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APM BOTTOM-LINE BENEFIT: Business Continuity

Petri Maanonen

One of the main benefits from implementing a holistic APM solution is, that every stakeholder in delivering the end user experience will have the same perspective and information to base their decisions and actions on, which ensures business continuity.

No matter whether the APM data is used in SLA, diagnostics, end user analytics or continuous service improvement purposes, this “single source of truth” will give the organization the right alignment to act and perform. Many companies see up to 60-70% reduction in downtime and in business impact by using APM solutions to optimize their operations.

One of the biggest impacts to application performance is caused by companies outsourcing/subcontracting their application development outside of their company and their quality control domain. Application quality and performance needs to be built into the application platform and cannot be an afterthought or something that “we’ll fix later”.

The subpar app performance that is accepted in the development phase is bound to manifest itself in the production stage. Modern APM solutions capture this poor performance, but can’t provide the cure. The only way to prevent poor app performance is to expose your app development to the rigorous quality controls and processes early on in the application lifecycle — and actually fix them early in the cycle.

Petri Maanonen is Product Marketing Manager for HP Application Performance Management.

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AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

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This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

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APM BOTTOM-LINE BENEFIT: Business Continuity

Petri Maanonen

One of the main benefits from implementing a holistic APM solution is, that every stakeholder in delivering the end user experience will have the same perspective and information to base their decisions and actions on, which ensures business continuity.

No matter whether the APM data is used in SLA, diagnostics, end user analytics or continuous service improvement purposes, this “single source of truth” will give the organization the right alignment to act and perform. Many companies see up to 60-70% reduction in downtime and in business impact by using APM solutions to optimize their operations.

One of the biggest impacts to application performance is caused by companies outsourcing/subcontracting their application development outside of their company and their quality control domain. Application quality and performance needs to be built into the application platform and cannot be an afterthought or something that “we’ll fix later”.

The subpar app performance that is accepted in the development phase is bound to manifest itself in the production stage. Modern APM solutions capture this poor performance, but can’t provide the cure. The only way to prevent poor app performance is to expose your app development to the rigorous quality controls and processes early on in the application lifecycle — and actually fix them early in the cycle.

Petri Maanonen is Product Marketing Manager for HP Application Performance Management.

The Latest

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...