As U.S. companies face heightened scrutiny over AI spending and outcomes, India’s operations are proving integral to delivering measurable ROI and sustaining global competitiveness, according to a new study.
The synergy between U.S. strategy and India execution is setting the pace for the next phase of enterprise AI maturity, finds the joint study by Zinnov, a global management and strategy consulting firm, and ProHance, an AI-led Workforce Management platform.
Titled “Navigating AI ROI: How India Operations of Global Enterprises Can Unlock Scalable Enterprise Value,” the study also reveals that U.S. enterprises’ India operations are emerging as key engines to scale and measure AI impact globally.
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But even as U.S. enterprises intensify their AI investments, the study finds that while 92% of India operations of global enterprises are piloting or scaling AI initiatives, 70% lack structured frameworks to measure ROI.
The study surveyed over 160 business and technology leaders to uncover how global organizations are translating AI ambition into measurable business outcomes.
“For U.S. enterprises, India has become an essential extension of their innovation ecosystem, not an alternative to it,” said Karthik Padmanabhan, Managing Partner, Zinnov. “The conversation has moved beyond adoption to accountability. The next competitive frontier for AI is proving business impact — faster innovation, higher accuracy, and differentiated customer experiences.”
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To help enterprises close the measurement gap, Zinnov and ProHance have developed a pragmatic “ROI from AI” framework built around five dimensions: maturity, baseline, adoption breadth and depth, total cost of ownership, and value delivered.The framework provides a structured approach to align AI programs with enterprise goals and quantify returns with precision.
“The study reinforces that AI ROI isn’t a regional conversation, it’s a global one,” says Saurabh Sharma, COO, ProHance. “India operations offer the scale and depth for enterprises to operationalize AI models developed in the U.S., helping transform pilot outcomes into enterprise-wide impact.”
The research identifies four critical enablers for sustainable AI scaling: unified data and infrastructure, domain-specific talent, robust governance, and measurable adoption depth. Together, these elements help enterprises move from AI experimentation to accountable growth.