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Nearly 96% of IT Professionals Believe GenAI Will Boost IT Productivity

Shamus McGillicuddy

When IT leaders started telling Enterprise Management Associates (EMA™) more than a year ago that their personnel were using premium ChatGPT subscriptions to create device configs and automation scripts, we knew the industry was on the verge of a revolution. Given the extreme interest in generative AI (GenAI) and the billions of dollars being invested in the technology,  EMA decided to investigate how enterprise IT organizations are applying the technology to IT operations tasks and processes today.

Artificial intelligence (AI) has been a hot IT industry buzzword for many years, particularly in the context of AIOps (AI for IT operations). AIOps is primarily the application of machine learning and other advanced algorithms to IT telemetry data for event correlation, anomaly detection, problem isolation, root-cause analysis, and other operational use cases. AIOps promised to streamline and automate various aspects of IT management, and it continues to gain momentum in the industry.

More recently, the emergence of ChatGPT from OpenAI kicked interest in AI into overdrive. ChatGPT and the countless competing platforms that followed it to market leverage large language models (LLM) to power generative AI, a technology that can produce new content in response to user prompts.

EMA spoke to many IT professionals who are successfully applying consumer-facing, general-purpose generative AI tools to IT operations tasks. The research aimed to uncover how these technologies can be effectively applied to IT management.

Some of the key findings from my new report, <span style="font-style: italic;">Applying Generative AI to IT Operations</span>, include:

■ Most IT professionals are using both general-purpose tools like ChatGPT and generative AI capabilities from their IT vendors.

■ The top challenges with applying generative AI to IT operations are validating quality of AI outputs, managing data quality, and integrating AI into tools and processes.

■ 93% believe it is at least somewhat important for their IT vendors to offer generative AI capabilities.

■ The two biggest potential benefits of applying generative AI to IT management tasks are the optimization of IT service performance and the improved alignment of IT with the business.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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 ...

Nearly 96% of IT Professionals Believe GenAI Will Boost IT Productivity

Shamus McGillicuddy

When IT leaders started telling Enterprise Management Associates (EMA™) more than a year ago that their personnel were using premium ChatGPT subscriptions to create device configs and automation scripts, we knew the industry was on the verge of a revolution. Given the extreme interest in generative AI (GenAI) and the billions of dollars being invested in the technology,  EMA decided to investigate how enterprise IT organizations are applying the technology to IT operations tasks and processes today.

Artificial intelligence (AI) has been a hot IT industry buzzword for many years, particularly in the context of AIOps (AI for IT operations). AIOps is primarily the application of machine learning and other advanced algorithms to IT telemetry data for event correlation, anomaly detection, problem isolation, root-cause analysis, and other operational use cases. AIOps promised to streamline and automate various aspects of IT management, and it continues to gain momentum in the industry.

More recently, the emergence of ChatGPT from OpenAI kicked interest in AI into overdrive. ChatGPT and the countless competing platforms that followed it to market leverage large language models (LLM) to power generative AI, a technology that can produce new content in response to user prompts.

EMA spoke to many IT professionals who are successfully applying consumer-facing, general-purpose generative AI tools to IT operations tasks. The research aimed to uncover how these technologies can be effectively applied to IT management.

Some of the key findings from my new report, <span style="font-style: italic;">Applying Generative AI to IT Operations</span>, include:

■ Most IT professionals are using both general-purpose tools like ChatGPT and generative AI capabilities from their IT vendors.

■ The top challenges with applying generative AI to IT operations are validating quality of AI outputs, managing data quality, and integrating AI into tools and processes.

■ 93% believe it is at least somewhat important for their IT vendors to offer generative AI capabilities.

■ The two biggest potential benefits of applying generative AI to IT management tasks are the optimization of IT service performance and the improved alignment of IT with the business.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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 ...