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FrontRange Adds New IT Service Catalog Functionality for HEAT 2014.1 Release

FrontRange announced the addition of several new critical capabilities to the IT service catalog that is included in its HEAT 2014.1 Service Management solution.

The Service Catalog updates include key updates to the user interface that allow for greater ease-of-use, additional reporting functionality and enhanced workflow integration.

Enhancements to HEAT Service Catalog functionality include:

- Improved usability and user interface: addition of tool tips for improved self-help, dynamic field capabilities based on customer input providing better context and navigation and single or multi-pages free-form design to meet customer specific layout needs.

- Enhanced reporting: full transactional and BI reporting capabilities for higher level of performance monitoring and measurement.

- Enhanced Workflow integration: dynamic workflow builder improvements with drag and drop capabilities and ability to call external systems for service fulfillments.

- Multi-currency support: broader multi-currency support allows users to define pricelists with one-time or recurring prices in local currencies based on the country of the employee.

- Pricelist support: allows users to define cost and price for each item and define pricelist per select option within a request offering.

- Price views per employees/cost center/business unit: creates possibility to show employees own costs (or prices) as one-time and recurring charges and transfer these charges to an internal accounting system.

HEAT is a Service Management solution that incorporates both on-premise and cloud applications on a unified platform. This approach delivers a complete service management solution from a single vendor, on a single platform, that gives customers the flexibility to leverage multi-tenant cloud or on-premise deployment options—or a combination of the two—to support their unique and evolving business needs.

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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 ...

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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 ...

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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 ...

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The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

FrontRange Adds New IT Service Catalog Functionality for HEAT 2014.1 Release

FrontRange announced the addition of several new critical capabilities to the IT service catalog that is included in its HEAT 2014.1 Service Management solution.

The Service Catalog updates include key updates to the user interface that allow for greater ease-of-use, additional reporting functionality and enhanced workflow integration.

Enhancements to HEAT Service Catalog functionality include:

- Improved usability and user interface: addition of tool tips for improved self-help, dynamic field capabilities based on customer input providing better context and navigation and single or multi-pages free-form design to meet customer specific layout needs.

- Enhanced reporting: full transactional and BI reporting capabilities for higher level of performance monitoring and measurement.

- Enhanced Workflow integration: dynamic workflow builder improvements with drag and drop capabilities and ability to call external systems for service fulfillments.

- Multi-currency support: broader multi-currency support allows users to define pricelists with one-time or recurring prices in local currencies based on the country of the employee.

- Pricelist support: allows users to define cost and price for each item and define pricelist per select option within a request offering.

- Price views per employees/cost center/business unit: creates possibility to show employees own costs (or prices) as one-time and recurring charges and transfer these charges to an internal accounting system.

HEAT is a Service Management solution that incorporates both on-premise and cloud applications on a unified platform. This approach delivers a complete service management solution from a single vendor, on a single platform, that gives customers the flexibility to leverage multi-tenant cloud or on-premise deployment options—or a combination of the two—to support their unique and evolving business needs.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...