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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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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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

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The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

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