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Gartner: Nearly Half of Customer Service Reps Avoid Adopting New Technologies

Access to Legacy Systems and Tools Serves as the Primary Barrier to Adoption

Nearly half (45%) of customer service reps (CSRs) avoid adopting new technologies by relying on legacy systems and tools, according to a survey from Gartner, Inc.

A Gartner online survey of 888 CSRs and support specialists from August 2022 revealed access to legacy systems is the main barrier to new technology adoption.

"Many leaders believe that certain groups are less likely to adopt new technologies, such as reps who are older or work from home," said Melissa Fletcher, Sr Principal, Research in the Gartner Customer Service and Support practice. "However, the survey shows that these groups are not more likely to resist new technology, and what matters instead is reps’ access to legacy systems and tools."

Reps who have consistent access to outdated tools use them 20% more frequently than reps who have less access to them. Half report still having access to legacy systems and tools seven to 12 months after a new technology rollout, given the difficulty that comes with removing such.

"Shutting down old systems is the single most impactful action that a leader can take to encourage rep adoption of new technologies, but it shouldn’t happen all at once," said Fletcher.

Customer service and support leaders can take three approaches to ease into shutting down legacy systems:

1. Grouped features: Assign features to groups based on the screens or systems they are in, and then remove rep access to these groups one phase at a time.

2. Login elimination: Remove direct login to the legacy system, and only allow legacy login for features not yet implemented.

3. Designated user groups: Assign a small group of reps who will have permission to perform actions in the legacy system, while removing access for the remaining reps.

"Prior to engaging in any shutdown strategy, customer service leaders should meet with key stakeholders in IT as well as front line managers to set expectations around the transition period and the issues that are likely to arise," said Fletcher. "Cross-functional teams can better assess causes of reduced performance such as system bugs or user error and develop short-term workarounds to maintain productivity while addressing these issues."

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Gartner: Nearly Half of Customer Service Reps Avoid Adopting New Technologies

Access to Legacy Systems and Tools Serves as the Primary Barrier to Adoption

Nearly half (45%) of customer service reps (CSRs) avoid adopting new technologies by relying on legacy systems and tools, according to a survey from Gartner, Inc.

A Gartner online survey of 888 CSRs and support specialists from August 2022 revealed access to legacy systems is the main barrier to new technology adoption.

"Many leaders believe that certain groups are less likely to adopt new technologies, such as reps who are older or work from home," said Melissa Fletcher, Sr Principal, Research in the Gartner Customer Service and Support practice. "However, the survey shows that these groups are not more likely to resist new technology, and what matters instead is reps’ access to legacy systems and tools."

Reps who have consistent access to outdated tools use them 20% more frequently than reps who have less access to them. Half report still having access to legacy systems and tools seven to 12 months after a new technology rollout, given the difficulty that comes with removing such.

"Shutting down old systems is the single most impactful action that a leader can take to encourage rep adoption of new technologies, but it shouldn’t happen all at once," said Fletcher.

Customer service and support leaders can take three approaches to ease into shutting down legacy systems:

1. Grouped features: Assign features to groups based on the screens or systems they are in, and then remove rep access to these groups one phase at a time.

2. Login elimination: Remove direct login to the legacy system, and only allow legacy login for features not yet implemented.

3. Designated user groups: Assign a small group of reps who will have permission to perform actions in the legacy system, while removing access for the remaining reps.

"Prior to engaging in any shutdown strategy, customer service leaders should meet with key stakeholders in IT as well as front line managers to set expectations around the transition period and the issues that are likely to arise," said Fletcher. "Cross-functional teams can better assess causes of reduced performance such as system bugs or user error and develop short-term workarounds to maintain productivity while addressing these issues."

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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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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 ...