2025 AI Readiness Index - Building the right foundation for AI

The 2025 AI Readiness Index by Cisco shows that the Pacesetters are not just investing more, but building smarter. Representing 13 per cent of the total surveyed, these companies have already scaled AI deployment across operations. According to the study, 97 per cent of them are achieving measurable returns, signalling a fundamental difference in their infrastructure decisions.

Simon Miceli, the managing director of Cisco, said the findings show that “AI leaders architect differently.” They build on strong networks, plan power needs early, embed security from the start, and continuously optimise. He warned that 45 per cent of Indian organisations without this level of architectural foresight are risking “AI infrastructure debt”, technical gaps that slow innovation and erode competitiveness.

Four architectural choices that distinguish AI leaders according to the Cisco AI Readiness Index 2025:

1. Predicting and Solving Power Constraints before they arise

More than half of Indian companies expect AI workloads to grow by over 50 per cent within three to five years. Yet, 51 per cent plan to add data centre capacity within a year—despite power infrastructure typically taking up to three years to provision. While 96 per cent of global Pacesetters have dedicated infrastructure to optimise power, the number drops to 55 per cent in India. Many are building AI capacity without the energy systems required to sustain it.

2. Treating the network as the foundation

Pacesetters focus on network performance before compute. Eighty-one per cent rate their networks as optimal for AI workloads compared to just 27 per cent in India. The study warns that when workloads double, network limits could become the first bottleneck. Globally, 79 per cent of Pacesetters have integrated AI with network systems, higher than cloud integration at 61 per cent. In India, the levels are 44 per cent for network and 34 per cent for cloud, with no clear hierarchy.

3. Continuously optimising post-deployment

For most, getting an AI model into production marks success. For Pacesetters, it’s day one. Seventy-two per cent monitor and retrain models automatically, compared to 33 per cent in India. They can update models in under an hour with minimal downtime, almost twice as fast as their Indian peers. The result is higher agility, with 50 or more optimisation cycles per year against the 12–15 industry average.

4. Building security that accelerates, not hinders

Security remains the backbone of scalable AI. Eighty-four per cent of Pacesetters have end-to-end encryption with continuous monitoring, compared with 33 per cent in India. The need is growing urgent as AI agents become autonomous: 91 per cent of Indian companies have deployed such agents, but only 37 per cent can secure them effectively. For Pacesetters, those figures are 96 and 75 per cent respectively, still a gap, but manageable because security was designed in, not added later.

The growing risk of ‘AI infrastructure debt’

The report warns of an emerging risk: AI infrastructure debt. This refers to the cumulative cost of short-term decisions in computing, network, power, or security that slow growth over time. Companies deploying AI without integrated foundations face higher operational and compliance risks, as well as reduced competitiveness.

For India, the early signs are visible: under-provisioned power, unoptimized networks, and security lagging behind rapid AI deployment. Despite expecting 50% more workload growth, only 27% networks are optimal, and 45% infrastructure is missing. The study suggests that being proactive, not reactive, will define future winners.

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