Artificial intelligence is often celebrated in terms of models, GPUs, and valuation rounds. Those metrics matter, but they don’t answer the deeper question: is AI helping people live better lives? India’s recent investments—most visibly the IndiaAI Mission and expansion of national compute—signal a strategic ambition: “Make AI in India; Make AI Work for India. ” Now we must decide how to measure success. A simple, human-centred metric: evaluate AI by how it strengthens human well-being across Maslow’s hierarchy—physiological needs, safety, love & belonging, esteem, and self-actualization. This framework shifts the conversation from technical capability to social outcomes, and it fits India’s opportunity: a large talent pool, vast linguistic diversity, and high-need public services ready for intelligent augmentation. Physiological: saving lives and securing livelihoods At the base of Maslow’s pyramid lies survival. AI can improve primary healthcare, agricultural resilience, and social welfare delivery—areas that directly affect life and livelihoods.
Example: an AI triage and diagnostic assistant deployed in rural primary health centres can help community health workers (ASHAs) identify likely cases of tuberculosis, maternal complications, or severe dehydration faster, referring critical cases sooner. Similarly, crop advisory systems that combine weather, soil, and pest prediction deliver timely alerts to farmers, improving yields and income stability. Measure success by reductions in missed diagnoses, faster referral times, percentage uplift in smallholder yields, and fewer welfare payment leakages. Safety: protecting citizens and institutions Safety covers physical, legal, and digital security. AI offers powerful tools for disaster response, policing analytics, and cybersecurity—but with risks if deployed without safeguards. Example: AI-enabled flood forecasting and resource allocation systems can guide early evacuations and targeted relief. In policing, analytics that identify crime patterns can reduce response times when paired with transparency mechanisms, oversight, and community feedback. KPIs should include improved disaster response times, validated reductions in crime or fraud, and measurable improvements in incident containment—alongside audits ensuring privacy, bias mitigation, and redress options. Love & Belonging: preserving culture and strengthening communities AI can connect people while preserving identity. India’s linguistic diversity is both a challenge and an asset: public models that serve many Indian languages foster belonging and inclusion. Example: Local-language conversational agents for government services, helplines, and education platforms increase access for non-English users. AI tools that help communities document oral histories, folk songs, and indigenous knowledge preserve cultural belonging for future generations. Measure reach by languages supported, adoption in rural and underserved communities, and user satisfaction in local tests. Esteem: expanding opportunity and dignity At the esteem level, AI should enhance dignity through fair access to economic and social opportunity. That means tools for skills development, equitable hiring, and personalized learning—not systems that lock people out with opaque scoring. Example: AI-driven skilling platforms that recommend learning paths, certify micro-credentials, and connect learners to local employers can raise incomes and self- worth. When transparency and bias audits are built in, these systems can help rather than harm. Track improvements in employment outcomes from AI-assisted training, reduction in discriminatory outcomes in hiring pilots, and learner completion rates.
Self-actualization: enabling creativity and civic voice The highest level of Maslow’s pyramid is about realizing potential. AI should democratize creative tools, enable civic innovation, and open public datasets so citizens innovate rather than merely consume. Example: Open Indian-language generative models that support local journalism, creative arts, and civic tech projects empower creators and civic entrepreneurs. Public compute credits and datasets for universities, startups, and NGOs catalyse grassroots innovation. Measure the number of new creative projects, civic apps, and open-data-powered startups, plus qualitative impacts like increased civic participation. Policy implications: how India can measure and shape impact To make Maslow’s
framework operational, India needs outcome-focused policy and investment
• Outcome KPIs for public AI: mandate social-impact metrics (health outcomes, service wait times, language coverage) alongside technical benchmarks. • Public models and datasets: fund open, multilingual models and curated Indian datasets for public-good use cases. • Responsible deployment: require impact assessments, algorithmic audits, privacy-by-design, and accessible grievance mechanisms for public-sector AI. • Human-in-the-loop systems: prioritize designs that keep humans accountable, with explainability and appeal pathways. • Regional compute and capacity grants: ensure states, universities, and civil- society organisations can access compute for public-interest projects. Risks and trade-offs This approach is not naive. AI also magnifies risks: surveillance, bias, job disruption, and concentration of compute. A Maslow-centred strategy requires explicit mitigation—legal safeguards, independent audits, community oversight, and investment in reskilling. Conclusion: measure what matters India’s AI investments can position the country as a global technology power—but legacy will be measured by whether AI improved everyday life. If we assess success by how effectively AI helps people meet physiological needs, feel safe, belong, earn esteem, and pursue self-actualization, we reorient technology toward dignity and opportunity.