Bridging the AI Gap: How Nonprofits Can Ensure Equitable Technology Access
By 2026, AI is no longer emerging in the nonprofit sector. It is embedded. But while adoption is widespread, access is not equal. Larger nonprofits with budgets over $1 million are adopting AI at nearly twice the rate of smaller organizations, creating a growing divide that threatens to leave behind the very organizations serving the most marginalized communities. This article explores the structural barriers preventing equitable AI access and provides actionable strategies for nonprofits of all sizes to participate in the AI revolution without leaving their missions behind.
The promise of AI has always been democratization. Tools that allow everyone, regardless of resources or technical expertise, to harness powerful technology for good. But as we enter 2026, the reality looks starkly different. While the vast majority of nonprofits report using some form of AI, a closer look reveals a troubling pattern. Access to the most powerful, privacy-protecting, and effective AI tools is increasingly concentrated among well-funded organizations, while smaller nonprofits, grassroots groups, and those serving marginalized communities face steep barriers to meaningful adoption.
This divide is not merely inconvenient. It represents a fundamental threat to equity in the social sector. The organizations most in need of AI's efficiency gains, those operating with razor-thin margins and serving communities facing systemic barriers, are the least able to access these tools. Meanwhile, larger nonprofits leverage AI to expand their reach, improve donor engagement, and streamline operations, widening the gap in capacity and impact between well-resourced and under-resourced organizations.
The AI gap in nonprofits is multifaceted. It encompasses financial barriers, where ongoing subscription fees and system integration costs put advanced tools out of reach. It includes expertise gaps, where organizations lack the technical staff or training budgets to implement and manage AI systems effectively. It manifests in governance challenges, where overwhelmed teams lack the capacity to develop policies and safeguards. And it surfaces in infrastructure limitations, where unreliable internet access or outdated systems make cloud-based AI tools impractical or impossible.
But this article is not about despair. It is about action. Nonprofits, funders, technology providers, and policymakers all have roles to play in ensuring that AI serves as a force for equity rather than exacerbating existing divides. By understanding the structural barriers at play and implementing targeted strategies, we can create a nonprofit ecosystem where technology amplifies the work of all organizations, regardless of budget or location.
In this article, we will examine the current state of AI adoption across the nonprofit sector, identify the key barriers preventing equitable access, and explore practical, actionable solutions that organizations, funders, and technology providers can implement today. Whether you are a small grassroots organization seeking to level the playing field, a foundation looking to support digital equity, or a technology provider committed to responsible AI, you will find frameworks and strategies to help bridge the AI gap and ensure that no community is left behind in the AI revolution.
The Current State of the AI Divide
Understanding the scope and nature of the AI gap is essential for addressing it effectively. Recent research reveals troubling patterns in how AI adoption correlates with organizational size, geography, and the communities served. These patterns suggest that without intentional intervention, AI will widen rather than narrow existing inequities in the nonprofit sector.
Adoption by Budget Size
How organizational resources shape AI access
Larger nonprofits with budgets exceeding $1 million are adopting AI at nearly double the rate of smaller organizations. This disparity reflects the reality that advanced AI implementation requires upfront investment in software licenses, staff training, system integration, and ongoing support. Small organizations operating with limited budgets face difficult trade-offs between investing in AI and maintaining direct services to their communities.
The gap is particularly pronounced when considering the sophistication of AI use. While many small nonprofits use free or low-cost generative AI tools like ChatGPT for basic tasks, larger organizations leverage integrated AI systems embedded in their CRM platforms, automated donor intelligence tools, and custom AI solutions tailored to their specific workflows. This creates a two-tier system where well-funded organizations gain compounding efficiency advantages while smaller organizations remain stuck with manual processes and basic automation.
- Organizations with budgets over $1M adopt AI at nearly 2x the rate of smaller organizations
- Small nonprofits use primarily free generative AI tools for basic tasks
- Larger organizations invest in integrated AI systems, donor intelligence, and custom solutions
- Budget constraints force small organizations to choose between AI investment and direct services
Communities Served and Access Barriers
How the digital divide compounds existing inequities
Nonprofits serving marginalized communities face a cruel paradox. These are the organizations that could benefit most from AI's efficiency gains, yet they are also the least likely to have the resources, infrastructure, and expertise needed to adopt AI effectively. Many operate in areas with limited broadband access, serve populations with low digital literacy, and work with sensitive data that requires privacy-protecting solutions they cannot afford.
