Nila Kartika Wati
In the summer of 2024, a delegation from the National Domestic Workers Alliance (NDWA) embarked on a high-stakes fact-finding mission to Silicon Valley. Comprising both executive leadership and elected worker council members, the group sought to demystify the rapidly evolving landscape of artificial intelligence (AI). Their objective was to determine whether these emerging technologies would serve as a tool for empowerment or as a mechanism to further entrench the power imbalances between domestic workers and their employers. By meeting with industry leaders at Google and Anthropic, alongside ethicists and policymakers, the NDWA began to formulate a critical question that now faces the entire social sector: Can AI be harnessed responsibly to advance worker power and organizational mission, or is it a force to be resisted?
As of mid-2026, the social sector stands at a crossroads. The initial hype surrounding generative AI has transitioned into a period of strategic implementation and, in some cases, cautious retreat. According to recent research conducted by The Bridgespan Group in partnership with the Nonprofit Technology Enterprise Network (NTEN), 70 percent of nonprofit leaders globally acknowledge that they are not yet taking advantage of meaningful opportunities provided by AI. Furthermore, only 8 percent of organizations report having a formal AI roadmap in place. This gap between potential and practice highlights a systemic challenge: while the technology moves at an exponential pace, the infrastructure, funding, and governance within the nonprofit world are struggling to keep up.
A Chronology of AI Integration in the Social Sector
The journey toward AI adoption in the nonprofit sector has been marked by three distinct phases. The first, beginning roughly in 2022 with the public release of advanced large language models (LLMs), was characterized by grassroots experimentation. Staff members began using tools like ChatGPT for drafting emails or summarizing reports, often without official organizational oversight.
The second phase, which peaked in late 2024 and throughout 2025, saw the emergence of "purpose-built" AI applications. Organizations like the International Rescue Committee (IRC) and Wadhwani AI began deploying machine learning models specifically designed for humanitarian aid and public health. This period also saw the rise of concerns regarding data privacy and algorithmic bias, leading to a demand for sector-specific ethical frameworks.
By 2026, the sector has entered a third phase: Strategic Choice. Organizations are no longer asking if they should use AI, but how they can use it in a way that aligns with their core values. The current landscape is defined by a shift from individual tool usage to systemic integration, where AI is viewed through the lens of organizational capacity, mission advancement, and societal advocacy.
The Three-Dimensional Framework for Action
To help leaders navigate this complexity, a strategic framework has emerged that categorizes AI engagement into three distinct dimensions: Augment, Advance, and Advocate. This framework allows organizations to move beyond broad debates and identify specific starting points based on their unique resources and missions.
Dimension 1: Augmenting Internal Capacity
The "Augment" dimension focuses on internal efficiency and operational productivity. For many nonprofits, particularly those with limited budgets, this is the most accessible entry point. AI tools in this category are used to automate administrative burdens, such as grant writing, scheduling, and data entry.
A prominent example is Community Rebuilders, a Michigan-based housing nonprofit with an annual budget of approximately $5 million. By deploying Microsoft 365 Copilot, the organization’s 60-person team has automated routine administrative tasks, saving more than 15 hours per week during peak campaign seasons. This time is redirected toward high-touch activities, such as donor cultivation and program strategy.
Similarly, the Akshaya Patra Foundation in India, which serves 2.35 million school children daily, uses AI-powered automation to process attendance and meal distribution forms. This system has recovered over 4,400 days of staff time annually, ensuring that resources are focused on the foundation’s primary mission of hunger relief rather than bureaucratic processing.
Dimension 2: Advancing Mission and Impact
While augmentation focuses on efficiency, the "Advance" dimension focuses on outcomes. This involves using AI to improve the quality of services, expand reach, or create entirely new program models. This work often requires deeper investment in data infrastructure and custom technology.
The International Rescue Committee (IRC) has become a leader in this dimension. With an annual budget of $1.3 billion, the IRC embeds AI across its services to support 118 million displaced people globally. Their "Signpost" platform uses AI to answer urgent legal and aid questions in multiple languages via WhatsApp and Facebook. Crucially, the IRC maintains a policy that AI supports caseworkers rather than replacing human judgment, ensuring that high-stakes decisions remain in human hands.
