Himani Agrawal, COO of Microsoft India and South Asia, emphasized at the WTI event in Noida that leadership-driven AI adoption ensures stronger outcomes, as initiatives led by executives deliver serious ROI and meaningful business returns
The adoption of artificial intelligence (AI) in enterprises has entered a new phase, shifting from grassroots experimentation to structured, leadership-driven strategies. According to Microsoft India’s Work Trend Index (WTI) 2025, senior executives and middle managers are now the primary drivers of AI integration, replacing the earlier employee-led momentum.
In India, more than 80% of business leaders said they are already familiar with AI agents, compared to just 66% of employees. Globally, 79% of leaders believe AI will accelerate their careers, outpacing the 67% of employees who share this confidence. This shift, experts suggest, is accelerating generative AI pilots into production.
Himani Agrawal, Chief Operating Officer of Microsoft India and South Asia, said leadership-driven adoption creates stronger business outcomes. “When it starts with leaders, it is serious adoption. It is serious ROI and things which mean business returns,” she noted during the recent WTI event in Noida.
From skepticism to confidence
Industry leaders agree that leadership engagement has transformed the AI conversation. Manpreet Singh Ahuja, Chief Digital Officer at PwC India, said the initial adoption cycle was clouded by skepticism. “The consultant was skeptical, the customer was skeptical, and ROI was expected almost immediately,” he explained.
Now, with leaders experimenting personally and integrating AI into decision-making, confidence in the technology has grown. A survey by hiring platform Indeed reinforces this trend, showing that nearly half of middle managers are actively upskilling in AI, compared to just 41% of younger employees.
Rajesh Kumar R, Executive Vice President and CIO at LTIMindtree, added that AI must become core to corporate strategy. “We want to be seen as an AI company solving our customers’ problems with AI, and we need to be adopters internally,” he said.
Scaling generative AI pilots
Despite rising adoption, challenges remain. A recent MIT study revealed that 95% of large U.S. firms investing billions in generative AI saw minimal returns due to weak integration strategies. Still, experts caution against writing off early struggles.
“AI adoption is like venture capital investing—many ideas may fail, but the successful ones create disproportionate value,” Singh said. LTIMindtree’s Kumar echoed this, emphasizing a “fail fast” culture to minimise costs while encouraging experimentation. The company has even introduced digital companions for employees to explore AI in day-to-day operations.
Data and infrastructure challenges
Experts agree that scaling AI requires not just leadership but also robust data and infrastructure readiness. Singh of PwC warned that weak datasets remain a bottleneck. “If you want to hyperpersonalise customer conversations, you need clean, structured data to support AI agents,” he said.
Agrawal pointed out that AI agents—widely viewed as the next frontier—are already being deployed by 59% of Indian leaders to automate workflows, with adoption expected to climb above 90% in the next 18 months. While compute-intensive and costly today, she added that falling token prices and advances in infrastructure will make them more affordable over time.
The road ahead
The transition to leadership-led AI adoption signals a maturing market. From cautious pilots to enterprise-wide deployments, organisations are moving toward embedding AI into the fabric of business. As Singh put it, “As you solve the data layer, AI will become more and more real.”
With leaders now steering the AI agenda, the focus is shifting from experiments to measurable returns—marking a turning point in how enterprises worldwide harness artificial intelligence.
