A new Cisco report reveals a widening gap between companies experimenting with artificial intelligence and those actually realising measurable business value. According to the Cisco AI Readiness Index 2025, just 13% of global organisations — dubbed “Pacesetters” — are deploying AI at a scale and speed that drives consistent ROI.
The study highlights that 97% of these AI-ready companies are already achieving tangible returns by building infrastructure around four key priorities: power optimisation, network readiness, continuous model tuning, and embedded security.
Infrastructure Choices Define AI Leaders
“AI leaders architect differently — and the Pacesetters prove it,” said Simon Miceli, Managing Director of Cloud and AI Infrastructure for Asia Pacific, Japan and Greater China at Cisco. He noted that the 45% of Indian companies still lacking this foundation risk accumulating what Cisco calls ‘AI infrastructure debt’ — technical gaps that will slow innovation and inflate costs over time.
Globally, 96% of Pacesetters have already built dedicated infrastructure to optimise power consumption, compared to just 55% in India. With AI workloads expected to increase by over 50% in the next three to five years, Cisco warns that power constraints are emerging as a critical bottleneck for enterprise AI expansion.
Networks Emerging as the Next Bottleneck
The report emphasises that while many organisations focus heavily on compute capacity, network infrastructure often determines scalability. Among Pacesetters, 81% rate their networks as “optimal” for AI workloads, compared to 27% in India.
As workloads double, companies that fail to integrate network, cloud, and data architectures risk operational bottlenecks that are difficult to fix mid-deployment. Cisco’s data shows that Pacesetters prioritise networks first, integrating AI with networking at higher rates than with cloud systems.
Continuous Optimisation and Built-In Security
Beyond deployment, continuous optimisation emerged as a defining factor. 72% of global Pacesetters continuously monitor and retrain AI models, compared to just 33% in India. This allows them to update models in under an hour and run over 50 retraining cycles a year — far ahead of the global average.
Security integration is another differentiator. While 91% of Indian organisations are deploying autonomous AI agents, only 37% can secure them effectively. In contrast, Pacesetters build security as infrastructure from day one — a model Miceli says “turns security from a gatekeeper into a growth enabler.”
The Cost of AI Infrastructure Debt
Cisco warns that organisations taking a reactive approach to AI deployment face mounting technical debt — from power and network strain to compliance and security exposure. “Pacesetters win not because they spend more, but because they architect early,” the report concludes.
The study, based on surveys of 8,000 senior IT and business leaders across 30 markets, suggests that India’s AI momentum could stall without faster investment in power capacity, network modernisation, and cross-functional AI security frameworks.
