared AI infrastructure and technology resources

    The promise of AI for nonprofits is undeniable. From automating administrative tasks to improving program outcomes and strengthening fundraising efforts, artificial intelligence offers transformative potential. Yet for many organizations, that potential remains frustratingly out of reach. Limited budgets, technical expertise gaps, and infrastructure constraints create substantial barriers to AI adoption—particularly for small and mid-sized nonprofits that lack the resources of larger institutions.

    But there's an alternative path forward. Rather than each organization struggling to build AI capabilities independently, nonprofits can pool resources, share infrastructure, and collectively build the technology platforms they need. This cooperative approach isn't new—nonprofits have successfully shared resources in areas like back-office operations, facilities, and human resources for decades. Now, that same collaborative model can unlock access to AI tools and infrastructure that would otherwise be financially or technically out of reach.

    According to recent research, 84% of AI-powered nonprofit respondents identify funding as the most significant barrier to developing and scaling AI initiatives. Meanwhile, 78% lack organization-wide AI policies, and many organizations simply don't have the knowledge or infrastructure to explore AI meaningfully. Cooperative technology models directly address these challenges by distributing costs, pooling expertise, and creating shared governance frameworks that reduce individual organizational burden while increasing collective capability.

    Society asks nonprofits to solve 21st-century problems with 20th-century technology. Cooperative AI infrastructure offers a way to bridge that gap—not by asking each organization to become a technology leader, but by enabling the sector to build shared platforms that serve everyone. From shared services centers that provide AI capabilities as a service, to group purchasing arrangements that leverage collective bargaining power, to open-source initiatives that customize technology for nonprofit needs, collaborative models make AI accessible, affordable, and appropriate for organizations of all sizes.

    This article explores practical strategies for building cooperative AI infrastructure. You'll learn how to identify the right partners, choose appropriate governance models, navigate technical and legal considerations, and create sustainable funding mechanisms. Whether you're a small organization looking to access capabilities beyond your budget or a larger institution ready to lead collaborative initiatives, you'll find actionable guidance for sharing AI resources effectively. The goal isn't to create one-size-fits-all solutions, but to help you identify the cooperative approaches that make sense for your organizational context and community needs.

    Why Cooperative AI Infrastructure Matters for Nonprofits

    The case for cooperation extends beyond simple cost savings. While financial benefits matter—particularly for resource-constrained organizations—the strategic advantages of shared AI infrastructure reshape what's possible for the nonprofit sector. Cooperative models democratize access to sophisticated technology, accelerate learning curves, reduce duplicative efforts, and create economies of scale that benefit all participants.

    Most importantly, cooperation enables nonprofits to maintain mission focus while building technical capabilities. Rather than every organization needing to become an AI expert, shared infrastructure allows organizations to access sophisticated tools through simplified interfaces, with technical complexity handled by specialized teams or platforms. This means program staff can leverage AI for their work without needing to understand machine learning algorithms, just as they use email without understanding network protocols.

    Reduced Costs and Shared Investment

    AI tools, infrastructure, and expertise are expensive. Pooling resources dramatically reduces individual organizational costs while providing access to better technology than any single organization could afford independently.

    • Spread software licensing costs across multiple organizations
    • Share infrastructure expenses for servers, storage, and computing power
    • Collectively employ technical staff who serve multiple organizations
    • Distribute costs of security, compliance, and ongoing maintenance

    Pooled Expertise and Knowledge Sharing

    AI adoption requires specialized knowledge that most individual nonprofits lack. Cooperative models create channels for sharing expertise, learning from peer experiences, and building collective knowledge faster than any organization could alone.

    • Access specialized technical expertise without hiring full-time staff
    • Learn from peer implementations and avoid common pitfalls
    • Share training materials, best practices, and use case documentation
    • Collectively negotiate for training and support from vendors

    Increased Bargaining Power with Vendors

    Technology vendors offer better pricing, terms, and support when serving larger customer bases. Cooperative purchasing gives nonprofits collective bargaining power similar to that of enterprise customers, resulting in significant savings and improved service.

    • Negotiate volume discounts on AI software and services
    • Access enterprise-tier features at nonprofit-appropriate pricing
    • Influence product roadmaps to serve nonprofit sector needs
    • Secure better support agreements and service level guarantees

    Improved Security and Compliance

    AI security and compliance require specialized expertise most nonprofits can't afford. Shared infrastructure allows organizations to implement enterprise-grade security, privacy protections, and compliance frameworks collectively.

