
Nexthink announced Mobile Experience, a natively-built solution that extends Nexthink's comprehensive experience-level insights to Android and iOS devices.
"For millions of employees, especially frontline workers, their primary device isn't even a laptop anymore - it's a smartphone or tablet," said Samuele Gantner, Chief Product Officer, Nexthink. "Yet mobile device insights are still a huge blind spot for IT. UEM tools help manage configuration and enforce compliance, but they don't answer the question that should matter most: can mobile users actually do their jobs? Without knowing what's happening with the mobile device, IT is just reactive to piling tickets or device replacement fire drills."
And that's the critical gap Mobile Experience intends to resolve. "Nexthink is now providing a level of visibility that transforms mobile device management into a proactive and DEX-driven function," Gantner explains.
With real-time mobile insights delivered directly to the Nexthink Infinity platform, IT teams can:
- Detect device performance degradation early: Monitor memory, storage, and deep battery health trends to identify devices at risk of failing before worker productivity is impacted.
- Understand the root cause of connectivity issues: Continuous Wi-Fi and cellular data network tracking allows IT to distinguish between user-side issues, such as poor signal due to the mobile device antenna, and infrastructure-side issues like weak Wi-Fi coverage in a facility.
- Gain continuous visibility into compliance and security posture: Track outdated OS versions, missing patches, and encryption status while adding context from device performance and app connections to better assess risk.
- Optimize hardware and battery refresh decisions: IT can move away from blanket refresh cycles by using a data-driven approach to determine which devices need a battery or full replacement and which can remain in use.
- Improve visibility into app usage and risks: Gain insights into mobile app activity through network connection trends to better understand app adoption, engagement, AI app usage, and identify any non-compliant apps that could pose potential security risks
"DEX management doesn't stop at the laptop," added Gantner. "With Nexthink, organizations can now extend their DEX strategy across every device employees rely on – ensuring that wherever work happens, IT has the visibility to support it."
The Latest
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...