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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

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...

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

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...