    • Implement robust security measures beyond individual org capabilities
    • Share costs of compliance audits, certifications, and monitoring
    • Collectively address data privacy regulations and ethical AI standards
    • Establish shared governance frameworks for responsible AI use

    Research from organizations like NTEN and NetHope demonstrates that nonprofits recognize the need for cooperative approaches. Organizations understand they need "coalitions, shared infrastructure, and cross-sector collaboration with technologists, policymakers, and funders" to unlock AI's full potential. The infrastructure exists in other sectors—corporate enterprises routinely share technology platforms, and government agencies increasingly pursue cooperative procurement. The nonprofit sector can adapt these proven models to its unique needs and values, creating infrastructure that serves mission-driven work while maintaining the autonomy and identity of individual organizations.

    Cooperative AI infrastructure doesn't mean surrendering organizational independence or mission focus. Well-designed collaborative models preserve organizational autonomy while providing access to shared resources. Just as nonprofits maintain distinct identities while participating in shared services centers for accounting or HR, they can leverage shared AI infrastructure while customizing applications for their specific programs and populations. The key is structuring cooperation in ways that balance efficiency gains with appropriate flexibility for diverse organizational needs.

    Models for Sharing AI Resources Across Nonprofits

    Cooperative AI infrastructure can take many forms, from informal knowledge-sharing networks to formal shared services centers. The right model for your organization depends on factors like organizational size, technical sophistication, budget constraints, existing partnerships, and the specific AI capabilities you need. Most successful approaches combine multiple models—using group purchasing for software licenses while participating in an open-source development community, for example.

    Understanding the strengths, limitations, and implementation requirements of each model helps you make informed decisions about which approaches fit your organizational context. The models described below aren't mutually exclusive—they represent complementary strategies that can be mixed and matched based on your needs and circumstances.

    Shared Services Centers for AI Infrastructure

    Formal organizations that provide AI capabilities as a service to member nonprofits

    Shared services centers establish a central entity—often structured as a nonprofit itself—that builds and maintains AI infrastructure serving multiple member organizations. This model has proven successful for administrative functions like accounting and HR, and it translates naturally to technology services. Member organizations typically pay subscription fees or usage-based charges that collectively fund the center's operations, including staff, infrastructure, and software licenses.

    How Shared Services Centers Work:

    • Centralized Technical Team: The center employs specialized AI staff who serve multiple organizations, making expertise affordable that individual orgs couldn't justify
    • Shared Infrastructure: Computing resources, data storage, and software platforms are purchased and maintained centrally, with costs distributed across members
    • Standardized Security: The center implements enterprise-grade security, compliance monitoring, and data governance that would be prohibitively expensive for individual organizations
    • Common Platforms with Custom Configurations: Core AI tools are standardized to maximize efficiency, but can be configured for each organization's specific needs

    Real-world examples demonstrate the viability of this approach. Community Service Partners in Chicago had six member nonprofits integrate the same IT platform, saving $100,000 in the first year plus an additional $96,000 from other efficiencies. While this example predates modern AI tools, the same principles apply: standardization creates efficiency, shared staff provide expertise, and distributed costs make sophisticated technology accessible.

    Best Suited For:

    • Groups of organizations with similar technical needs and compatible missions
    • Organizations willing to standardize on common platforms in exchange for cost savings
    • Situations where long-term commitment justifies upfront investment in governance and infrastructure
    • Communities with existing collaborative relationships and trust among organizations

    Group Purchasing and Collective Procurement

    Consortiums that negotiate volume discounts and favorable terms with AI vendors

    Group purchasing leverages collective buying power to secure better pricing and terms from AI vendors without requiring shared technical infrastructure. Organizations remain independent but benefit from discounts available only to larger customer bases. This model works particularly well for software-as-a-service AI tools where vendors can easily serve multiple organizations through the same platform.

    Organizations like TechSoup have pioneered group purchasing for nonprofit technology, demonstrating how intermediaries can negotiate with major vendors on behalf of the sector. For AI specifically, this might involve negotiating enterprise licenses for tools like Microsoft 365 Copilot (currently $25.50 per user/month for eligible nonprofits) or securing discounted rates for AI-powered CRM, grant writing, or impact measurement platforms.

    Note: Prices may be outdated or inaccurate.

