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Nexthink Introduces Application Experience Capability

Nexthink announced it is extending end-to-end experience visibility to the application transaction level with its new Application Experience capability.

With Application Experience, IT teams can go beyond network, sentiment and basic application visibility to deeply analyze and optimize the complete digital experience. With Nexthink’s platform, IT teams can now deliver personalized experiences for every employee in the context of the applications, networks and tools they use all the time, in real-time. Nexthink’s new capability gives IT teams full span of control to ensure a seamless digital experience for employees.

“End-user computing teams are under pressure to continuously deliver great digital experiences to employees, involving hundreds or thousands of on-prem and SaaS applications on millions of different devices,” said Pedro Bados, CEO and Co-founder of Nexthink. “While DevOps in application teams or software vendors can instrument a few of these applications, IT operations teams are ultimately responsible for the delivery, support and sentiment at scale in the enterprise. This product is for them. Now IT operations can understand the experience of every employee with every application all the time.”

With the latest capability, IT teams can quickly isolate whether a poor experience with an application is being caused by the application itself or triggered by a device, network, configuration, or other resource. IT teams can minimize application deployment risks by surfacing employee concerns quickly through context-driven employee feedback. The solution also helps reduce application license costs by aligning licenses with actual employee usage.

Additionally, in the case of SaaS applications, organizations can hold vendors accountable for availability guarantees through clear data that shows application downtime.

Key enhancements to the Nexthink Experience platform include:

- Adoption insights: Visibility into application adoption and usage to show how employee groups are experiencing applications and features at the domain, page and transaction level, enabling employee education and application workflow improvements to increase adoption and productivity.

- Deep insight and context-driven automation: Proactive experience improvement for application page and transaction availability and performance, with in-context remediation across the entire digital environment and workforce.

- Easy data exploration: Real-time, out-of-the-box filters and visualizations for application pages and transactions – providing complete visibility into the health, adoption and employee sentiment of applications.

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Nexthink Introduces Application Experience Capability

Nexthink announced it is extending end-to-end experience visibility to the application transaction level with its new Application Experience capability.

With Application Experience, IT teams can go beyond network, sentiment and basic application visibility to deeply analyze and optimize the complete digital experience. With Nexthink’s platform, IT teams can now deliver personalized experiences for every employee in the context of the applications, networks and tools they use all the time, in real-time. Nexthink’s new capability gives IT teams full span of control to ensure a seamless digital experience for employees.

“End-user computing teams are under pressure to continuously deliver great digital experiences to employees, involving hundreds or thousands of on-prem and SaaS applications on millions of different devices,” said Pedro Bados, CEO and Co-founder of Nexthink. “While DevOps in application teams or software vendors can instrument a few of these applications, IT operations teams are ultimately responsible for the delivery, support and sentiment at scale in the enterprise. This product is for them. Now IT operations can understand the experience of every employee with every application all the time.”

With the latest capability, IT teams can quickly isolate whether a poor experience with an application is being caused by the application itself or triggered by a device, network, configuration, or other resource. IT teams can minimize application deployment risks by surfacing employee concerns quickly through context-driven employee feedback. The solution also helps reduce application license costs by aligning licenses with actual employee usage.

Additionally, in the case of SaaS applications, organizations can hold vendors accountable for availability guarantees through clear data that shows application downtime.

Key enhancements to the Nexthink Experience platform include:

- Adoption insights: Visibility into application adoption and usage to show how employee groups are experiencing applications and features at the domain, page and transaction level, enabling employee education and application workflow improvements to increase adoption and productivity.

- Deep insight and context-driven automation: Proactive experience improvement for application page and transaction availability and performance, with in-context remediation across the entire digital environment and workforce.

- Easy data exploration: Real-time, out-of-the-box filters and visualizations for application pages and transactions – providing complete visibility into the health, adoption and employee sentiment of applications.

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For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...