Indian companies are now focusing their AI investments more carefully, dropping pilots that do not produce clear benefits for revenue, cost savings, or productivity. This change signals a move away from general testing toward focused applications with defined business results, according to Ganesh Gopalan, co-founder and CEO of Gnani.ai, a voice AI company. The experimentation era, Gopalan noted, was marked by broad mandates and tolerance for ambiguity, but corporate boards now demand proof that AI systems directly enhance financial performance—whether by lowering delinquency rates, cutting customer service expenses, or speeding up first-contact resolutions. Without measurable impact, these projects are being discontinued.
Gopalan stated in an interview that the period of broad experimentation is ending. Corporate boards now require proof that AI systems directly enhance financial performance—whether by lowering delinquency rates, cutting customer service expenses, or speeding up first-contact resolutions. The shift is forcing companies to abandon vague AI initiatives in favor of targeted solutions. “Enterprises are now asking which deployments generated measurable revenue or cost outcomes, and the ones that cannot answer that question are getting cut,” he said.
The shift is forcing companies to move away from broad mandates to “implement AI” and instead identify specific business problems such as reducing delinquency rates, lowering the cost per customer interaction, or improving first-call resolution. Enterprises achieving scale are also integrating AI with existing customer relationship management, collections, and compliance systems instead of treating it as a standalone layer.
“AI deployed against a vague mandate stays in pilot. AI deployed against a measurable outcome, through a system that fits how the enterprise actually operates, becomes infrastructure,” Gopalan said.
How AI pilots fail without measurable outcomes
Founded in 2016 by Ganesh Gopalan and Ananth Nagaraj, Gnani.ai develops speech and language models for enterprise voice interactions, managing over 30 million daily calls across more than 200 organizations. The company was also chosen for India’s IndiaAI Mission.
High-volume tasks with clear structures—such as collections, insurance follow-ups, and service bookings, are where AI delivers the fastest returns. Customer intent in these areas is easier to predict, and decision-making follows defined rules, making automation practical. Gnani.ai’s deployments across banking, financial services and insurance, telecom, consumer durables, and hospitality have improved contact rates, resolution times, and interaction costs, though exact performance figures were not shared. The company’s success stems from narrow agentic AI use cases, where AI agents handle specific, high-volume interactions while humans manage exceptions. These applications work because customer intent is relatively easy to classify, and the range of decisions available to an AI agent is restricted.
However, AI’s effectiveness in complex workflows, where instructions are unclear or decisions carry high risk, remains limited. Companies achieving the most success treat AI as “supervised autonomy,” handling repetitive tasks while humans manage exceptions. Contact centers will not vanish but will shift toward hybrid models, where AI handles routine inquiries and employees address complex issues.
Gopalan expects contact centers to become increasingly automated but not entirely devoid of people. “The contact centre is not becoming humanless; it is becoming better designed,” he said. “The ones treating headcount reduction as the primary metric will miss the bigger opportunity.” AI can manage repetitive interactions such as policy renewals, payment collections, appointment reminders, and initial service queries, while human agents remain necessary for disputed claims, distressed customers, and complex escalations.
India’s voice AI edge in global markets
The company’s models support over 40 languages and dialects, including code-switching and poor-quality phone audio. This expertise could establish Indian AI developers as global leaders, particularly in linguistically diverse regions like Latin America or Southeast Asia. Gopalan emphasized that voice AI could widen digital access, especially for users constrained by literacy, device limitations, or low-bandwidth connectivity. Expanding voice AI across India will require models to handle language switching within conversations, regional dialects, noisy telephone audio, and low-latency responses at scale.
India’s linguistic complexity could also give domestic AI developers an advantage in overseas markets. Technology built to handle Hindi-English code-switching and low-resource Indian languages could be adapted for Spanish-English conversations in the US and Latin America, as well as linguistically diverse markets in Eastern Europe, Southeast Asia, and the Middle East. “Companies that solve for India are not solving a local problem. They are building capability the rest of the world needs,” he said.
While access to computing infrastructure has improved, partly due to capacity being added under the IndiaAI Mission, it remains a constraint for startups training models for low-resource languages. The larger challenge, however, is finding engineers who can convert AI research into reliable production systems capable of managing millions of interactions.
Trust and compliance in AI-driven decisions
For industries like banking and insurance, reliability goes beyond accuracy. Systems must be able to identify uncertainty and prevent an erroneous response from becoming an unauthorized action. For banks, insurers, and other regulated businesses, this requires compliance logs, audit trails, real-time intervention, and explicit limits on the decisions an AI system can make. “Trust is ultimately an architectural property, not a model property,” Gopalan said.
In March, Gnani.ai secured $10 million in a Series B funding round led by Aavishkaar Capital, with participation from Info Edge Ventures. The company plans to expand its API platform and enter new markets, including the US, Southeast Asia, and the Middle East, within two years. The recent funding will also deepen Gnani.ai’s presence in sectors where it already has deployment experience, such as banking, telecom, and hospitality. The key test for AI startups, Gopalan noted, is whether their products can operate reliably at enterprise scale, and whether they contribute to profitability. “India was the proving ground,” he said. “The next 18 to 24 months are about taking what we built here to the world.”
