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Speed without stability: The AI trade-off facing social purpose organisations
Speed without stability: The AI trade-off facing social purpose organisations Premium
In the week before Christmas, we were in Bengaluru wrapping up a workshop with Social Purpose Organisations (SPOs). The sessions focused on a familiar challenge: how organisations working closest to communities can better access and use data. One part of the workshop, however, generated unusual excitement. One of the sessions demonstrated how AI tools, specifically Google's NotebookLM, could take reports and research papers and instantly turn them into presentations, infographics, videos, and summaries.
For organisations chronically short of time, money, and skilled staff, this felt like a breakthrough. What once required designers, editors, and consultants could now be done in minutes. AI appeared to offer the social sector what it has long been promised but rarely received: efficiency without compromise.
That promise, however, deserves closer scrutiny.
On the drive back from the workshop, our conversation shifted from excitement to unease. If AI tools are so powerful, what exactly are SPOs trading away for this speed? And more importantly, can a sector built on trust afford to get this wrong?
The first and most obvious risk is factual reliability. AI tools are improving, but they still hallucinate. They fabricate citations, misinterpret evidence, and produce confident-sounding inaccuracies. In many industries, such errors are inconvenient. In the social sector, they are dangerous.
SPOs operate in a high-trust environment. Their credibility determines whether policymakers listen, whether donors fund programmes, and whether communities believe what they are being told. A single error in an AI-generated report or infographic, especially one that circulates widely, can undo years of careful, evidence-based work. Unlike a marketing misstep, a factual mistake in the social sector can directly affect public policy, programme design, and people's lives.
Language makes this risk even sharper. Much of India's social sector work unfolds in multilingual settings, where translation is not merely technical but political and cultural. Words carry histories, power, and intent. AI, however, treats language as a mathematical problem, not a social one.
Our own experiments exposed how easily meaning can be distorted. An infographic generated in English accurately reflected the findings of a report. But when translated into Hindi using an AI tool, the word “leadership” became shāsan, meaning “to rule”, instead of netṛtva, which captures the intended idea of leadership. This was not a cosmetic error. It fundamentally altered the message, subtly shifting it from empowerment to authority.
Even more troubling was what happened to the design. The Hindi version did not simply translate the text; it redesigned the entire infographic. Layouts changed, emphasis shifted, and visual hierarchy was altered without explanation. For organisations that depend on consistent messaging, this loss of control is not a minor inconvenience, it is a serious risk.
This points to a deeper problem: reproducibility. SPOs need to know that what they create today can be reliably reproduced tomorrow, across languages and formats. AI tools, as they currently stand, offer speed but not stability. They generate outputs, not guarantees.
And here lies the cruel irony. The only way to manage these risks is through human intervention. Content must be vetted by subject-matter experts. Translations must be reviewed by linguists. Designs must be checked and corrected. All of this costs time and money, resources that SPOs were hoping AI would help them save.
In other words, AI does not eliminate professional labour in the social sector; it merely shifts it. The danger is that organisations, seduced by efficiency, may skip these safeguards. When that happens, AI becomes not a tool for empowerment, but a liability.
This leads to an uncomfortable but necessary conclusion: AI is not a silver bullet for the social sector. Human intelligence still matters more than artificial intelligence. Critical thinking, contextual judgment, and ethical responsibility cannot be automated.
Yet much of the current discourse treats AI adoption as inevitable and unproblematic. Social sector leaders are encouraged to “embrace AI” without adequate discussion of what that actually entails. The result is a growing gap between technological capability and institutional readiness.
What is needed is not faster adoption, but more responsible adoption.
First, SPOs must recognise that AI-generated content is never final. It is a draft, nothing more. Organisations should build explicit review protocols into their workflows, especially for public-facing materials and translations.
Second, capacity-building efforts in the social sector need a fundamental rethink. Digital literacy is no longer enough. What SPOs require is AI literacy: the ability to question outputs, identify risks, and understand where automated systems fail.
Finally, the sector must confront a governance question it has so far avoided. Should there be shared ethical standards for AI use in the social sector? Or will each organisation be left to figure this out on its own, at its own risk? Without common principles around transparency, validation, and accountability, AI adoption will remain uneven, and potentially harmful.
The social sector has always claimed moral authority in how it works and why it exists. That authority cannot be delegated to algorithms. If AI is to play a role, it must strengthen, not weaken, the sector's commitment to accuracy, accountability, and trust.
AI will not save the social sector. But used carelessly, it could quietly undermine the very foundations on which the sector stands.
(Satender Rana, Senior Manager–Research at ISDM, and Siddharth Dhote , Senior Associate–Data Analyst, ISDM.)
(Sign up for THEdge, The Hindu's weekly education newsletter.)
Published - January 28, 2026 07:21 pm IST
education
Source: TheHindu
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