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Availability ≠ Responsiveness

Robin Lyon

How many of us IT professionals have been in a meeting similar to this: The chairs of various departments throughout the company are sitting around a long table and are giving a monthly summary.  IT presents that the applications, network and servers were some amount of 9’s available and may explain an outage. The meeting goes on and then one of the heads explains a failure to meet department goals by stating some application was "slow."


IT is asked about it but unfortunately can only present data upon number of tickets and general up time. The slow comment is then picked up another department and IT is left in the untenable position of defending its metrics and supposedly achieved goals while other departments are blaming IT for lack of productivity. 

The real problem is one of communication of expectations. IT has data that supports availability but the customer is complaining of slowness. Slowness is a subjective term and for IT to resolve the difficulty different metrics and SLAs are needed. Fortunately, there is a perfectly good way to measure slowness – time. When we think of availability we need to understand we are actually speaking of capacity while the users are interested in throughput. 

By measuring transaction time (the amount of time it takes for the user to commit an action and receive the corresponding data from the program they are using) IT can state how fast an application is working in objective terms. SLAs can be established that some percentage of the transactions during a reporting period will be completed within a certain amount of time. This allows business decisions based upon performance and is a salve for the mysterious "slow" comment.

Availability is one of the early metrics IT has used to create a simple number to represent complex systems. 

Robin Lyon is Director of Analytics at AppEnsure.

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Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

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 ...

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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 ...

Availability ≠ Responsiveness

Robin Lyon

How many of us IT professionals have been in a meeting similar to this: The chairs of various departments throughout the company are sitting around a long table and are giving a monthly summary.  IT presents that the applications, network and servers were some amount of 9’s available and may explain an outage. The meeting goes on and then one of the heads explains a failure to meet department goals by stating some application was "slow."


IT is asked about it but unfortunately can only present data upon number of tickets and general up time. The slow comment is then picked up another department and IT is left in the untenable position of defending its metrics and supposedly achieved goals while other departments are blaming IT for lack of productivity. 

The real problem is one of communication of expectations. IT has data that supports availability but the customer is complaining of slowness. Slowness is a subjective term and for IT to resolve the difficulty different metrics and SLAs are needed. Fortunately, there is a perfectly good way to measure slowness – time. When we think of availability we need to understand we are actually speaking of capacity while the users are interested in throughput. 

By measuring transaction time (the amount of time it takes for the user to commit an action and receive the corresponding data from the program they are using) IT can state how fast an application is working in objective terms. SLAs can be established that some percentage of the transactions during a reporting period will be completed within a certain amount of time. This allows business decisions based upon performance and is a salve for the mysterious "slow" comment.

Availability is one of the early metrics IT has used to create a simple number to represent complex systems. 

Robin Lyon is Director of Analytics at AppEnsure.

Hot Topics

The Latest

Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

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 ...