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.

Related Links

Hot Topics

The Latest

Exploring AI Observability – Part 5: Roles

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Exploring AI Observability – Part 4: Challenges

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Exploring AI Observability – Part 3: The Current State of AI Observability

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

Exploring AI Observability – Part 2: Traditional vs. AI Observability

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

Exploring AI Observability – Part 1: Why It Matters

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

AgenticOps Scaling Quickly in the Enterprise

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

Why Better Technology Won't Fix a Broken Workflow

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

What If AI's Biggest Competitive Advantage Isn't the Model?

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

Why Going All-In on Cloud No Longer Works in the Age of AI

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Visibility Is Not a Strategy - What Matters Is What Happens Next

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

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.

Related Links

Hot Topics

The Latest

Exploring AI Observability – Part 5: Roles

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Exploring AI Observability – Part 4: Challenges

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Exploring AI Observability – Part 3: The Current State of AI Observability

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

Exploring AI Observability – Part 2: Traditional vs. AI Observability

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

Exploring AI Observability – Part 1: Why It Matters

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

AgenticOps Scaling Quickly in the Enterprise

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

Why Better Technology Won't Fix a Broken Workflow

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

What If AI's Biggest Competitive Advantage Isn't the Model?

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

Why Going All-In on Cloud No Longer Works in the Age of AI

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Visibility Is Not a Strategy - What Matters Is What Happens Next

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...