On February 19th, I found myself caught in an unusually long traffic jam in Delhi. Several roads had been closed due to VIP movement linked to the ongoing AI Summit in the city. As the minutes turned into hours, I couldn’t help but reflect on the irony of the situation. On one hand, the country was hosting global conversations on the future of Artificial Intelligence and technological transformation; on the other, millions of our citizens continue to struggle with basic access to livelihoods, services, and dignity.
Sitting there, immobilised in traffic, I began to wonder what this rapidly expanding world of AI really means for civil society organisations and, more importantly, for the poor, the ultra-poor, and the resource-less communities with whom these organisations engage.
For civil society organisations, AI undoubtedly presents certain immediate advantages. It can enhance efficiency in documentation, proposal development, data management, impact assessment, and communication. In a context where NGOs are increasingly expected to do more with fewer resources while meeting complex compliance and reporting requirements, such tools could reduce administrative burdens and potentially free up valuable time for field engagement. AI-driven analysis may also help organisations better understand programme trends, identify gaps in service delivery, and support more informed planning and decision-making processes.
Yet, when viewed from the standpoint of the poor themselves, the promise of AI appears far more distant and uncertain. The ultra-poor often lack not only access to digital devices and reliable connectivity but also the foundational literacy and confidence required to engage with technological platforms.
There is a real danger that AI may end up strengthening existing inequalities if its benefits remain confined to organisational and professional spaces rather than extending to community-level empowerment. Solutions generated without adequate contextual understanding risk becoming technocratic responses that fail to recognise the complex social, cultural, and economic realities that shape poverty and exclusion. Questions of language, affordability, trust, and usability remain critical barriers that cannot be ignored.
At the same time, it would be equally short-sighted to dismiss AI as irrelevant to grassroots development work. Its meaningful application will depend on how consciously it is adapted and mediated by institutions that work closely with communities.
Civil society organisations can potentially serve as bridges between advanced technological systems and marginalised populations—by integrating AI tools into participatory planning processes, improving access to entitlements, strengthening livelihood opportunities, and facilitating better engagement with local governance systems. Investments in digital literacy among frontline workers and community groups, alongside the development of locally contextualised and language-sensitive applications, will be essential in this regard.
Ultimately, the question is not whether AI is helpful or harmful, but whose interests it serves and how equitably it is deployed. If introduced without sensitivity to structural disparities, it may widen the gap between the digitally empowered and the digitally excluded. However, if guided by ethical considerations and grounded in grassroots realities, AI could become a supportive instrument in enhancing access, reducing vulnerability, and strengthening resilience among the most marginalised.
As I eventually inched forward through the traffic that day, it seemed evident that the future of AI in development will depend less on technological sophistication and more on the collective intent to ensure that its benefits reach those who need them the most.
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