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Raise The Bar with Machine Learning for Improved Customer Service

Holly Simmons

For today's executives, machine learning is the latest term to get hyped before slowly becoming a reality. And in fact, the majority of CIOs have now begun to take advantage of this transformational, labor-saving technology for customer service, IT, and other parts of the organization.

More than two-thirds of CIOs believe that decisions made by machines will be more accurate than human-made decisions

The Global CIO Point of View report compiled by ServiceNow notes that 89 percent of organizations are either in the planning stages or are already taking advantage of machine learning. Nearly 90 percent of the CIOs surveyed anticipate that increasing automation will increase the speed and accuracy of decisions, and more than two-thirds believe that decisions made by machines will be more accurate than human-made decisions.

With digital transformation being a top priority on many corporate agendas, IT and customer service are partnering to bring machine learning to real world use to improve the customer experience, to reduce manual work by customer service agents and field service technicians, and to improve the quality of service.

A new report from Accenture found that front-line customer support functions spend up to 12 percent of their time categorizing, prioritizing, and assigning tickets. And 27 percent are weighed down by having to choose from 100+ assignment groups.

Machine Learning Improves Customer and Agent Experiences

Most customers today prefer to help themselves via self-service ... Machine learning simplifies this process for the customer

Most customers today prefer to help themselves via self-service including filing a case or request online. Machine learning simplifies this process for the customer by reducing the number of categories from which to choose. Additionally, because requests are being automatically assigned, response times are faster and fewer calls are required.

For agents, eliminating manual work opens the door to focusing on more strategic work such as helping customers get more out of the products or services they purchased. Assignment errors are reduced thus eliminating unnecessary escalations and shortening the time to case closure. For companies, machine learning not only reduces costs, but also improves agent engagement and satisfaction.

Removing the Hurdles Democratizes Machine Learning

One of the obstacles CIOs face in bringing machine learning into their organization is the high cost of entry. Taking full advantage of machine learning in-house requires data scientists that are costly and in short supply. Only about one in four CIOs report having the staff to properly execute their machine learning strategy. This requires a rethink of the best way to implement machine learning. How can you take advantage of this technology without hiring an army of data scientists?

The good news is that third-party providers are now able to integrate machine learning models into their applications including customer service or CRM systems. Pre-built approaches enable rapid implementation and the ability to see results in less than a day without the need to staff up.

Something as simple as fewer categories and faster case assignment can have a noticeable impact on customer engagement, agent satisfaction, and the bottom line. IT working in harmony with customer service to take advantage of machine learning opens up a new world of possibilities. The hype is high, the rewards are real, and the time is right for organizations to embrace this technology and experience the benefits for themselves.

The Latest

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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Raise The Bar with Machine Learning for Improved Customer Service

Holly Simmons

For today's executives, machine learning is the latest term to get hyped before slowly becoming a reality. And in fact, the majority of CIOs have now begun to take advantage of this transformational, labor-saving technology for customer service, IT, and other parts of the organization.

More than two-thirds of CIOs believe that decisions made by machines will be more accurate than human-made decisions

The Global CIO Point of View report compiled by ServiceNow notes that 89 percent of organizations are either in the planning stages or are already taking advantage of machine learning. Nearly 90 percent of the CIOs surveyed anticipate that increasing automation will increase the speed and accuracy of decisions, and more than two-thirds believe that decisions made by machines will be more accurate than human-made decisions.

With digital transformation being a top priority on many corporate agendas, IT and customer service are partnering to bring machine learning to real world use to improve the customer experience, to reduce manual work by customer service agents and field service technicians, and to improve the quality of service.

A new report from Accenture found that front-line customer support functions spend up to 12 percent of their time categorizing, prioritizing, and assigning tickets. And 27 percent are weighed down by having to choose from 100+ assignment groups.

Machine Learning Improves Customer and Agent Experiences

Most customers today prefer to help themselves via self-service ... Machine learning simplifies this process for the customer

Most customers today prefer to help themselves via self-service including filing a case or request online. Machine learning simplifies this process for the customer by reducing the number of categories from which to choose. Additionally, because requests are being automatically assigned, response times are faster and fewer calls are required.

For agents, eliminating manual work opens the door to focusing on more strategic work such as helping customers get more out of the products or services they purchased. Assignment errors are reduced thus eliminating unnecessary escalations and shortening the time to case closure. For companies, machine learning not only reduces costs, but also improves agent engagement and satisfaction.

Removing the Hurdles Democratizes Machine Learning

One of the obstacles CIOs face in bringing machine learning into their organization is the high cost of entry. Taking full advantage of machine learning in-house requires data scientists that are costly and in short supply. Only about one in four CIOs report having the staff to properly execute their machine learning strategy. This requires a rethink of the best way to implement machine learning. How can you take advantage of this technology without hiring an army of data scientists?

The good news is that third-party providers are now able to integrate machine learning models into their applications including customer service or CRM systems. Pre-built approaches enable rapid implementation and the ability to see results in less than a day without the need to staff up.

Something as simple as fewer categories and faster case assignment can have a noticeable impact on customer engagement, agent satisfaction, and the bottom line. IT working in harmony with customer service to take advantage of machine learning opens up a new world of possibilities. The hype is high, the rewards are real, and the time is right for organizations to embrace this technology and experience the benefits for themselves.

The Latest

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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...