    Key Components of Group Purchasing:

    • Coordinating Organization: A trusted intermediary (existing nonprofit support org, consortium, or new entity) manages vendor relationships and member participation
    • Volume Commitments: Members commit to minimum purchase volumes or contract terms that justify vendor discounts
    • Standardized Terms: The group negotiates contract language addressing nonprofit-specific concerns like data privacy, ethics, and accessibility
    • Collective Influence: The group can request features, integrations, or support arrangements that individual orgs couldn't command

    Implementation Considerations:

    Group purchasing requires less infrastructure than shared services centers, but successful implementation still requires coordination. Organizations need mechanisms for collective decision-making about which vendors to engage, how to structure agreements, and how to handle vendor performance issues. Transparency about pricing, terms, and vendor selection criteria builds trust among participating organizations.

    The coordinating organization typically earns small administrative fees or vendor commissions that fund its operations. This creates sustainability without requiring member dues, though it also creates potential conflicts of interest that should be disclosed and managed through governance policies.

    Best Suited For:

    • Organizations seeking cost savings without major changes to operations or governance
    • Purchasing standardized AI tools available as SaaS products
    • Situations where organizations have diverse technical needs but overlapping vendor relationships
    • Building initial collaborative relationships that might evolve into deeper partnerships

    Open-Source AI Tools and Collaborative Development

    Community-driven development of AI tools specifically designed for nonprofit needs

    Open-source approaches leverage collaborative development to create AI tools owned and maintained by the nonprofit community rather than commercial vendors. This model provides maximum flexibility and transparency while allowing organizations to customize tools for their specific needs. Organizations with technical capabilities can contribute to development, while less technical orgs benefit from using the resulting tools.

    The open-source AI ecosystem includes powerful frameworks like TensorFlow, PyTorch, and platforms like Hugging Face that make advanced AI accessible without expensive proprietary licenses. The Linux Foundation AI & Data Foundation exemplifies how neutral governance structures can coordinate open-source AI development, creating collaborative environments that accelerate innovation while ensuring technology remains accessible.

    Advantages of Open-Source AI for Nonprofits:

    • Cost Accessibility: No licensing fees for software, though implementation and maintenance require technical resources
    • Transparency: Organizations can inspect code to verify ethical AI practices, understand how algorithms work, and audit for bias
    • Customization: Tools can be modified to serve nonprofit-specific needs, from grant writing assistance to program outcome tracking
    • Community Support: Diverse developer communities provide ongoing enhancement, with contributions from organizations worldwide
    • Data Sovereignty: Organizations retain complete control over data and can host tools on their own infrastructure

    Challenges and Mitigation Strategies:

    Open-source AI requires more technical expertise than commercial products. Organizations need developers who can implement, customize, and maintain tools—capabilities many nonprofits lack. However, intermediary organizations can bridge this gap by packaging open-source tools into user-friendly applications, providing implementation support, or offering hosted versions that retain open-source benefits while reducing technical burden.

    Sustainability presents another challenge. Commercial vendors have business models that fund ongoing development, while open-source projects depend on community contributions. Successful nonprofit open-source initiatives often combine foundation grants, member organization contributions, and donated technical resources from corporate partners. Organizations like the Open Source Initiative work to ensure open-source AI development serves diverse communities, not just those with technical resources.

    Best Suited For:

    • Organizations with technical capacity or access to developer volunteers
    • Situations requiring maximum transparency and control over AI systems
    • Communities that can coordinate collaborative development and maintenance
    • Organizations prioritizing data sovereignty and avoiding vendor lock-in

    Knowledge Networks and Learning Communities

    Collaborative platforms for sharing expertise, training resources, and implementation experiences

    Not all cooperative infrastructure involves sharing technology directly. Knowledge networks focus on pooling expertise, creating learning resources, and documenting implementation experiences that help all participants adopt AI more effectively. These networks lower adoption barriers by reducing the learning curve and helping organizations avoid common mistakes.

    Organizations like NTEN demonstrate this model through their AI Resource Hub, designed as a central repository offering training and tools to assess and manage AI. NTEN provides a 13-course AI certificate program, creating standardized training that individual organizations couldn't develop alone. NetHope similarly offers collaborative toolkits and working groups where nonprofits share AI knowledge and resources.

