AI Adoption Accelerates Despite Infrastructure Deficiencies
According to reports from Cisco‘s 2025 AI Readiness Index, most organizations are planning significant artificial intelligence deployments without the necessary foundation to support them. Sources indicate that 86% of companies expect AI to improve employee productivity within three years, but these expectations may not align with current capabilities.
The networking hardware manufacturer found that infrastructure scalability represents a major challenge, with 54% of respondents admitting their infrastructure cannot handle rising AI workloads. Only 15% described their networks as “flexible or adaptable” enough for AI implementation, suggesting widespread infrastructure limitations.
Beyond Hardware: The Full AI Readiness Equation
Analysts suggest that infrastructure represents just one component of successful AI adoption. A Cisco spokesperson explained in an email to The Register that strategy, data, governance, human talent, and company culture complete the AI readiness picture.
The report states that companies face particular challenges with agentic AI systems, where 83% of organizations plan deployments but only 31% feel prepared to control and secure these systems. Furthermore, just 32% have identified which human tasks they want AI agents to supplement or take over.
The Emerging Challenge of AI Infrastructure Debt
Cisco’s analysis introduces the concept of “AI infrastructure debt,” a new iteration of traditional technical debt. According to the report, this phenomenon occurs when organizations make early compromises that later become systemic bottlenecks.
“As history with technical debt shows, what looks like an acceptable compromise in the early phases can snowball into systemic drag,” Cisco noted in the report. Sources indicate that organizations recognize their infrastructure isn’t ready for surging workloads, their security measures remain fragile, and workforce plans are out of sync with technology requirements.
Measurement Gap Skews AI ROI Perception
The study reveals that only 32% of companies have established processes to measure the success or failure of their AI investments. This measurement gap reportedly makes it difficult to determine actual AI value and could be skewing perceptions about AI return on investment.
According to the analysis available through Cisco’s AI readiness resources, the inability to properly track results means many organizations cannot accurately assess whether their AI initiatives are delivering expected benefits.
Pacesetter Companies Demonstrate Systematic Approach
Cisco’s research identifies a small group of “Pacesetter” companies representing just 10-15% of organizations that demonstrate significantly higher readiness across all measured categories. Among these leaders, 74% report high or full readiness in IT infrastructure, 93% in data management, and 84% in governance.
Contrary to assumptions that resources alone determine success, sources indicate that Pacesetters exist across company size brackets. “What really seems to distinguish Pacesetters is discipline and execution: they plan, fund, and measure AI systematically, and get more consistent results,” Cisco explained.
Broader Technology Context
The AI readiness challenges come amid other significant technology developments, including new hardware innovations like the MediaTek-NVIDIA GB10 superchip powering advanced computing systems. Meanwhile, regulatory developments such as Wisconsin’s age verification legislation and recent climate lawsuit rulings demonstrate the evolving legal landscape affecting technology implementation.
Building Sustainable AI Foundations
As organizations move into what Cisco describes as “an always-on, agentic era,” the evidence suggests that readiness built on secure, scalable foundations will determine which companies successfully harness AI’s potential. According to reports, the strain on networks, compute, and security is expected to rise significantly as AI adoption accelerates.
The study concludes that planning AI initiatives requires the same disciplined approach as any major business transformation: without solid foundations, AI investments risk becoming merely window dressing rather than drivers of genuine business value.
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