In the legal sector, Mobile Pathways has developed the "Pathfinder" platform, an agentic AI system that allows legal aid workers to synthesize complex government records into case snapshots instantly. Over 80 percent of users reported that the tool improved their ability to explain case status to immigrant clients, demonstrating how AI can bridge the information gap in complex legal systems.
Dimension 3: Advocating for Equitable Governance
The final dimension, "Advocate," recognizes that nonprofits have a unique role in shaping the policies and norms that govern AI in society. This is particularly vital for organizations serving communities that are historically marginalized and most at risk of being harmed by algorithmic bias.
The ACLU of Massachusetts and the Greenlining Institute are key players in this space. The ACLU uses litigation and advocacy to oppose AI-powered surveillance and policing systems that disproportionately target communities of color. Meanwhile, the Greenlining Institute champions "algorithmic greenlining," pushing for state laws like California’s AB 1018, which would require bias assessments before AI deployment in sectors like housing and credit.
In the Global South, Kenya’s Lawyers Hub works to ensure that AI governance frameworks are not merely copies of Western models but reflect the cultural and economic realities of the African continent. This advocacy ensures that the digital public infrastructure of the future is built with equity at its core.
Supporting Data: The Reality of the Funding Gap
Despite the clear potential of AI, significant barriers remain. The Bridgespan/NTEN survey reveals that 34 percent of organizations cite a lack of staff capacity as a primary hurdle, while 29 percent point to a lack of clear governance policies. Perhaps most strikingly, 69 percent of respondents report receiving no AI-specific funding of any kind.
Historically, the social sector has underinvested in technology infrastructure. In the age of AI, this "tech debt" is becoming a critical liability. Without flexible funding from institutional philanthropy, the digital divide between well-resourced organizations and smaller, community-based nonprofits is expected to widen.
Experts like Afua Bruce, CEO of ANB Advisory Group, emphasize that AI is not a "plug-and-play" solution. "Mission-driven organizations must determine the responsible path forward for themselves," Bruce notes. "To continue doing quality work in a world where AI introduces both risks and opportunities, leaders must be proactive in their strategic choices."
Official Responses and Sector Reactions
The reaction from sector leaders has been a mix of cautious optimism and urgent concern. Alistair Stephenson of the NDWA highlights the interconnectedness of the issues: "It was difficult to talk about using AI to assist with grant writing without the conversation immediately becoming about environmental impacts or data centers." This sentiment reflects a growing awareness that AI adoption is not just a technical choice, but an ethical and environmental one.
Funders are also beginning to shift their perspectives. Lul Tesfai of the James Irvine Foundation argues that the starting point should always be an organization’s goals, not the technology itself. "The right solution may involve AI, another organizational change, or some combination of the two," Tesfai says. This suggests a move toward "tech-agnostic" funding that prioritizes impact over the adoption of specific tools.
Broader Impact and Future Implications
Looking ahead toward 2028, AI is expected to become a central political and social issue. For nonprofits, the risk of inaction is significant. If leadership fails to set a clear direction, staff may experiment with tools without necessary guardrails, leading to data breaches or the unintended amplification of bias.
Furthermore, the role of nonprofit boards is evolving. Alethea Hannemann, CEO of Board.Dev, points out that while staff can initiate AI projects, only boards can scale them. Boards must now take responsibility for setting risk appetites, authorizing funding for capacity building, and ensuring that AI strategies are grounded in the needs of the communities they serve.
In conclusion, the evolution of AI in the social sector is not predetermined. It is being shaped by the choices made today by executive directors, board members, and frontline workers. By focusing on augmenting capacity, advancing mission, and advocating for justice, the nonprofit sector can ensure that AI serves as a tool for the public good rather than a driver of further inequality. The immediate opportunity for leaders is to move beyond the hype and begin the disciplined work of making informed, values-driven choices.
