New report identifies critical infrastructure gap as companies rush to deploy AI agents without adequate network or data foundations
In sum – what we know:
- An AI readiness gap – Cisco finds only 13% of organizations (“Pacesetters”) are fully AI-ready, while 54% report their networks cannot scale for current complexity or data volume.
- Infrastructure deficits – Just 15% of global organizations surveyed have networks fully ready for AI, and only 19% possess fully centralized data infrastructure.
- Performance impact – Pacesetters are 4x more likely to move pilots to production and 90% report gains in profitability, productivity, and innovation compared to ~60% of peers.
Many companies are rushing to deploy AI, but the infrastructure needed to actually run it apparently isn’t there yet. Cisco’s latest AI Readiness Index makes that clear. Organizations want to implement AI across their operations, but the underlying networks and data systems aren’t built to handle what AI demands. That gap is creating problems before these projects can even deliver value.
The third annual report shows that networks, in particular, aren’t ready. They can’t handle the complexity, speed, or data volume that modern AI deployments require. That’s a pretty major issue, and it’s widening the divide between the small group of organizations that have their infrastructure sorted and everyone else trying to deploy without it.
The infrastructure crisis
Cisco surveyed 8,000 senior IT and business leaders across 30 markets and 26 industries. What emerged is a divide between “Pacesetters” — the 13% of organizations that are fully ready for AI — and everyone else. The gap shows up most clearly in network and data readiness.
Only 34% of companies feel they have fully integrated networks ready for AI. Among Pacesetters, it’s 79%. When it comes to scalability, the numbers get worse. Just 15% of all organizations have networks fully ready for AI deployment, while 71% of Pacesetters report flexible networks that can scale instantly for new AI projects. More than half acknowledge their networks can’t even scale for current complexity or data volume.
Data fragmentation is another major issue. While 76% of Pacesetters have centralized data infrastructure, the global average is 19%. That fragmentation creates visibility problems and inefficiencies that make AI scaling harder. And, on top of that, insufficient GPU capacity and rising workloads are straining systems that are already stretched thin.
The “AI infrastructure debt”
There’s a disconnect between what companies want to do with AI and what their systems can actually support. While 83% of companies say they plan to deploy AI agents within a year, the foundations needed to support those systems are largely missing. Cisco calls it “AI infrastructure debt” — a mounting obligation that will eventually require either significant upgrades or scaled-back ambitions.
The timeline is even more aggressive in some cases. 40% of organizations expect AI agents to work alongside employees within the next 12 months. But current systems struggle to handle even reactive, task-based AI, let alone the autonomous learning systems these organizations envision. As AI agents become more common, they’re exposing weak infrastructure across the board, leading to performance bottlenecks, security vulnerabilities, and disappointing returns on investment.
Clearly, organizations need to address fundamental deficiencies in their technology stacks before AI can scale successfully. Without resolving those foundational issues, companies are essentially building advanced software on unstable platforms.
The pacesetter advantage
Success in AI implementation comes down to what Cisco calls the “Six Pillars Framework.” Infrastructure and Data are weighted most heavily, at 25% and 20% respectively. Other pillars include Strategy, Governance, Talent, and Culture, but infrastructure and data are the foundation.
Pacesetters show exceptional operational discipline. 95% actively track ROI on AI investments, a practice missing from most AI programs. That focus yields measurable results. Pacesetters are four times more likely to move pilots into production and 50% more likely to see measurable value from AI investments. While 77% of Pacesetters have use cases finalized and in production, the global average sits at 18%.
Security maturity also varies sharply. 87% of Pacesetters are highly aware of AI-specific threats, compared to 42% overall. 62% integrate AI into security and identity systems versus 29% overall, and 75% are fully equipped to control and secure AI agents compared to 31% overall.
The performance metrics reflect these differences. Around 90% of Pacesetters report gains in profitability, productivity, and innovation, compared to roughly 60% of their peers. 92% of Pacesetters see increased revenue and 91% experience increased profitability. What that shows is that infrastructure readiness has become the competitive frontier in AI adoption.
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