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Creating Agility with DevOps and AI-Driven ITSM

Akhil Sahai

There are some IT organizations that are using DevOps methodology but are wary of getting bogged down in ITSM procedures. But without at least some ITSM controls in place, organizations lose their focus on systematic customer engagement, making it harder for them to scale.

Other IT organizations believe that they're too large, complex and/or process-driven to adopt DevOps. Perhaps team members would like to give it a try but fear that their culture is too old-school and would not allow the disruption that DevOps usually brings. However, process is made for users, not the other way around, and an over-focus on process can keep customers from receiving the experience they need.

So then, DevOps and IT service management must not be mutually exclusive anymore. In fact, combining the two offers organizations ways to scale the enterprise and create agility while maintaining control of IT. They gain both speed and process controls. IT Service Management has to be re-imagined for that to happen successfully. By using technologies like AI/ML, ITSM has been re-imagined so much so that DevOps and ITSM are synergistic now. For instance, organizations can track and resolve incidents and create service requests and have them fulfilled in DevOps environments with AI-driven service management in minutes.

AI-Driven ITSM and DevOps Are Colleagues, Not Enemies

With the advent of AI, many such scenarios are made possible. Organizations for example can deploy an AI-driven digital agent available 24/7 to developers to use across multiple channels. Developers can create service requests for sandboxed environments and have them stood up or taken away and add additional capacity to existing development environments, in minutes. The digital agent would understand and classify the intent of requests using AI and resolve these requests automatically without human intervention. If there are approvals involved, such a digital agent will be able to seek approvals and still automate these deployments thus taking significant load off operations teams.

Similarly, incidents may be tracked in the operations environment, service tickets created and may be resolved by using AI-driven automation in matter of minutes. This would help bring much-needed agility in DevOps environments while following the best of IT Service Management practices.

DevOps doesn't eliminate the need for controls and data. Controls still need to be maintained and risks still need to be managed. AI-driven ITSM for DevOps brings new ways to achieve speed and control while driving value through the IT channel and supporting existing ITSM and DevOps initiatives within a company.

A More Perfect Union

DevOps and ITSM are not an either/or proposition. Instead, they need to be integrated so that the best aspects of each yield a result that is greater than the sum of their parts. Organizations will be able to scale quickly while maintaining process controls. Integration tools make this easier, as do AI-based digital agents. Essentially, there's never been a better time to bring AI-driven ITSM and DevOps together. Doing so will yield greater agility, speed, control and growth potential.

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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 ...

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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 ...

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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 ...

Creating Agility with DevOps and AI-Driven ITSM

Akhil Sahai

There are some IT organizations that are using DevOps methodology but are wary of getting bogged down in ITSM procedures. But without at least some ITSM controls in place, organizations lose their focus on systematic customer engagement, making it harder for them to scale.

Other IT organizations believe that they're too large, complex and/or process-driven to adopt DevOps. Perhaps team members would like to give it a try but fear that their culture is too old-school and would not allow the disruption that DevOps usually brings. However, process is made for users, not the other way around, and an over-focus on process can keep customers from receiving the experience they need.

So then, DevOps and IT service management must not be mutually exclusive anymore. In fact, combining the two offers organizations ways to scale the enterprise and create agility while maintaining control of IT. They gain both speed and process controls. IT Service Management has to be re-imagined for that to happen successfully. By using technologies like AI/ML, ITSM has been re-imagined so much so that DevOps and ITSM are synergistic now. For instance, organizations can track and resolve incidents and create service requests and have them fulfilled in DevOps environments with AI-driven service management in minutes.

AI-Driven ITSM and DevOps Are Colleagues, Not Enemies

With the advent of AI, many such scenarios are made possible. Organizations for example can deploy an AI-driven digital agent available 24/7 to developers to use across multiple channels. Developers can create service requests for sandboxed environments and have them stood up or taken away and add additional capacity to existing development environments, in minutes. The digital agent would understand and classify the intent of requests using AI and resolve these requests automatically without human intervention. If there are approvals involved, such a digital agent will be able to seek approvals and still automate these deployments thus taking significant load off operations teams.

Similarly, incidents may be tracked in the operations environment, service tickets created and may be resolved by using AI-driven automation in matter of minutes. This would help bring much-needed agility in DevOps environments while following the best of IT Service Management practices.

DevOps doesn't eliminate the need for controls and data. Controls still need to be maintained and risks still need to be managed. AI-driven ITSM for DevOps brings new ways to achieve speed and control while driving value through the IT channel and supporting existing ITSM and DevOps initiatives within a company.

A More Perfect Union

DevOps and ITSM are not an either/or proposition. Instead, they need to be integrated so that the best aspects of each yield a result that is greater than the sum of their parts. Organizations will be able to scale quickly while maintaining process controls. Integration tools make this easier, as do AI-based digital agents. Essentially, there's never been a better time to bring AI-driven ITSM and DevOps together. Doing so will yield greater agility, speed, control and growth potential.

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 ...