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The Most AI-Ready Companies Outpace Peers in the Race to Value

According to the Cisco AI Readiness Index, a small but consistent group of companies surveyed — the "Pacesetters," about 13% of organizations for the last three years — outperform their peers across every measure of AI value in the global study of over 8,000 AI leaders across 30 markets and 26 industries.

The Pacesetters' sustained advantage indicates a new form of resilience: a disciplined, system-level approach that balances strategic drivers with the data and infrastructure needed to keep pace with AI's accelerating evolution. They're already architecting for the future with 98% designing their networks for the growth, scale and complexity of AI compared to 46% overall.

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.

The Pacesetter Profile: Readiness as Competitive Advantage

Cisco's research outlines a consistent pattern among these leaders delivering real returns.

They make AI part of the business, not a side project

Nearly all Pacesetters (99%) have a defined AI roadmap (vs 58% overall) and 91% (vs 35%) have a change-management plan. Budgets match intent, with 79% making AI the top investment priority (vs 24%) and 96% with short- and long-term funding strategies (vs 43%).

They build infrastructure that's ready to grow

They architect for the always-on AI era. 71% of Pacesetters say their networks are fully flexible and can scale instantly for any AI project (vs 15% overall), and 77% are investing in new data-center capacity within the next 12 months (vs 43%).

They move pilots into production

62% have a mature, repeatable innovation process for generating and scaling AI use cases (vs 13% overall), and three-quarters (77%) have already finalized those use cases (vs 18%).

They measure what matters

95% track the impact of their AI investments — three times higher than others — and 71% are confident their use cases will generate new revenue streams, more than double the overall average.

They turn security into strength

87% are highly aware of AI-specific threats (vs 42% overall), 62% integrate AI into their security and identity systems (vs 29%), and 75% are fully equipped to control and secure AI agents (vs 31%). Trust is part of the Pacesetters' value equation.

Pacesetters achieve more widespread results than their peers because of this approach: 90% report gains in profitability, productivity, and innovation, compared with ~60% overall.

AI Agents: Ambition Outpacing Readiness

The Index shows 83% of organizations plan to deploy AI agents, and nearly 40% expect them to work alongside employees within a year. But for majority of these companies, AI agents are exposing weak foundations — systems that can barely handle reactive, task-based AI, let alone AI systems that act autonomously and learn continuously. More than half (54%) of respondents say their networks can't scale for complexity or data volume and just 15% describe their networks as flexible or adaptable.

Pacesetters are again the exception. Their disciplined, system-level approach has already helped lay the foundations they will need to scale.

AI Infrastructure Debt: The emerging drag on value

The report introduces a new concept — AI Infrastructure Debt — the modern evolution of technical and digital debt that once held back digital transformation.

It's the silent accumulation of compromises, deferred upgrades, and underfunded architecture that erodes the value of AI over time. Some early warning signs are already visible: 62% expect workloads to rise by over 30% within three years, 64% struggle to centralize data, only 26% have robust GPU capacity and fewer than one in three can detect or prevent AI-specific threats.

These early warning signs point to a gap between AI ambition and operational readiness. But when the systems that power AI aren't secure, the debt can increase risk. Pacesetters aren't immune, but their foresight, governance, and investment discipline help them to avoid problems compounding into more costly risks.

Hot Topics

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The Most AI-Ready Companies Outpace Peers in the Race to Value

According to the Cisco AI Readiness Index, a small but consistent group of companies surveyed — the "Pacesetters," about 13% of organizations for the last three years — outperform their peers across every measure of AI value in the global study of over 8,000 AI leaders across 30 markets and 26 industries.

The Pacesetters' sustained advantage indicates a new form of resilience: a disciplined, system-level approach that balances strategic drivers with the data and infrastructure needed to keep pace with AI's accelerating evolution. They're already architecting for the future with 98% designing their networks for the growth, scale and complexity of AI compared to 46% overall.

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.

The Pacesetter Profile: Readiness as Competitive Advantage

Cisco's research outlines a consistent pattern among these leaders delivering real returns.

They make AI part of the business, not a side project

Nearly all Pacesetters (99%) have a defined AI roadmap (vs 58% overall) and 91% (vs 35%) have a change-management plan. Budgets match intent, with 79% making AI the top investment priority (vs 24%) and 96% with short- and long-term funding strategies (vs 43%).

They build infrastructure that's ready to grow

They architect for the always-on AI era. 71% of Pacesetters say their networks are fully flexible and can scale instantly for any AI project (vs 15% overall), and 77% are investing in new data-center capacity within the next 12 months (vs 43%).

They move pilots into production

62% have a mature, repeatable innovation process for generating and scaling AI use cases (vs 13% overall), and three-quarters (77%) have already finalized those use cases (vs 18%).

They measure what matters

95% track the impact of their AI investments — three times higher than others — and 71% are confident their use cases will generate new revenue streams, more than double the overall average.

They turn security into strength

87% are highly aware of AI-specific threats (vs 42% overall), 62% integrate AI into their security and identity systems (vs 29%), and 75% are fully equipped to control and secure AI agents (vs 31%). Trust is part of the Pacesetters' value equation.

Pacesetters achieve more widespread results than their peers because of this approach: 90% report gains in profitability, productivity, and innovation, compared with ~60% overall.

AI Agents: Ambition Outpacing Readiness

The Index shows 83% of organizations plan to deploy AI agents, and nearly 40% expect them to work alongside employees within a year. But for majority of these companies, AI agents are exposing weak foundations — systems that can barely handle reactive, task-based AI, let alone AI systems that act autonomously and learn continuously. More than half (54%) of respondents say their networks can't scale for complexity or data volume and just 15% describe their networks as flexible or adaptable.

Pacesetters are again the exception. Their disciplined, system-level approach has already helped lay the foundations they will need to scale.

AI Infrastructure Debt: The emerging drag on value

The report introduces a new concept — AI Infrastructure Debt — the modern evolution of technical and digital debt that once held back digital transformation.

It's the silent accumulation of compromises, deferred upgrades, and underfunded architecture that erodes the value of AI over time. Some early warning signs are already visible: 62% expect workloads to rise by over 30% within three years, 64% struggle to centralize data, only 26% have robust GPU capacity and fewer than one in three can detect or prevent AI-specific threats.

These early warning signs point to a gap between AI ambition and operational readiness. But when the systems that power AI aren't secure, the debt can increase risk. Pacesetters aren't immune, but their foresight, governance, and investment discipline help them to avoid problems compounding into more costly risks.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.