Indian enterprises possess a substantial five to ten-year period to integrate and fully leverage the Artificial Intelligence (AI) technologies already available, even if further innovation in the field were to cease, according to Srikanth Velamakanni, Chairperson of NASSCOM and Group Chief Executive Officer of Fractal.
Speaking at NASSCOM’s Agentic AI Confluence 2026, Velamakanni highlighted a significant disparity between current AI capabilities and their actual deployment within companies. While interest in AI among CEOs and boards is high, Indian businesses face considerable hurdles in scaling the technology.
Bridging the AI Implementation Gap
Velamakanni emphasized that the immediate challenge for businesses isn't the pace of new AI breakthroughs, but rather their capacity to absorb and implement existing solutions. He coined the term “enterprise friction” to describe the multifaceted obstacles hindering widespread adoption. These include:
- Budget constraints
- Data readiness
- Talent shortages
- Legal considerations
- Concerns regarding accuracy
- Reputational risks
This friction, he noted, is not unique to India but is a global phenomenon slowing enterprise AI integration.
AI's Shift to Operating Expenditure: An Advantage for India
A crucial factor that could potentially give Indian companies a structural advantage this time is AI's evolving consumption model. Historically, Indian businesses have been cautious with technology investments due to high capital costs and comparatively low labor costs, favoring operational efficiency over large upfront capital expenditures (CapEx).
“For the first time, we have an opportunity to not face that problem, because today, AI is coming as an opex, rather than capex for most organisations,” Velamakanni stated.
With AI increasingly available as an operating expense (OpEx), it becomes more economically attractive, potentially accelerating adoption across the Indian corporate landscape.
Varying Adoption Patterns
The pattern of AI adoption also differs between large corporations and smaller businesses. Large enterprises grapple with annual budgeting cycles, stringent regulatory requirements, and entrenched legacy data systems, which can slow down deployment. In contrast, small and mid-sized businesses often find it easier to integrate commercially available AI models for specific functions.
While the opportunity for large companies is immense, so is the inherent friction they face. Velamakanni acknowledged that Indian enterprises are not yet on par with the fastest-adopting markets, such as the US, but noted a rapid surge of interest in AI across all business segments in India.
The near-term focus for Indian companies, he suggested, should be on closing the gap between existing AI capabilities, ensuring data readiness, and achieving comprehensive enterprise-wide deployment, rather than solely anticipating future breakthroughs.