Organizations serving refugee communities, rural populations, unhoused individuals, and other vulnerable groups often operate on shoestring budgets with limited access to philanthropic capital. They rely on small grants, government contracts with tight restrictions, and individual donations that barely cover operational costs. When free AI tools reduce features or require payment for continued access, these organizations face impossible choices between losing critical capabilities and diverting funds from direct services.
Additionally, organizations led by people of color and those serving marginalized communities often face greater skepticism from funders about technology investments, making it harder to secure grants for AI implementation even when strong cases exist for efficiency gains and improved service delivery. This creates a cycle where the organizations least able to afford AI are also least able to access the funding needed to bridge the gap.
The Expertise and Training Gap
Understanding the skills barrier to effective AI adoption
More than half of nonprofit leaders report that staff lack the expertise to use or even learn about AI. This skills gap extends beyond basic technical literacy to include machine learning fundamentals, data science concepts, cybersecurity awareness, and the ability to evaluate AI vendors and integrate AI tools into existing workflows. Advanced AI implementation requires specialized knowledge that most nonprofits cannot afford to hire.
The challenge is not simply a lack of training. It is a structural issue rooted in nonprofit compensation, where technology roles are consistently underpaid compared to the private sector, making it difficult to attract and retain staff with AI expertise. Small organizations often cannot justify hiring dedicated technology staff at all, leaving AI implementation to overburdened program staff or executive directors who are already stretched thin.
- Over 50% of nonprofit leaders report staff lack expertise to use or learn about AI
- Skills needed include machine learning, data science, cybersecurity, and systems integration
- Nonprofit technology roles are underpaid compared to private sector, limiting talent recruitment
- Small organizations often lack dedicated technology staff entirely
These patterns reveal a clear reality. The AI divide in nonprofits is not a temporary challenge that will resolve as technology becomes more accessible. It is a structural issue rooted in the unequal distribution of financial resources, technical expertise, and philanthropic capital. Without intentional intervention from funders, technology providers, and policymakers, the gap will continue to widen, creating a nonprofit sector where mission impact increasingly correlates with technology access rather than program effectiveness or community need.
Understanding the Barriers to Equitable AI Access
To bridge the AI gap effectively, we must understand the specific barriers preventing equitable access. These obstacles are not isolated challenges but interconnected systems that compound one another. Addressing them requires multipronged strategies that tackle financial constraints, build capacity, strengthen infrastructure, and shift funding priorities.
Financial Barriers
The cost of AI implementation extends far beyond software subscription fees. Nonprofits face expenses for system integration, where AI tools must connect to existing CRM platforms and databases. They incur costs for data cleaning and preparation, a necessary step before AI can deliver meaningful insights. They pay for ongoing technical support and maintenance. And they must invest in staff training and change management to ensure successful adoption.
For small nonprofits, these costs can easily reach tens of thousands of dollars annually, a sum that represents a significant portion of their operating budgets. When funders restrict technology spending or view AI as a "nice to have" rather than essential infrastructure, organizations must make difficult choices. Many opt to forgo AI altogether rather than divert resources from program delivery.
The shift from free to paid AI services compounds this challenge. Many nonprofits initially adopted generative AI tools when they were freely available, only to face subscription requirements as platforms matured. This creates a pattern where organizations build workflows and dependencies on AI tools they can no longer afford, forcing them to either find budget for subscriptions or lose efficiency gains they have come to depend on.
Additionally, privacy-protecting AI solutions, which are essential for organizations working with vulnerable populations, typically cost more than standard cloud-based tools. This creates a perverse incentive where organizations serving the most sensitive populations face higher costs for responsible AI implementation, making it even harder for them to access appropriate technology.
Expertise and Capacity Gaps
Implementing AI successfully requires more than purchasing software. It demands understanding of how machine learning models work, how to evaluate AI vendors and avoid misleading marketing claims, how to prepare data for AI analysis, and how to interpret and act on AI-generated insights. Most nonprofit staff have not received training in these areas, and many organizations lack the resources to provide it.
The expertise gap manifests in several ways. Staff may struggle to write effective prompts for generative AI tools, limiting their utility. They may lack the technical knowledge to integrate AI systems with existing databases and workflows. They may not recognize when AI outputs are flawed or biased, leading to poor decision-making. And they may fail to identify security and privacy risks associated with specific AI implementations.
