AI-ready companies turning network pilots into profit | Computer Weekly

"Regarding AI agents, the survey found ambition was outpacing readiness. Overall, 83% of organisations planned to deploy AI agents, and nearly 40% expected them to work alongside employees within a year. But the study discovered that, for majority of these companies, AI agents were exposing weak foundations - that is, systems that can barely handle reactive, task-based AI, let alone AI systems that act autonomously and learn continuously."

"Cisco added that the combination of foresight and foundation is delivering real, tangible results at a time when two major forces are starting to reshape the landscape: AI agents, which raise the bar for scale, security and governance; and AI infrastructure debt, the early warning signs of hidden bottlenecks that threaten to erode long-term value. Regarding AI agents, the survey found ambition was outpacing readiness."

"AI infrastructure debt was called the modern evolution of technical and digital debt that once held back digital transformation. Moreover, the survey regarded it as "the silent accumulation of compromises, deferred upgrades, and underfunded architecture that erodes the value of AI over time". Some 62% of firms expect workloads to rise by over 30% within three years, 64% struggle to centralise data, only 26% said that they have robust GPU capacity."

Only 13% of businesses are fully prepared for AI, while organisations that are ready are up to four times more likely to move pilots into production and 50% more likely to see measurable value. A global double-blind survey of 8,000 senior IT and business leaders across 26 industries and 30 markets provided the data. Eighty-three percent of organisations plan to deploy AI agents and nearly 40% expect those agents to work alongside employees within a year. More than half of respondents said networks cannot scale for complexity or data volume and only 15% describe networks as flexible. AI infrastructure debt describes the silent accumulation of compromises, deferred upgrades, and underfunded architecture that erodes AI value over time; many firms also report rising workloads, data centralisation struggles, and limited GPU capacity.

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