According to Network World, Cisco’s research reveals a dramatic gap between AI “pacesetters” and other companies, with infrastructure readiness being the key differentiator. Seventy-one percent of pacesetters have networks that can scale instantly for AI projects, compared to widespread unpreparedness elsewhere. The data shows 93% of leaders have fully prepared data systems versus just 34% of others, while 76% have centralized their in-house data compared to only 19% of non-pacesetters. Cisco identifies “infrastructure debt” as the primary culprit—companies taking shortcuts by not upgrading infrastructure, skipping security reviews, or avoiding skilled hiring. This analysis reveals why these IT shortcuts are directly curbing AI returns across the industry.
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Table of Contents
The True Price of Cutting Corners
What makes artificial intelligence infrastructure requirements fundamentally different from previous technological shifts is the compound effect of data volume, processing demands, and latency sensitivity. Unlike traditional enterprise applications that could tolerate “best effort” networking, AI workloads create cascading failures when any component underperforms. The high compute costs and unpredictable hybrid-infrastructure expenses that Cisco identifies as warning signs aren’t just operational inefficiencies—they’re symptoms of architectural mismatch. Many companies are trying to run Formula One workloads on family sedan infrastructure, and the performance penalties are compounding daily.
Beyond Technology: The Executive Mandate
The leadership gap highlighted in the research points to a deeper organizational challenge. Successful AI implementation requires breaking down traditional silos between IT, data science, and business units in ways that most companies have never attempted. The fact that 62% of pacesetters have established processes for generating, piloting, and scaling AI use cases—versus just 13% of others—suggests this isn’t about buying better software but about building better organizational muscles. Companies struggling with AI returns often make the fatal mistake of treating AI as another IT project rather than a core business transformation initiative.
The Coming Infrastructure Refresh Wave
The research indicating that roughly three-quarters of pacesetters are investing in new data center capacity signals an industry-wide infrastructure refresh that will separate the AI haves from have-nots. What’s particularly telling is that this isn’t just about adding more capacity—it’s about designing for AI-specific requirements like the extreme latency sensitivity of real-time inference workloads. Companies that delay these investments aren’t just falling behind temporarily; they’re accumulating technical debt that will become increasingly expensive to resolve as AI models grow more complex and data-intensive.
When Promises Meet Performance
The expectation gap is becoming dangerously wide—while 83% of companies plan to deploy AI agents within a year, most admit their networks aren’t ready for that scale. This disconnect between ambition and capability is where ROI evaporates. The pacesetters expecting 50% to 100% ROI within a year have likely built the measurement frameworks to track actual business impact, not just technical metrics. Companies taking infrastructure shortcuts often compound the problem by having inadequate measurement systems, creating a double-blind scenario where they can’t see their failures or course-correct effectively.
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Market Implications and Future Outlook
We’re approaching an inflection point where AI infrastructure readiness will become a primary competitive differentiator across industries. The companies that have invested in scalable networks, centralized data, and mature governance aren’t just better positioned to implement AI—they’re building structural advantages that competitors will struggle to match. The pressure for tangible ROI that Cisco notes will likely accelerate this divergence, creating a winner-take-most dynamic in many sectors. Organizations that continue taking infrastructure shortcuts may find themselves permanently relegated to the second tier as the gap between pacesetters and strugglers becomes institutionalized.
