Skip to main content

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 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

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 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...