AI Adoption in India’s Social Sector: Ecosystem Roles, Risks, and the Path Forward 

India’s social sector is at an inflexion point. While Artificial Intelligence (AI) is reshaping sectors such as healthcare, education, agriculture, and finance, its adoption within nonprofit programmes remains limited, uneven, and largely exploratory. At Pacta, we spent over a year examining this gap through field research, approximately 30 expert interviews, and a multi-stakeholder convening held in Bengaluru in May 2025. This article distils key insights from that work, showing that AI adoption in India’s social sector is less a technical challenge and more an ecosystem question, shaped by funding, power, capacity, and governance.

Our central finding is that AI adoption in India’s social sector remains at an early stage. Most organisations are piloting relatively simple tools while facing constraints related to funding, technical capacity, data governance, and ethical risk management. Larger and digitally mature organisations are better positioned to experiment and scale, while smaller and medium-sized non-profits require sustained ecosystem support. In this context, philanthropy plays a decisive role in determining who gets to experiment, who scales, and who is left behind.

The current landscape: Promise with pragmatism 

India ranks first globally in AI skill penetration and talent concentration, and fifth in AI scientific publications. The World Economic Forum’s ‘AI for Impact’ report notes that India ranks second globally, after the United States, in AI deployment by social innovators. Yet this technological leadership has not translated into widespread adoption across the social sector. This gap underscores the difference between national AI capacity and the realities faced by non-profits working at the frontline.

Among the non-profits studied, AI use is largely confined to chatbots, workflow automation, and basic decision-support systems. Common applications include content personalisation, automated feedback mechanisms, voice-enabled bots, and chat-based information delivery. Of the organisations interviewed, 10 out of 14 had moved from pilot to live deployment, with larger organisations progressing more consistently due to greater access to financial, technical, and human resources. 

Field examples illustrate both promise and constraint. Digital Green’s AI-enabled voice bots for agricultural advice required redesign and WhatsApp integration to address device and storage limitations among users. Pratham’s PadhAI initiative employs a privacy-first speech recognition engine trained on Hindi- and Marathi-speaking children but remains at an early stage of public uptake. Khan Academy’s KhanMigodemonstrates how generative AI can support teachers, reflecting the advantages of organisational scale and technical maturity. 

Across cases, a clear pattern emerges: AI tools are most effective when they are contextually grounded and supported by resources for iteration and learning. 

Development models, funding patterns, and the role of philanthropy 

Most AI development observed in the sample was led by internal technical teams within non-profits. Larger organisations were more likely to develop in-house, while smaller organisations relied on external partners or vendors. 

Success in securing funding for AI deployment depended on several factors: 

• A scalable proof of concept, 
• Proximate technical capacity, 
• Clear value for end users, 
• Robust data governance practices, and 
• A defined deployment roadmap. 

While reasonable, these criteria often privilege digitally mature organisations and unintentionally exclude smaller actors. External funding accounted for 50% of AI investments, followed by self-funding at 43%, with hybrid models remaining limited. This suggests that many organisations continue to absorb the financial risk of experimentation themselves, particularly for foundational work such as data collection, testing, and iteration. 

Here, philanthropy plays a critical role, not only as a funder of pilots but as a risk buffer for experimentation and learning. By funding early-stage exploration, supporting foundational data work, and investing in organisational capacity rather than only outcomes, philanthropic actors can broaden participation in AI adoption across the social sector.

Stakeholder dynamics and ecosystem relationships 

AI adoption in India’s social sector is shaped by interlinked stakeholder groups, whose interactions influence whether adoption is extractive, exclusionary, or socially grounded: 

• Big tech companies provide funding, mentorship, and access to technical resources such as cloud credits, tools, and volunteer developers. However, these relationships can be asymmetrical, with non-profits often treated as sources of data to train, validate, or showcase proprietary models, raising concerns about extractive dynamics and power imbalances. 
• Tech enablers and intermediaries have emerged as mission-critical actors in catalysing AI adoption. Organisations such as People+AI and Ooloi Labs support non-profits in translating social challenges into deployable AI systems, while others like Apurva AI, Project Tech4Dev, and Open NyAI provide technical capacity through models such as fractional leadership, community volunteers, or subsidised developers. 
• Philanthropic organisations act as ecosystem catalysts. Beyond funding individual projects, they shape norms around responsible AI by supporting intermediaries, convening diverse stakeholders, and investing in shared infrastructure such as open-source tools and data commons. Long-term philanthropic support is essential for sustaining inclusive AI systems that prioritise equity and governance over short-term efficiency gains. 
• Government can serve as a potential scale partner, particularly when pilots demonstrate technical robustness, legal compliance, and alignment with population-scale digital infrastructure. However, while AI initiatives are often driven at senior policy levels, adoption challenges frequently arise at the frontline and community levels. 
• Academic institutions contribute through independent research on bias and governance gaps, pedagogical leadership in public-interest technology, and the creation of open-access knowledge to inform policy and practice. 

Critical risks and vulnerabilities 

Despite growing interest, non-profits face persistent technical, financial, and governance challenges. Limited familiarity with AI use cases, the scarcity of India-based models and high-quality datasets, and the absence of structured AI risk management frameworks constrain adoption largely to basic applications. These gaps raise concerns around bias, ethical use, and data protection.

Human resource constraints further restrict progress. Organisations such as MSSRF point to the high cost and scarcity of AI talent, while Wadhwani AI highlights the difficulty of securing funding for foundational work, such as large-scale data collection, work that remains critical yet under-supported. 

Without deliberate ecosystem intervention, these constraints risk deepening a digital divide within the social sector itself. 

The path forward: Grounded and collaborative approaches 

Three principles cut across more effective initiatives: 

1. First, technology must remain a means rather than an end, supporting human judgment instead of replacing it. 
2. Second, community-centred design ensures relevance, trust, and uptake. 
3. Third, continuous iteration and feedback are essential for refining systems and responding to real-world conditions. 

For philanthropy and ecosystem actors, this points to a broader responsibility to strengthen the institutional and ecosystem frameworks that guide AI adoption. This includes funding intermediaries, supporting shared learning spaces, investing in governance and data infrastructure, and enabling collaboration across sectors. 

AI presents real opportunities for India’s social sector, but its impact will depend on how it is adopted and by whom. It remains too early to either celebrate or dismiss AI. What matters is staying grounded, investing in the right intermediaries, and working collaboratively to ensure that AI serves equity rather than deepening existing divides.