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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.

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One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

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Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

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If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

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

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...