    Components of Effective Knowledge Networks:

    • Shared Learning Resources: Training materials, implementation guides, use case documentation, and template policies that members can adapt
    • Peer Networks: Forums, working groups, and communities of practice where practitioners share experiences and troubleshoot challenges
    • Expert Access: Connections to AI specialists, consultants, and vendors who understand nonprofit contexts
    • Standardized Frameworks: Common vocabularies, assessment tools, and evaluation methods that enable comparing experiences across organizations

    Knowledge networks complement other cooperative models. Organizations participating in shared services centers or group purchasing arrangements benefit from knowledge networks that help them maximize value from those investments. Similarly, knowledge networks help less technical organizations leverage open-source tools by providing documentation, training, and implementation support.

    Best Suited For:

    • Organizations in early stages of AI exploration seeking guidance
    • Building sector-wide capacity without requiring specific technology commitments
    • Complementing other cooperative models with learning and support infrastructure
    • Lower-commitment entry point for organizations testing collaborative approaches

    Implementation Considerations for Cooperative AI Infrastructure

    Moving from concept to implementation requires careful planning, clear governance, and realistic expectations. Successful cooperative initiatives balance enthusiasm for collaboration with pragmatic attention to the technical, legal, and organizational complexities involved. The considerations below help you avoid common pitfalls while building sustainable collaborative infrastructure.

    Finding the Right Partners

    Not all partnerships work equally well. Successful cooperative AI initiatives typically involve organizations with compatible missions, similar technical needs, and aligned values around data ethics and community benefit. Geographic proximity often helps—though not required with modern tools—because it facilitates relationship-building and trust development essential for collaboration.

    Look for partners who share your commitment to transparency, equitable governance, and mission focus. Organizations with vastly different sizes, technical sophistication, or resources can struggle to find mutually beneficial arrangements, though thoughtful governance can address these differences. Consider starting with organizations you already collaborate with in other areas, as existing trust and communication channels reduce friction.

    Neutral third parties—existing nonprofit support organizations, technology assistance providers, or community foundations—can facilitate partnerships by providing governance infrastructure and managing relationships. These intermediaries reduce the burden on individual organizations while ensuring decisions serve collective interests rather than the priorities of the largest or most vocal participants.

    Governance Structures and Decision-Making

    Clear governance prevents conflicts and ensures cooperative infrastructure serves all participants equitably. Effective governance addresses questions like: How are technology decisions made? How are costs allocated? Who owns shared data and intellectual property? How do organizations join or leave the collaboration? What happens if the partnership dissolves?

    Research on nonprofit shared services emphasizes that having a neutral third party with no affiliation with any organization facilitates honest conversation about commitment, capacity, responsibilities, and concerns. Even when organizations trust each other, power dynamics can emerge around funding, technical expertise, or organizational size. Structured governance processes—voting procedures, representation requirements, transparency standards—help manage these dynamics proactively.

    Consider governance frameworks like:

    • Cooperative Models: All participants have voting rights, with decisions made democratically regardless of organizational size or financial contribution
    • Weighted Voting: Larger contributors or organizations have proportionally greater influence, balanced against provisions protecting smaller participants
    • Advisory Boards: Technical experts and community representatives advise on decisions even if they don't vote, bringing specialized knowledge and diverse perspectives
    • Consensus-Based Approaches: Major decisions require broad agreement, preventing any single organization from imposing its preferences

    Document governance arrangements formally in operating agreements, memoranda of understanding, or partnership contracts. These documents should address financial obligations, intellectual property ownership, data governance, dispute resolution, exit procedures, and modification processes. While legal documentation feels bureaucratic, it protects all participants and provides clarity when disagreements arise.

    Financial Sustainability and Cost Allocation

    Cooperative infrastructure requires sustainable funding models that fairly distribute costs while remaining affordable for participating organizations. Research shows that ensuring collaboration generates savings requires robust financial models that include realistic estimation of start-up costs as well as ongoing operating costs. Underfunding cooperative initiatives leads to service quality problems, organizational frustration, and partnership collapse.

    Common funding approaches include:

    • Subscription Fees: Organizations pay regular fees (monthly or annually) that fund infrastructure operations, typically scaled to organizational size or budget
    • Usage-Based Pricing: Costs allocated based on actual resource consumption—computing power, storage, or staff time utilized
    • Tiered Membership: Different service levels at different price points, allowing organizations to participate at levels matching their needs and budgets
    • Grant Funding: Foundation or government grants support initial development or subsidize participation by smaller organizations
    • In-Kind Contributions: Organizations contribute technical expertise, staff time, or resources rather than only financial support

    Be transparent about all costs, including hidden expenses like staff time for coordination, legal fees for contract development, or opportunity costs from standardizing on particular platforms. Organizations make better decisions when they understand total cost of ownership, not just direct technology expenses.