Training programs exist, but they often require time commitments that overwhelmed nonprofit staff cannot make. Free online courses may teach AI fundamentals, but they rarely address nonprofit-specific use cases or provide hands-on support for implementation. Paid consulting and training services are out of reach for budget-constrained organizations. This creates a cycle where those who most need training are least able to access it.
The capacity challenge extends to organizational governance. Developing AI policies, conducting data privacy assessments, and establishing ethical guidelines for AI use all require staff time and expertise. While a vast majority of nonprofits report using AI, only a small percentage have formal governance frameworks in place. This leaves organizations vulnerable to risks they may not even recognize.
Infrastructure Limitations
Cloud-based AI tools, which dominate the market, assume reliable high-speed internet access. But many nonprofits, particularly those serving rural communities or operating in areas with limited broadband infrastructure, cannot count on consistent connectivity. When AI systems require constant internet access to function, they become impractical for organizations operating in connectivity deserts.
Legacy technology systems compound the challenge. Many nonprofits rely on outdated databases, older versions of software that no longer receive updates, and hardware that cannot run modern applications. Integrating AI into these environments is difficult and expensive, often requiring complete system overhauls that small organizations cannot afford. The promise of AI efficiency rings hollow when the infrastructure needed to support it is out of reach.
Device access is another barrier. Some AI tools require smartphones or tablets with specific operating systems, excluding organizations and communities where basic feature phones are the norm. Even when cloud-based tools are accessible via web browsers, slow connection speeds and data costs can make them prohibitively expensive for organizations in low-resource settings.
For nonprofits working internationally or serving immigrant and refugee communities, language support is a critical infrastructure need. Most AI tools are optimized for English, with limited functionality in other languages. Organizations serving multilingual communities often find that AI tools do not work effectively in the languages their staff and beneficiaries speak, limiting their utility and creating new inequities.
Funding and Philanthropic Barriers
Perhaps the most significant barrier to equitable AI access is the way philanthropic funding flows. Most foundation grants restrict overhead spending, including technology investments, forcing nonprofits to rely on limited unrestricted funds for AI implementation. Even when organizations make strong cases for how AI could improve program delivery and reduce costs, funders often resist supporting technology infrastructure, viewing it as administrative rather than programmatic.
This funding environment creates a vicious cycle. Organizations without AI tools are less efficient, making them less competitive for grants and contracts. They cannot demonstrate the data-driven impact that funders increasingly demand. They struggle to scale their programs without the operational efficiency AI provides. But they also cannot access the resources needed to implement AI in the first place, leaving them perpetually behind.
Research suggests that organizations led by people of color and those serving marginalized communities face additional scrutiny when requesting technology funding. Funders may question whether these organizations have the capacity to implement AI successfully, creating a self-fulfilling prophecy where lack of resources to demonstrate capacity prevents access to the resources needed to build capacity.
The emphasis on "innovation" in philanthropy paradoxically disadvantages smaller organizations. Foundations often fund pilot programs and novel approaches but fail to support the basic infrastructure and capacity-building that would enable smaller nonprofits to innovate effectively. Without stable funding for core technology needs, organizations cannot experiment with AI or other emerging tools.
Strategies for Bridging the AI Gap
Addressing the AI divide requires coordinated action from multiple stakeholders. Nonprofits can adopt strategies to maximize limited resources and build internal capacity. Funders can shift policies to support equitable technology access. Technology providers can design solutions with accessibility and affordability in mind. And policymakers can create infrastructure and programs that level the playing field. Below are actionable strategies for each of these groups.
For Nonprofits: Maximizing Resources and Building Capacity
Practical steps organizations can take to access AI equitably
Even with limited resources, nonprofits can take concrete steps to access AI tools and build the capacity needed for effective implementation. These strategies focus on leveraging free and low-cost resources, building coalitions, and making strategic choices about where to invest limited funds.
- Start with free and freemium tools. Many powerful AI tools offer free tiers with meaningful functionality. ChatGPT, Claude, and other generative AI platforms provide free access that can support content creation, data analysis, and workflow automation. Focus on mastering these tools before investing in paid solutions.
