NWN announced that the United States Patent and Trademark Office (USPTO) will be issuing a patent providing the company with broad coverage for the core intelligence underpinning its proprietary Experience Management Platform (EMP).
The newly issued patent, titled, “System and Method for Unified Communication Monitoring,” validates and protects NWN’s approach to collecting, correlating and analyzing real-time data across complex IT environments.
EMP is a unified platform for AI-powered managed services that provides full visibility, centralized control and co-managed IT operations for the digital-first workplace. It leverages agentic AI, automation and proactive monitoring to enable predictive support services that reduce resolution times and increase productivity. EMP integrates advanced capabilities, such as AiVA, DEX and Asset Assurance, to maximize efficiency and empower both employees and IT teams.
“EMP is the intelligence layer that transforms NWN’s managed services into a strategic advantage for our customers,” said Jim Sullivan, President and CEO of NWN. “This achievement is a testament to our team’s talent and reinforces our innovative approach to delivering secure, scalable and personalized AI-powered experiences that drive measurable outcomes in the modern workplace.”
Key EMP features and capabilities covered under the newly granted patent include:
- Model-based analytics representing live device states across clusters, data centers, and customer environments
- Threshold-based anomaly detection for latency, disconnections, and error accumulation
- Subnet impact analysis using last-known IP mapping and model correlation
- Automated device reset and remediation workflows
- Plugin-based data collection that prevents system overload while maintaining visibility
EMP enables organizations to optimize NWN’s award-winning integrated service offerings, including Intelligent Workplace, Customer Experience, Managed Devices, Cybersecurity, Intelligent Connectivity and Intelligent Cloud. Its customizable dashboards deliver tailored insights, and AI-powered analytics offer actionable intelligence to meet evolving demands.
The Latest
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 ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.