Enterprise AI has no shortage of pilots. What it lacks is a reliable bridge from a promising demonstration to a working system that produces measurable value. Cisco’s third annual AI Readiness Index suggests only 13 percent of organisations worldwide have built that bridge.
Cisco calls this group the “Pacesetters.” They are four times more likely than other organisations to move an AI project from pilot to production and 50 percent more likely to obtain measurable value. Ninety percent report gains in profit, productivity, and innovation, compared with results around 60 percent among their peers.
The gap is not explained by enthusiasm. Many organisations are preparing to introduce AI agents, and leaders expect workloads to climb sharply. The differentiator is disciplined preparation across strategy, infrastructure, data, and security. In Cisco president and chief product officer Jeetu Patel’s formulation, AI does not fail; readiness does.
Pacesetters Treat AI as Core Business
Almost every Pacesetter—99 percent—has a defined AI roadmap, compared with 58 percent of other organisations. For 79 percent of the leading group, AI is the top investment priority. Only 24 percent of the rest give it the same status. Those figures describe companies making coordinated choices rather than funding a collection of disconnected experiments.
Their networks reflect that commitment. Ninety-eight percent of Pacesetters design infrastructure for the scale and complexity of AI, against 46 percent of peers. Seventy-one percent believe their networks can immediately scale for any AI project; among other organisations, confidence falls to 15 percent.
This foundation helps explain why more projects survive contact with production. A pilot can operate with a limited dataset, a small user group, and generous manual support. A live service must handle unpredictable demand, integrate with business systems, remain secure, and continue performing when many employees depend on it at once.
AI Agents Will Test Weak Foundations
The pressure is about to increase. Eighty-three percent of organisations plan to deploy AI agents, while nearly 40 percent expect agents to work alongside employees within a year. Yet more than half say their existing networks cannot handle the volume of data or complexity these systems require.
Security and control create another divide. Three-quarters of Pacesetters feel fully prepared to secure and govern agents, compared with 31 percent of everyone else. That difference becomes critical when an AI moves beyond recommending an action and receives permission to perform one. More autonomy increases the cost of unclear access, weak monitoring, or unreliable infrastructure.
Compute is also constrained. Only one-quarter of surveyed organisations report having sufficient GPU capacity. Meanwhile, close to two-thirds of leaders expect workloads to grow by more than 30 percent during the next three years, and a similar share struggle to consolidate their data. Ambitious agent plans are therefore colliding with capacity and information problems that cannot be solved by a better prompt.
Infrastructure Debt Is Accumulating Quietly
Cisco describes the resulting burden as “AI Infrastructure Debt”: postponed upgrades, compromises, and underfunded plans that gradually restrict the value an organisation can extract from AI. Like traditional technical debt, it is easy to ignore when an early project still works. The bill arrives when teams try to scale, connect more data, or apply consistent security across many deployments.
The Index does not argue that every company should spend indiscriminately. It shows that investment has to follow a coherent roadmap. Networks, GPUs, data preparation, and agent controls should support defined business outcomes. Otherwise, organisations risk buying isolated capabilities that become another layer of debt.
The 13 percent figure is sobering, but the Pacesetters make the remedy concrete. They turn strategic priority into architecture, governance, and production discipline. Their advantage is not that they discovered a secret model. It is that they prepared the organisation surrounding the model—and can therefore convert AI enthusiasm into systems that keep working after the pilot ends.