- Prioritize nonprofit-specific discount programs. Many technology vendors offer significant discounts for registered 501(c)(3) organizations through programs like TechSoup, Microsoft Nonprofits, and Google for Nonprofits. Research available discounts before purchasing any AI tool at full price.
- Join or form nonprofit AI consortiums. Organizations can pool resources to negotiate better pricing, share training costs, and collaboratively develop AI policies and frameworks. Sector-specific coalitions can advocate for accessible AI tools tailored to their unique needs.
- Invest in staff AI literacy, not just tools. Training staff to use AI effectively delivers better returns than purchasing advanced tools without the expertise to leverage them. Free online courses, webinars, and peer learning networks can build capacity without major expense.
- Seek pro bono support from corporate partners. Many tech companies offer volunteer programs where employees provide consulting, implementation support, or training to nonprofits. Organizations like Catchafire and Taproot Foundation can connect nonprofits with skilled volunteers.
- Consider local and offline-first AI tools. For organizations with unreliable internet, local AI solutions that run on desktop computers without cloud connectivity can provide privacy and reliability. Open-source models like those available through Ollama offer powerful capabilities without subscription costs.
- Make the case to funders for technology infrastructure. Frame AI investments in terms of program impact rather than administrative efficiency. Show how AI enables better service delivery, stronger data for impact reporting, and the ability to serve more people with existing resources. For guidance, see our article on justifying AI investments during economic uncertainty.
For Funders: Supporting Equitable Technology Access
How foundations and donors can help bridge the AI gap
Philanthropic funders have tremendous power to shape equitable AI access across the nonprofit sector. By recognizing technology infrastructure as essential rather than overhead, providing flexible funding, and prioritizing capacity-building alongside innovation, funders can ensure that AI amplifies rather than exacerbates existing inequities.
- Provide unrestricted funding for technology infrastructure. Recognize that AI tools are not overhead but essential infrastructure for effective program delivery. Include specific allocations for technology in grant budgets or provide flexible funding that allows grantees to invest in AI as needed.
- Fund capacity-building alongside tools. Training, policy development, and change management are critical for successful AI implementation. Support grants that cover consulting fees, staff training, and the staff time needed to learn and integrate new tools.
- Create sector-specific AI learning cohorts. Fund cohort-based programs where organizations serving similar populations can learn together, share implementation experiences, and collectively solve challenges. This reduces individual costs while building stronger sector-wide capacity.
- Support nonprofit technology intermediaries. Organizations like NTEN, TechSoup, and sector-specific capacity builders provide critical infrastructure for equitable technology access. Funding these intermediaries strengthens the entire ecosystem.
- Prioritize equity in technology grant programs. Design grant criteria that level the playing field for smaller organizations. Provide application support, accept simpler proposals, and evaluate capacity-building potential alongside current technological sophistication.
- Fund research on AI equity and impact. Support studies that track AI adoption patterns, identify emerging disparities, and evaluate the effectiveness of different equity strategies. Data-driven insights can guide more effective grantmaking.
- Challenge grantees to demonstrate responsible AI use, not just adoption. Rather than encouraging AI for its own sake, ask grantees how they are ensuring AI tools serve their mission, protect beneficiary privacy, and advance equity. This shifts focus from technology trends to mission alignment.
For Technology Providers: Designing for Equity
How AI vendors can build accessibility into their products
Technology companies developing AI tools for nonprofits have both a business opportunity and a moral imperative to ensure their products serve organizations of all sizes and resource levels. Thoughtful pricing, accessible design, and genuine nonprofit partnerships can help bridge the AI gap while building sustainable businesses.
- Offer meaningful nonprofit discount programs. Go beyond token discounts and provide truly affordable pricing for small nonprofits. Consider tiered pricing based on organizational budget size rather than one-size-fits-all nonprofit rates.
- Maintain free tiers with real functionality. Resist the temptation to strip free versions of all useful features. Sustainable freemium models allow small organizations to accomplish meaningful work while creating natural upgrade paths as they grow.
- Design for low-bandwidth and offline environments. Build AI tools that function with intermittent internet access or offer offline-first alternatives. Consider how your product works for users without consistent high-speed connectivity.
- Prioritize multilingual support. Ensure AI tools work effectively in languages beyond English. Partner with organizations serving multilingual communities to test and refine language capabilities.
- Provide accessible training and implementation support. Offer free onboarding, documentation in plain language, and support communities where users can help each other. Consider what small organizations need to succeed, not just what large clients demand.
