MELBOURNE, 12th November, 2025: New findings from Cisco’s 2025 AI Readiness Index reveal that Pacesetters the 13% of organizations surveyed that are AI ready are making fundamentally different infrastructure decisions that create compounding advantages. Notably, 97% of global Pacesetters have deployed AI at the scale and speed needed to unlock use cases and achieve ROI.

“This year’s report shows AI leaders architect differently—and the Pacesetters prove it. They build network-first foundations, prioritize power infrastructure, optimize continuously, and embed security from day one. And as a result, 97% deliver tangible, scalable value. The 45% of India  companies without this level of architectural foresight risk accumulating ‘AI Infrastructure Debt’—the shortcuts and gaps that compound into bottlenecks that cripple innovation and competitiveness,” said Simon Miceli, Managing Director, Cloud and AI infrastructure, Asia Pacific, Japan and Greater China, Cisco.

The Cisco AI Readiness Index 2025 reveals four architectural choices that distinguish AI leaders:

  1. Solve for power constraints before hitting them

More than half of India organizations surveyed expect AI workloads to increase by more than 50%+ within three to five years, with 51% building new data center capacity in the next 12 months. But power infrastructure takes 18-36 months to provision. 96% of global Pacesetters have built dedicated infrastructure to optimize power consumption, compared to 55% in India. More than half of India companies are building AI capacity without the power infrastructure to run it. Pacesetters build for the constraint that is far more difficult to retrofit.

  1. Treat network as a foundation, not an afterthought

While most respondents focus on compute, Pacesetters prioritize network infrastructure. 81% of global Pacesetters rate their network as ‘optimal’ for AI workloads versus 27% in India. When workloads double, network could become the bottleneck before compute does—and it may not be so easy to rewire a data center under production load. More Pacesetters have fully integrated AI with network (79%) than with cloud (61%). India  organizations surveyed integrate both at similar, but lower levels –44% network, 34% cloud – with no clear hierarchy. Pacesetters chose network as a foundation. Everything else builds on top.

  1. Drive real value beyond deployment through continuous optimization

Getting AI models into production is worth celebrating. But model performance can degrade. Pacesetters focus on continuous optimization after deployment. 72% have continuous monitoring with automated retraining versus 33% in India. This enables speed: 65% of Pacesetters can update models in under an hour with minimal downtime versus 35% in India. The advantage compounds: organizations that optimize 3-4x faster run 50+ cycles per year versus 12-15 cycles. Pacesetters treat deployment as day one, not the finish line.

  1. Build in security that enables velocity, not blocks it

Pacesetters build security as infrastructure from day one. 84% of global Pacesetters have end-to-end encryption with continuous monitoring versus 33% in India . This approach to security becomes even more critical with AI agents: 91% of India  organizations are deploying autonomous AI agents, yet only 37% can properly secure them. For Pacesetters, it’s 96% deploying and 75% securing—still a gap, but manageable because security is more likely to be architected in, not bolted on.  Pacesetters are building security into their infrastructure to propel innovation, not gates that slow it down.

The compounding cost of AI infrastructure debt

Pacesetters are winning not because they spend more, but because they made architectural bets early—before workloads demanded them, before bottlenecks emerged, before security became urgent.

Organizations that choose to build their AI reactively face the risk of accumulating AI Infrastructure Debt. And the early warning signs are already visible across India respondents—organizations deploying AI agents faster than they can secure them, networks rated optimal by only 27%, power infrastructure missing for 45% despite half expecting more than 50% workload growth.