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APM Evolution: End User Experience

While Application Performance Management (APM) has traditionally monitored and managed the performance and availability of software applications, mobility is forcefully transforming the expectations of the end user and disrupting the marketplace. Failing and low performing mobile, web and SaaS-based applications drive users to seek alternatives – resulting in lost revenue, lost employee productivity, and lost business efficiencies. It is no longer sufficient to monitor the performance and availability of backend systems; businesses must focus on the entire digital end user experience.

APM tools must evolve to focus on the problems that users and customers are seeing from their perspective and give insight as to how to continuously diagnose and correct the digital user experience for them. In order to do this, application teams will need an APM tool that cuts through the service tiers. The tool must be able to analyze and correlate issues from the users’ clicks or swipes to the service code execution in order to diagnose any issues along the way – down to the line of code and log messagees. This new APM must also use analytical algorithms to understand the system’s behavior and predict problems and failures before they occur.

Because user experience is the new service level agreement standard, businesses must arm themselves with APM tools that natively provide end-to-end visibility into user experience across mobile, web and e-commerce platforms, monitoring and analyzing API calls, runtime environments, and business transactions in the context of user actions and their devices.

Monica Benjamin is Director of Product Marketing at HPE Software.

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APM Evolution: End User Experience

While Application Performance Management (APM) has traditionally monitored and managed the performance and availability of software applications, mobility is forcefully transforming the expectations of the end user and disrupting the marketplace. Failing and low performing mobile, web and SaaS-based applications drive users to seek alternatives – resulting in lost revenue, lost employee productivity, and lost business efficiencies. It is no longer sufficient to monitor the performance and availability of backend systems; businesses must focus on the entire digital end user experience.

APM tools must evolve to focus on the problems that users and customers are seeing from their perspective and give insight as to how to continuously diagnose and correct the digital user experience for them. In order to do this, application teams will need an APM tool that cuts through the service tiers. The tool must be able to analyze and correlate issues from the users’ clicks or swipes to the service code execution in order to diagnose any issues along the way – down to the line of code and log messagees. This new APM must also use analytical algorithms to understand the system’s behavior and predict problems and failures before they occur.

Because user experience is the new service level agreement standard, businesses must arm themselves with APM tools that natively provide end-to-end visibility into user experience across mobile, web and e-commerce platforms, monitoring and analyzing API calls, runtime environments, and business transactions in the context of user actions and their devices.

Monica Benjamin is Director of Product Marketing at HPE Software.

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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 ...

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 4 covers negative impacts of AI ...

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 3 covers barriers and challenges for AI ...