- Build privacy protections into default configurations. Small nonprofits working with vulnerable populations need privacy-protecting AI but cannot afford premium security features. Make responsible data handling the default, not an expensive add-on.
- Engage authentically with nonprofit users. Include small nonprofits in product development feedback loops. Hire staff with nonprofit sector experience. Test products with organizations facing real resource constraints, not just beta users at well-funded institutions.
For Policymakers: Creating Enabling Infrastructure
How government can support equitable nonprofit AI access
Government at federal, state, and local levels can play a critical role in ensuring equitable AI access for nonprofits. From broadband infrastructure to grant programs, public sector investments can create the foundation for widespread, responsible AI adoption across the social sector.
- Expand broadband access to underserved communities. Reliable internet is prerequisite for cloud-based AI tools. Continue investments in broadband infrastructure, particularly in rural areas and low-income urban neighborhoods where many nonprofits operate.
- Create grant programs for nonprofit AI capacity-building. Fund training, consulting, and implementation support for small nonprofits. Model programs after successful digital equity initiatives that combine infrastructure access with skills development.
- Support nonprofit technology intermediaries and consortiums. Provide funding for organizations that build sector-wide capacity, negotiate collective purchasing agreements, and develop shared resources for responsible AI adoption.
- Develop AI governance frameworks for nonprofits. Create accessible guidance, policy templates, and compliance frameworks tailored to nonprofit contexts. Help organizations navigate AI regulations without requiring expensive legal counsel.
- Allow technology investments in government contracts. Permit nonprofits receiving government funding to allocate portions of contracts toward technology infrastructure and AI tools that improve service delivery and reporting.
- Fund research on nonprofit AI adoption and impact. Support studies that identify adoption barriers, track equity outcomes, and evaluate which interventions most effectively bridge the AI gap across different nonprofit contexts.
Building AI Literacy and Capacity at Every Level
Access to tools alone does not create equity. Nonprofits must also build the internal capacity to use AI effectively, ethically, and strategically. This requires investments in staff training, policy development, and organizational change management. While these investments require resources, they are essential for ensuring that AI serves mission rather than creating new burdens.
AI literacy begins with understanding what AI can and cannot do. Staff need to recognize that AI is a tool for augmenting human judgment, not replacing it. They must learn to evaluate AI outputs critically, identify bias and errors, and understand when to rely on AI and when human expertise is essential. This foundational literacy applies across all roles, from frontline workers to executive leadership.
Beyond basic literacy, organizations need deeper expertise in specific AI applications relevant to their work. Fundraising staff should understand how AI-powered donor intelligence works and how to interpret predictive models. Program staff need training in how AI can support case management, beneficiary tracking, and outcome measurement. Finance teams must learn how AI can assist with budgeting, forecasting, and compliance. Each function requires tailored training that connects AI capabilities to real workflows.
Developing AI policies and governance frameworks is equally critical. Organizations must establish clear guidelines for what AI tools can be used for, what data can be processed with AI, and how to protect beneficiary privacy. They need processes for evaluating new AI tools, conducting privacy impact assessments, and monitoring for bias and unintended harms. This governance work requires time and expertise but is essential for responsible AI use. For organizations developing AI policies, resources like our guide to creating AI acceptable use policies can provide a starting point.
Free and low-cost resources can help build capacity. Online platforms offer AI fundamentals courses, nonprofit-specific webinars, and peer learning communities where organizations share implementation experiences. Nonprofit technology associations provide training, consulting, and policy templates. Academic partnerships can connect organizations with students and researchers who provide pro bono support. The key is dedicating staff time to learning and implementation, even when resources are constrained.
Organizations should also consider joining or forming AI learning cohorts with peer nonprofits. Cohort-based learning reduces individual costs while building stronger sector capacity. Organizations can share vendor research, co-develop policies, and troubleshoot implementation challenges together. These collaborative approaches are particularly valuable for small nonprofits that cannot afford dedicated technology staff or consulting support.
Ultimately, building AI capacity is about organizational culture as much as technical skills. Leaders must model curiosity and experimentation, create space for staff to learn and make mistakes, and prioritize ethical considerations alongside efficiency gains. When organizations approach AI as a tool for advancing mission rather than cutting costs, they create the foundation for equitable, effective, and responsible implementation.
