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Impact of the Pandemic on ITSM Teams

Organizations are grappling with a new set of problems that were not previously deemed the top priorities, according to The State of ITSM Two Years Into the COVID-19 Pandemic, a new survey conducted by ManageEngine.
The survey found: ■ With three-fifths of the workforce now working in a hybrid mode, managing IT assets (46%) and communication and collaboration (41%) have emerged as the biggest challenges. ■ Both jumped to the top spots, registering a positive difference of 11% and 7%, respectively, when compared to the 2020 figures. ■ The most significant shift in reported challenges was a drop from 36% to 22% for securing company and client data in a distributed network. This change is likely the result of the proactive efforts of IT teams to ensure remote working risks were minimized. "The survey clearly reveals that traditional IT needs to transform itself in the post-pandemic world to cater to the new realities in the workplace," said Kumaravel Ramakrishnan, Evangelist at ManageEngine. "Self-organizing teams, high-velocity workflows and a digital-first approach to customer experience are the hallmarks of new age, democratized IT." Other key findings from the report include: ■ Employees are better equipped: Compared to the beginning of the pandemic, an additional 47% of organizations are now providing mobile assets to employees. ■ IT teams see their value rise: 52% of respondents think IT is now viewed and treated better because of the pandemic, and another 14% think IT has always been highly regarded. ■ BYOD policies are still absent: Two years after workplaces were totally disrupted, 40% of organizations still do not have a BYOD policy. ■ User experience falls short: 34% of organizations still do not offer users self-help capabilities, and 52% do not have chatbots. "Organizations worldwide learned invaluable lessons from the pandemic, including what’s most important to them and their end users, the importance of IT to business operations and the changes needed to meet the needs of a hybrid workforce," Ramakrishnan added. "ITSM teams played a critical role in ensuring that business operations continued during the pandemic, from overseeing BYOD policies and the provision of mobile assets to implementing self-service features and chatbots, investing more in business continuity planning and offering IT service delivery and support."

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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.

Impact of the Pandemic on ITSM Teams

Organizations are grappling with a new set of problems that were not previously deemed the top priorities, according to The State of ITSM Two Years Into the COVID-19 Pandemic, a new survey conducted by ManageEngine.
The survey found: ■ With three-fifths of the workforce now working in a hybrid mode, managing IT assets (46%) and communication and collaboration (41%) have emerged as the biggest challenges. ■ Both jumped to the top spots, registering a positive difference of 11% and 7%, respectively, when compared to the 2020 figures. ■ The most significant shift in reported challenges was a drop from 36% to 22% for securing company and client data in a distributed network. This change is likely the result of the proactive efforts of IT teams to ensure remote working risks were minimized. "The survey clearly reveals that traditional IT needs to transform itself in the post-pandemic world to cater to the new realities in the workplace," said Kumaravel Ramakrishnan, Evangelist at ManageEngine. "Self-organizing teams, high-velocity workflows and a digital-first approach to customer experience are the hallmarks of new age, democratized IT." Other key findings from the report include: ■ Employees are better equipped: Compared to the beginning of the pandemic, an additional 47% of organizations are now providing mobile assets to employees. ■ IT teams see their value rise: 52% of respondents think IT is now viewed and treated better because of the pandemic, and another 14% think IT has always been highly regarded. ■ BYOD policies are still absent: Two years after workplaces were totally disrupted, 40% of organizations still do not have a BYOD policy. ■ User experience falls short: 34% of organizations still do not offer users self-help capabilities, and 52% do not have chatbots. "Organizations worldwide learned invaluable lessons from the pandemic, including what’s most important to them and their end users, the importance of IT to business operations and the changes needed to meet the needs of a hybrid workforce," Ramakrishnan added. "ITSM teams played a critical role in ensuring that business operations continued during the pandemic, from overseeing BYOD policies and the provision of mobile assets to implementing self-service features and chatbots, investing more in business continuity planning and offering IT service delivery and support."

Hot Topics

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.