AI Readiness: Are 87% of Companies Missing the Point?

This data from the Cisco AI Readiness Index provides a phenomenal insight into the current state of enterprise AI adoption. What I see immediately is a chasm. We have a small cohort of companies that are succeeding and a vast majority that are currently setting themselves up for disappointment. It is a stunning difference. You can find out more about the report here.

The core finding is simple: only thirteen percent of organizations surveyed are fully prepared for AI. This is a remarkably low figure given the pervasive industry dialogue about large language models and generative applications. Yet, this prepared minority is performing magnificently. My analysis shows they are four times more likely to move their AI pilots directly into production systems. They are also fifty percent more likely to see a measurable business value from those investments. Preparation, in short, is the difference between experimentation and real-world, tangible return on investment. It is a brilliant differentiator.

For the eighty-seven percent of organizations not fully ready, the path forward looks significantly more arduous. The data suggests these companies possess plenty of ambition but lack the necessary technical foundation. This disconnect is most apparent when we look at the immediate future of the workforce. A staggering eighty-three percent of companies plan to deploy some form of AI agents. Further compounding the urgency, forty percent expect these agents to be working alongside their human employees within the next twelve months. This is not a slow transformation. This is a rapid operational shift. It is a change in the fundamental structure of the workforce and how tasks are executed.

However, the report makes clear that few of these companies have the secure infrastructure architected to sustain such an ambitious and rapid deployment. This is the inflection point where ambition collides with reality. You cannot introduce millions of automated entities, each accessing sensitive data and executing business processes, without a robust and secure networking backbone. The security posture of an organization planning to deploy this many agents must be radically redefined. Every agent is a new endpoint. Every automated process is a potential new attack vector. The unprepared majority simply does not have the network visibility or the zero-trust controls designed to manage this expanded attack surface effectively. Security is paramount.

The challenge extends beyond security and into the fundamental compute fabric of the enterprise. The report highlights early signs of significant disruption to value realization. These are not abstract concerns. These are hard, physical, and architectural constraints that slow progress to a crawl. Chief among them are rising workloads, insufficient GPU capacity, and a widespread lack of centralized data.

Let us focus on the compute constraint. The demand for Graphics Processing Units, particularly the high-end accelerators suitable for large model training and inference, is vastly outstripping global supply. For the vast majority of companies, securing sufficient GPU capacity has become a critical bottleneck. Rising workloads simply exacerbate the problem. As more departments begin piloting or deploying small models, the demand for compute spikes across the organization. If the resources are not pooled, managed, and provisioned centrally, performance degradation is inevitable. The prepared thirteen percent likely architected their compute systems with foresight, ensuring scalable, shared pools of GPU resources. They planned for growth. The others face scrambling for expensive, last-minute capacity or resorting to public cloud resources that may not integrate seamlessly or cost-effectively.

The final structural hurdle is data. The finding that a lack of centralized data is a major disruption sign is deeply concerning. AI models are only as effective as the data they consume. If enterprise data remains fragmented, siloed in different departments, or locked away in incompatible legacy systems, the resulting AI output will be poor. Garbage in, garbage out. A lack of centralized data is essentially a structural flaw in the organization’s ability to execute a successful AI strategy. The prepared companies have invested heavily in unified data platforms and governance models designed to feed clean, integrated data into their AI stacks. They understood that data preparation is eighty percent of the AI effort. The unprepared organizations face years of costly, time-consuming data remediation projects before they can truly realize the promise of their AI agent deployments.

My perspective is that the thirteen percent did not achieve their readiness status by accident. They architected their networks, their security posture, and their compute and data layers specifically for the demands of generative AI. They are focused on the infrastructure required to run the models, not just the models themselves. The remaining eighty-seven percent must execute a rapid pivot. CIOs must shift focus from simply piloting models to radically overhauling their foundational compute and data strategies. This means viewing the AI agent ambition as a catalyst for immediate, massive investment in network security and centralized, high-performance infrastructure. They need to stop looking at the models and start looking at the wires, the chips, and the databases. This is an all-encompassing infrastructure challenge.