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ITinvolve Launches Social IT Maturity Assessment Tool

ITinvolve announced the official launch of its Social IT Maturity Assessment tool, designed to help IT professionals gauge and benchmark their current social IT maturity.

Consolidated and anonymous results of the assessment will be published in a forthcoming Social IT Maturity Index in the first quarter of 2013.

Research from McKinsey & Company reveals social technologies have the potential to raise the productivity of high-skill knowledge workers by 20 to 25 percent.

"Social IT has significant potential to improve the way IT professionals conduct day-to-day and long-term tactical and strategic operations. From more effective capturing of human knowledge to streamlined in-context collaboration and improved decision making, we anticipate more widespread adoption of social IT tools well into 2013," said ITinvolve VP of Marketing Matthew Selheimer. "Our Social IT Maturity Assessment is the first survey of its kind to provide a way for IT professionals to assess their current social IT maturity level and receive practical guidance on how to advance to higher level of maturity."

ITinvolve's social IT maturity model spotlights an evolution of social IT in four levels:

Level 1: Social Exploration: Learning and discovering how social tools can improve IT support.

Level 2: Social Add-ons: Improving user intimacy and end user satisfaction.

Level 3: Social Embedding: Improving key performance indicators through social knowledge management and social process enhancements.

Level 4: Social Driven: Fostering a self-sustaining community in which social capabilities drive process excellence and improvement.

"ITinvolve's social object model and the social knowledge management system is huge and something we've really not seen before," said Pink Elephant Executive Vice President George Spalding. "By taking this approach, IT organizations can break free of the bounds of traditional ITIL processes, and I encourage IT professionals to open their minds to understanding what ITinvolve is offering with its social object model."

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 ...

ITinvolve Launches Social IT Maturity Assessment Tool

ITinvolve announced the official launch of its Social IT Maturity Assessment tool, designed to help IT professionals gauge and benchmark their current social IT maturity.

Consolidated and anonymous results of the assessment will be published in a forthcoming Social IT Maturity Index in the first quarter of 2013.

Research from McKinsey & Company reveals social technologies have the potential to raise the productivity of high-skill knowledge workers by 20 to 25 percent.

"Social IT has significant potential to improve the way IT professionals conduct day-to-day and long-term tactical and strategic operations. From more effective capturing of human knowledge to streamlined in-context collaboration and improved decision making, we anticipate more widespread adoption of social IT tools well into 2013," said ITinvolve VP of Marketing Matthew Selheimer. "Our Social IT Maturity Assessment is the first survey of its kind to provide a way for IT professionals to assess their current social IT maturity level and receive practical guidance on how to advance to higher level of maturity."

ITinvolve's social IT maturity model spotlights an evolution of social IT in four levels:

Level 1: Social Exploration: Learning and discovering how social tools can improve IT support.

Level 2: Social Add-ons: Improving user intimacy and end user satisfaction.

Level 3: Social Embedding: Improving key performance indicators through social knowledge management and social process enhancements.

Level 4: Social Driven: Fostering a self-sustaining community in which social capabilities drive process excellence and improvement.

"ITinvolve's social object model and the social knowledge management system is huge and something we've really not seen before," said Pink Elephant Executive Vice President George Spalding. "By taking this approach, IT organizations can break free of the bounds of traditional ITIL processes, and I encourage IT professionals to open their minds to understanding what ITinvolve is offering with its social object model."

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 ...