    Plan for various scenarios: What happens if an organization can't pay? How do you handle organizations that want to join mid-cycle? What if costs exceed projections? Building contingency funding and clear policies around financial hardship prevents situations where one organization's problems destabilize the entire collaboration.

    Technical Architecture and Integration

    Shared AI infrastructure must balance standardization with flexibility. Too much standardization restricts organizational autonomy and limits adoption of tools that don't fit predetermined templates. Too much flexibility defeats the purpose of shared infrastructure, creating integration nightmares and losing economies of scale. Finding the right balance requires understanding which components benefit from standardization and which require customization.

    Consider adopting modular architectures where core infrastructure components—computing resources, data storage, security frameworks—are standardized and shared, while application layers allow organization-specific customization. This approach, used successfully in healthcare shared services models, enables organizations to leverage common infrastructure while tailoring user experiences to their specific programs and populations.

    Integration with existing systems presents another critical consideration. Most nonprofits already use CRM platforms, accounting software, program management tools, and other systems that need to work with shared AI infrastructure. Prioritize solutions with robust APIs, standard data formats, and integration capabilities with common nonprofit technology platforms. The goal is enhancing existing workflows, not forcing organizations to abandon tools they depend on.

    Don't underestimate data interoperability challenges. Organizations structure data differently, use varying coding schemes, and maintain different data quality standards. Shared AI infrastructure requires common data models or translation layers that enable AI tools to work with diverse data sources. Invest time upfront in data governance, quality standards, and integration frameworks—these foundational elements determine whether shared infrastructure actually works in practice.

    Data Privacy, Security, and Ethics

    Shared AI infrastructure raises complex data governance questions. When multiple organizations share computing resources or AI platforms, how do you ensure one organization's data remains isolated from others? How do you prevent AI models trained on one organization's data from exposing that data to other participants? How do you maintain compliance with regulations when data moves across organizational boundaries?

    Address these concerns through technical controls (data encryption, access restrictions, isolated computing environments) and governance policies (data usage agreements, privacy standards, breach notification procedures). Organizations like OpenAI's People-First AI Fund emphasize models that "respect local culture and center worker needs"—values that should guide shared infrastructure development.

    Consider establishing shared ethical AI frameworks that all participating organizations commit to. These frameworks should address:

    • Algorithmic Transparency: Requirements that AI decision-making processes be explainable and auditable
    • Bias Detection: Processes for identifying and addressing discriminatory outcomes from AI systems
    • Human Oversight: Clear boundaries around which decisions require human judgment rather than automated processing
    • Community Benefit: Commitments that AI serves community needs rather than organizational convenience
    • Data Sovereignty: Respect for community ownership of data, particularly for vulnerable populations

    Shared governance can actually strengthen ethical AI practices. When multiple organizations collaborate, they can collectively invest in ethical review processes, bias auditing, and impact assessment that individual organizations couldn't afford. Collaborative governance also brings diverse perspectives that help identify potential harms that homogeneous groups might miss.

    Getting Started with Cooperative AI Infrastructure

    Building cooperative AI infrastructure is a journey, not a destination. Start with achievable first steps that build trust and demonstrate value, then expand as relationships deepen and capabilities grow. The pathway below provides a framework for moving from initial exploration to fully developed cooperative arrangements.

    Step 1: Assess Your Needs and Readiness

    Before pursuing cooperative arrangements, clarify what AI capabilities you need, what resources you can contribute, and what governance models align with your organizational values. Understanding your own position helps you identify compatible partners and realistic collaboration models.

    • Evaluate which AI use cases would most benefit your mission—consider starting with our guide for nonprofit leaders
    • Identify gaps between your needs and your current technical capabilities or budget
    • Determine what you could contribute to a collaborative arrangement (funding, technical expertise, governance leadership, pilot participation)
    • Assess your organization's capacity for collaboration—time, staff attention, decision-making processes

    Step 2: Connect with Existing Networks and Initiatives

    Don't reinvent the wheel. Multiple organizations already facilitate nonprofit technology collaboration. Connecting with existing initiatives provides immediate value while helping you understand what works and what doesn't in cooperative arrangements.

