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

Learn more about Cisco

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.

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

Learn more about Cisco

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.

Related Links

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Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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