Skip to main content

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

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

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

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ...