Nasscom and Krisp have released the India Voice AI Readiness 2026 report, a survey of more than 140 organisations on how they are deploying voice AI in customer operations. The headline numbers are upbeat: 61% report a cost reduction of more than 20% per interaction, and two-thirds expect investment to rise by up to 50%. The report is also co-authored by a vendor selling the technology. And its central claim, that India’s multilingual complexity is an advantage, collides with hard benchmark data.
What the report claims
The survey covers pure-play and integrated BPM providers, GCCs with contact centres, and organisations evaluating or deploying voice AI. It finds that 31% have reached limited production, while 13% have scaled across multiple customer journeys. A further 41% use a hybrid model combining internal teams with external providers, and 29% co-develop with partners. On impact, 61% report more than a 20% reduction in cost per eligible interaction, and 68% report ROI within 6–24 months. BFSI, telecom, e-commerce, retail, travel and logistics lead adoption.
The survey is co-authored by a vendor
Nasscom is an industry body. Krisp is a vendor. The report is published “in partnership with Krisp,” and Krisp’s product set, from accent conversion to speech-to-speech translation, is exactly what the report concludes organisations should buy.
That does not make the data wrong. It does mean the framing serves the partner. Krisp’s chief executive supplies one of the two quotes in the release, and the company’s own figures sit alongside the survey findings.
The multilingual voice AI claim meets the benchmark reality
The report’s thesis is that India can turn its multilingual complexity into a global advantage. The engineering reality is harder.
Independent benchmarks show Indian-language speech recognition still runs high error rates. Voice of India, built by AI4Bharat at IIT Madras with Josh Talks, tested 536 hours of real telephonic speech across 15 Indian languages. Most systems exceeded a 20% word error rate, a common usability threshold. None cleared it across all languages. Some failed outright: GPT-4o Mini Transcribe reached a 295.9% error rate on Gujarati.
Even the best models struggle on the harder languages. The report itself concedes that accents, code-switching and real-world operating conditions remain key challenges. That is the most honest sentence in the document.
The language count keeps moving
The release says Krisp’s speech-to-speech translation covers 65 languages. Krisp’s own materials say 60-plus, then 61, and its chief executive has said “62 to be precise.” The number is close, but it is not fixed at 65.
Krisp also leans on partners for Indian-language coverage. Its chief executive has said the company works with Indian firms to incorporate their technology into its platform. The Indian-language capability is partly assembled, not wholly built.
The efficiency claims and the real cost gap
The 61% figure is self-reported by survey respondents. It is not audited. Independent cost data gives a sense of the true gap: voice AI handling routine contacts runs at roughly ₹5–8 per call, against ₹45 for a human agent. The typical mid-market split is about 76% AI and 24% human.
The economics are real. They are also workload-specific. Complex disputes, late-bucket collections and high-value sales stay human-led, and any vendor promising full replacement is overselling.
Where the opportunity actually sits
The strongest independent evidence for productivity gains does not come from the report. An NBER study by Brynjolfsson, Li and Raymond found a generative-AI assistant raised customer-support productivity by 14% on average. For novice agents, the lift was 34%.
That distinction matters in India. The BPM segment brought in roughly $54.6 billion in FY2025, and voice-led support carries attrition of about 30–35%. Tools that flatten the ramp for new hires are worth more there than the headline average.
What to watch
Whether Indian-language error rates fall fast enough to make production-grade multilingual voice AI routine. Whether accent conversion and translation become standard buying criteria or stay niche. And whether the global-delivery-hub framing survives contact with clients who measure accuracy, not ambition.
Editor’s Note
This analysis draws on the Nasscom–Krisp India Voice AI Readiness 2026 report and its release, Krisp’s product and developer documentation, the Voice of India benchmark from AI4Bharat at IIT Madras with Josh Talks, Gnani AI’s code-switching research, SraVaani-1.0, the NBER study on generative AI and customer support, Nasscom’s Strategic Review figures on India’s BPM segment, and independent cost data on voice AI deployment. Where a figure is a company’s own or an estimate, we say so.