    • Join knowledge networks like NTEN's AI Resource Hub or NetHope's collaborative working groups
    • Explore group purchasing through TechSoup or similar nonprofit technology intermediaries
    • Participate in sector-specific networks (youth services, environmental organizations, community foundations) that may be developing AI initiatives
    • Research foundations and donors supporting cooperative nonprofit technology initiatives

    Step 3: Start with Low-Commitment Collaboration

    Build relationships and demonstrate value through initial projects with limited scope and commitment. Success with smaller initiatives builds trust and clarifies governance needs before tackling more ambitious arrangements.

    • Form a learning community where organizations share AI experiences and training resources
    • Coordinate group purchasing for a specific AI tool where multiple organizations have expressed interest
    • Pilot shared AI infrastructure for a limited use case, such as document summarization or email optimization
    • Jointly develop an AI pilot program that could benefit multiple organizations

    Step 4: Develop Governance and Formalize Arrangements

    As collaboration deepens, invest in governance structures that ensure equity, sustainability, and clear decision-making. Formal arrangements protect all participants and provide frameworks for resolving inevitable disagreements.

    • Document governance principles, decision-making processes, and conflict resolution procedures
    • Create financial models that distribute costs fairly while remaining sustainable
    • Establish data governance frameworks addressing privacy, security, and ethical AI use
    • Define participation requirements, benefits, and procedures for joining or leaving

    Step 5: Scale Sustainably and Continuously Improve

    Successful cooperative infrastructure evolves based on participant feedback and changing needs. Build mechanisms for continuous improvement, performance monitoring, and adaptation to new opportunities or challenges.

    • Regularly assess whether shared infrastructure is delivering promised benefits—consider approaches from our measuring AI success guide
    • Gather feedback from participating organizations about what's working and what needs improvement
    • Expand services gradually based on demonstrated demand and successful pilot programs
    • Document successes and challenges to help other organizations pursuing similar initiatives
    • Stay connected to broader sector developments through knowledge networks and collaborative platforms

    Building a Stronger Sector Through Cooperation

    The nonprofit sector has always understood that collaboration amplifies impact. Organizations routinely partner on programs, share facilities, coordinate services, and pool resources to serve their communities more effectively. Extending that collaborative spirit to AI infrastructure is both natural and necessary. Individual organizations struggling to adopt AI on their own can collectively build the platforms, expertise, and governance frameworks that enable everyone to benefit from these powerful technologies.

    Cooperative AI infrastructure doesn't require massive upfront investment or complex governance arrangements—though those may develop over time. Start with achievable steps: join a knowledge network, explore group purchasing, pilot a shared service with trusted partners. Each collaborative experience builds relationships, clarifies needs, and demonstrates what's possible when organizations work together rather than competing for scarce technical resources.

    The barriers to AI adoption—cost, expertise, infrastructure—are real, but they're not insurmountable when addressed collectively. Small organizations gain access to capabilities they couldn't afford independently. Mid-sized organizations reduce redundant investments in shared infrastructure. Larger organizations benefit from standardization, shared governance, and collective influence with vendors. Everyone gains from shared learning, reduced risk, and accelerated adoption of tools that advance mission-driven work.

    Society asks nonprofits to solve complex, urgent problems with limited resources. Cooperative AI infrastructure helps level the playing field, ensuring that organizations serving the most vulnerable communities aren't left behind as AI transforms how work gets done. By pooling resources, sharing expertise, and building common platforms, the nonprofit sector can harness AI's potential while maintaining the values—equity, transparency, community benefit—that define mission-driven work.

    The future of nonprofit AI isn't each organization going it alone. It's collaborative infrastructure that serves the entire sector, built on principles of shared value, equitable governance, and collective benefit. That future starts with the conversations you have with peer organizations, the networks you join, and the small collaborative steps you take today. Together, nonprofits can build the AI infrastructure the sector needs—infrastructure that serves community needs, respects organizational autonomy, and amplifies the sector's collective impact.

    Ready to Explore Cooperative AI Infrastructure?

    Whether you're looking to join existing collaborative initiatives or build new partnerships, we can help you navigate the technical, governance, and strategic considerations involved in sharing AI resources. Let's explore how cooperative models could strengthen your organization's AI capabilities while benefiting the broader nonprofit community.

    Discuss Cooperative AI StrategyExplore Our Services