Zenoss announced a strategic partnership with HCL Technologies (HCL) to deliver an end-to-end solution that combines full-stack monitoring, AIOps and AI-powered runbook automation.
This strategic partnership leverages Zenoss Cloud’s intelligent monitoring and AIOps capabilities to derive much-needed context, enabling DRYiCE iAutomate (iAutomate) to intelligently automate and remediate IT incidents. The result is an improvement in IT efficiency while simultaneously lowering costs and mitigating risk.
Under the agreement, Zenoss will offer advanced automation capabilities to its customers by leveraging iAutomate from HCL’s DRYiCE software. In return, HCL will offer its customers monitoring and AIOps products from Zenoss as part of the DRYiCE MTaaS platform. These offerings will provide customers with faster time to value and improved ROI, and allow them to be better positioned for the future.
"Digital transformation has changed IT forever, and our customers look to us for innovative solutions that will help them manage modern, complex environments," said Amit Gupta, EVP and Global Head of DRYiCE at HCL. "This partnership will help us jointly deliver intelligent automation capabilities for today's complex IT environments."
The Zenoss and iAutomate integrated offering delivers:
- Immediate root-cause analysis - Increase efficiency by isolating problems in real time to improve MTTR and eliminate service outage losses.
- Prevention of IT disruptions - Manage costs by leveraging AI and machine learning for predictive analytics.
- Optimized application performance - Drive AIOps insights to predict service health and application performance issues.
- Intelligent automation - Mitigate risk by leveraging key data and insights for AI-powered runbook automation.
"For years, we have been talking about delivering on the promise of a lights-out data center," said Greg Stock, chairman and CEO of Zenoss. "This partnership with HCL's DRYiCE software is another step in that journey — delivering real value to our customer bases at a time when it's needed the most."
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.