The rush to deploy AI is reshaping how companies think about risk, according to Cisco. A global study finds that while most organizations are moving quickly to adopt AI, many are not ready for the pressure it puts on their systems and security.
A small group of companies have managed to stay ahead. These “Pacesetters” treat AI readiness as part of their long-term strategy. They plan for scale, build solid infrastructure, and take security seriously.
The rise of AI infrastructure debt
Researchers introduce the idea of AI infrastructure debt, the buildup of gaps and delays that come from trying to deploy AI on systems that were never designed for it. These gaps create friction, increase costs, and open security risks that grow over time.
Many companies admit their infrastructure is not keeping up. Few feel confident their systems can scale with the workloads that AI brings. The same problem shows up in security. Only a small portion of organizations say they can protect the AI systems they run. Data protection and access control remain weak points, and monitoring tools are often missing.
The controls that once protected applications and users may not extend to autonomous AI systems that make decisions and take actions on their own.
Agentic AI brings new attack surfaces
Most companies plan to use agentic AI, systems that can perform tasks, communicate with other software, and make operational choices without constant supervision.
These agents could automate customer support, manage supply chains, or detect threats. But they also expand the attack surface. If one is misconfigured or breached, it can spread problems across connected systems.
Many organizations have not yet decided how they will control or monitor these agents. Most have no plan for human oversight once agents begin handling parts of business operations. The risk is that deployment moves faster than defense.
Security gaps are already visible
Even before agentic systems arrive, many companies struggle with basic readiness. Rising compute costs, limited data integration, and network strain are common. Few have the centralized data or reliable infrastructure needed for large-scale AI.
Encryption, access control, and tamper detection are unevenly applied. Many teams still treat them as separate add-ons rather than built-in safeguards. That patchwork approach makes it harder to spot problems early and contain them when they happen.
Pacesetters handle this differently. They build security into the core of their AI programs, modernize infrastructure before scaling, and maintain stronger governance over how AI is used. That discipline gives them flexibility when workloads rise and new threats appear.
The risk of ignored debt
AI infrastructure debt does not appear all at once. It builds slowly as upgrades are delayed and quick fixes pile up. What starts as a small gap in compute or data management can turn into a weakness that limits growth and invites attacks.
If left unresolved, this debt can slow innovation and erode trust in AI systems. Every new model, dataset, and integration point becomes a potential attack surface. Without steady investment, it becomes harder to understand where sensitive information lives and how it is protected.
Organizations that address these gaps early can reduce long-term costs and build systems they can trust. Those that postpone the work will face both technical and financial fallout later.
Readiness drives value
“We’re moving past the era of question-answering chatbots and stepping into the next major phase of AI: agents that independently execute tasks,” said Jeetu Patel, Cisco’s President and Chief Product Officer. “Our study shows that over 80% of companies are prioritizing agentic solutions, with two out of three reporting that these systems are already meeting or exceeding their performance goals. The evidence points to a massive competitive advantage: companies that are further along are seeing dramatically stronger returns than their peers.”
While the study warns of major gaps, it also shows what progress looks like. Pacesetters are more likely to report measurable gains in profitability, productivity, and innovation. Nearly all design their infrastructure for future demands and maintain strong governance frameworks.
AI’s value depends on the systems that support it. For most enterprises, the biggest obstacle is not the technology itself but the readiness to manage it securely and at scale.
The next phase of AI will test that readiness. Companies that plan, modernize, and embed security early will shape how safely the technology evolves. Organizations that delay may end up paying back an infrastructure debt that grows more expensive with every deployment.