The Path Forward: Collective Action for Equity
Bridging the AI gap in nonprofits is not a technical challenge. It is a moral imperative. AI has the potential to amplify the impact of organizations serving the most vulnerable communities, but only if we act intentionally to ensure equitable access. Without intervention, AI will widen existing disparities, concentrating power and resources among already well-funded organizations while leaving smaller, community-based nonprofits further behind.
The path forward requires coordinated action. Nonprofits must advocate for the resources and support they need while making strategic choices about how to leverage limited funds for maximum impact. Funders must recognize technology infrastructure as essential to mission delivery and provide flexible, adequate funding for AI capacity-building. Technology providers must design products with accessibility at their core, ensuring that cost is not a barrier to responsible AI use. And policymakers must invest in broadband infrastructure, digital literacy programs, and regulatory frameworks that protect vulnerable populations while enabling innovation.
This work is urgent. Every day the gap widens, organizations serving marginalized communities fall further behind their well-resourced peers. Donors increasingly expect data-driven impact reporting that requires AI tools to produce at scale. Funders demand operational efficiency that manual processes cannot achieve. And beneficiaries deserve services enhanced by technology that respects their privacy and advances equity.
But the work is also achievable. We have examples of successful AI equity initiatives, from nonprofit discount programs that make tools accessible to collaborative purchasing agreements that reduce costs. We have capacity-building models that combine training with implementation support. We have technology providers committed to serving the social sector equitably. And we have a growing recognition across the nonprofit ecosystem that digital equity is not optional, it is essential.
As we move forward, we must keep equity at the center of every AI conversation. We must ask not just whether AI can solve a problem, but whether it can solve the problem equitably, serving all communities regardless of organizational resources or infrastructure. We must evaluate AI tools not just on their features but on their accessibility to organizations of all sizes. And we must hold ourselves accountable for ensuring that the AI revolution in nonprofits lifts all organizations, particularly those serving the communities with the greatest need.
The AI gap is real, but it is not inevitable. With intentional action from nonprofits, funders, technology providers, and policymakers, we can build a nonprofit sector where technology amplifies mission impact equitably, where innovation serves justice, and where no organization is left behind because they cannot afford the tools needed to serve their communities effectively. The work begins now, and it begins with all of us.
Conclusion
The promise of AI in the nonprofit sector is profound, offering tools that can amplify impact, improve efficiency, and strengthen data-driven decision-making. But this promise will remain unfulfilled for many organizations unless we address the structural barriers preventing equitable access. The AI divide is not a temporary challenge that will resolve as technology matures. It is a growing chasm rooted in resource inequality, infrastructure limitations, and philanthropic funding patterns that disadvantage the organizations serving the most marginalized communities.
Bridging this gap requires more than goodwill. It demands intentional strategies from multiple stakeholders. Nonprofits must make strategic choices about technology investments, build internal capacity through training and policy development, and advocate for the resources they need. Funders must shift from viewing technology as overhead to recognizing it as essential infrastructure, providing flexible funding for AI implementation and capacity-building. Technology providers must design products with accessibility at their core, offering meaningful nonprofit pricing and building for low-resource environments. And policymakers must invest in broadband infrastructure, digital literacy programs, and regulatory frameworks that enable responsible AI adoption.
The organizations that could benefit most from AI's efficiency gains are often the least able to access these tools. Small nonprofits serving vulnerable populations operate on shoestring budgets, lack technical expertise, and struggle to justify technology investments to funders focused on direct services. This creates a cycle where those left behind by AI fall further behind their well-resourced peers, widening the capacity gap and ultimately limiting their ability to serve their communities effectively.
But the path forward is clear. By leveraging free and low-cost tools, building coalitions for shared learning and purchasing power, making the case for technology infrastructure funding, and prioritizing equity in product design and grantmaking, we can ensure that AI serves as a force for equity rather than exacerbating existing divides. The work is urgent, the stakes are high, and the time to act is now. Together, we can build a nonprofit sector where every organization, regardless of budget or location, has the tools needed to serve their communities with excellence.
Ready to Bridge the AI Gap in Your Organization?
Whether you are a small nonprofit seeking equitable AI access, a funder looking to support digital equity, or a technology provider committed to accessibility, we can help you develop strategies that ensure AI serves all communities equitably. Let's work together to build a nonprofit sector where technology amplifies mission impact for all organizations.