AI’s next frontier is equity: Global leaders push for responsible scale at India AI Impact Summit 2026

As artificial intelligence reshapes economies and industries at breakneck speed, the more pressing question is no longer about capability but control—who builds it, who governs it, and who ultimately benefits from it.

At the India AI Impact Summit 2026, a high-level panel titled “Multistakeholder Partnerships for Thriving AI Ecosystems” brought together policymakers, corporate leaders, and applied AI practitioners to examine whether current AI trajectories risk widening global inequalities—or can instead accelerate sustainable development.

The panel featured Bärbel Kofler, Parliamentary State Secretary at Germany’s Federal Ministry for Economic Cooperation and Development (BMZ); Arundhati Bhattacharya, Chairperson and CEO of Salesforce South Asia; Nakul Jain, CEO of Wadhwani AI; and Sachin Lodha, Chief Scientist and Head of Research at Tata Consultancy Services.

 The AI divide is structural, not technical

Kofler framed the debate sharply. The challenge, she argued, is not an innovation gap but a power gap. Talent and ideas are globally distributed, yet venture capital, compute capacity, and data centre infrastructure remain concentrated in the Global North. Without deliberate governance frameworks, she warned, AI could deepen existing development divides rather than bridge them.

She pointed to stark imbalances in venture capital flows and data centre capacity between advanced economies and developing regions. For governments, the responsibility lies in creating enabling legal frameworks, investing in digital public infrastructure, strengthening skills ecosystems, and ensuring open access to datasets and AI building blocks.

Her ministry’s work under the Hamburg Declaration on Responsible AI for Sustainable Development was cited as an example of moving from principle to performance. Germany committed to train 160,000 people in AI-related skills within a year and has already exceeded that target. It has also expanded the number of open AI building blocks and public datasets made available as digital public goods, particularly in areas such as climate action.

The message was clear: declarations must be tied to measurable outcomes.

India’s DPI model as a blueprint

If Kofler provided the global policy lens, Bhattacharya offered a practitioner’s view shaped by India’s digital transformation journey.

Drawing from her tenure at the State Bank of India and her current leadership at Salesforce South Asia, she argued that technology only delivers impact when it is democratised. India’s financial inclusion story—enabled by Aadhaar-based identity verification and the rapid adoption of UPI—demonstrates how digital public infrastructure can alter economic trajectories at scale.

For AI, she suggested, adoption will not be the bottleneck. In populous markets like India, citizens readily adopt technology when it tangibly improves daily life. The real responsibility rests with policymakers and corporations to ensure ethical deployment, data privacy, and infrastructure readiness.

Bhattacharya emphasised that private sector players must institutionalise responsible use through internal governance. At Salesforce, this has taken the form of a longstanding Office of Humane and Ethical Use of Technology. She also underscored the role of skilling ecosystems, partnerships with industry bodies, and academic collaborations in preparing talent for AI-driven growth.

From pilot to scale: Lessons from the field

For Nakul Jain of Wadhwani AI, which focuses on AI solutions for underserved communities across the Global South, the most difficult part of AI deployment is rarely the model itself.

In education, healthcare, and agriculture deployments, he observed that success hinges on early institutional ownership and ecosystem alignment. AI tools integrated into government systems from the outset—rather than bolted on as standalone apps—have demonstrated higher adoption and sustained impact.

In healthcare use cases such as tuberculosis risk assessment, Jain highlighted the importance of building evaluation frameworks from day one, often in partnership with public research institutions. In sensitive domains, impact validation cannot be an afterthought.

He also identified two emerging needs for the global AI ecosystem: a cross-border repository of tested AI solutions and playbooks to enable replication in other developing countries, and regional AI assurance hubs that can evaluate models within local regulatory and social contexts before deployment.

Without such infrastructure, AI risks becoming trapped in pilot projects that never scale.

Data, compute, and talent: The missing triangle

Lodha approached the issue from the vantage point of enterprise-scale research and deployment. He described three structural constraints facing developing economies: fragmented and Western-skewed datasets, limited compute access, and shortages of specialised AI talent.

 Models trained predominantly on Western datasets often fail to translate effectively into local contexts, particularly in sectors like healthcare. Addressing this requires investment not just in data aggregation but in sensing infrastructure that generates high-quality, context-specific datasets.

On compute, Lodha acknowledged the disparity between the Global North and South but suggested that innovation in hardware utilisation—including repurposing legacy systems—can partially mitigate constraints. TCS, he noted, is also investing in responsible AI platforms that help evaluate and calibrate AI systems for trustworthiness.

The broader objective, he argued, is to embed responsibility and sustainability into AI architecture from the design stage rather than retrofit safeguards later.

The collective action imperative

Across perspectives, the panel converged on a central theme: no single stakeholder can build a thriving AI ecosystem alone. Governments must provide regulatory clarity and infrastructure; corporations must invest in ethical governance and skilling; applied AI organisations must ensure contextual deployment; and research institutions must validate and refine models.

At the India AI Impact Summit 2026, the tone was less about technological triumphalism and more about structural alignment. AI’s transformative promise—in health diagnostics, climate resilience, education access, and public service delivery—is widely acknowledged. But whether it becomes a force multiplier for inclusive growth or a catalyst for deeper inequities will depend on how these partnerships evolve.

If digital public infrastructure defined India’s first wave of digital transformation, the next chapter may well be about building AI ecosystems that are not only innovative—but equitable.