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The Road to Automation in IT Operations-Part 1

Anirban Chatterjee

The buzz around automation continues to grow, in every industry, sector and vertical — and for good reason. In IT Ops, the impact can be instant and huge, with improvement measured in the tens or even hundreds of percent as automation enables even a very lean team to operate at an outsized level. Automation can facilitate faster production, the creation of new products and the delivery of more services — and all in a stable, predictable, scalable way. And today, with AI and Machine Learning in the mix (aka AIOps), the possibilities and potential for automation are almost limitless.

So, how do you ensure your journey to automated IT Ops is streamlined and effective, and not just a buzzword?


Here are 4 golden rules to help you do just that:

1. Set good standards

2. Reduce complexity

3. Define and simplify processes

4. Automate wisely

1. Set good standards — powerful and specific

In general, standards can be thought of as specifications and procedures that have been designed to make sure the materials, products, methods, or services people use every day are reliable. Standardization is particularly relevant to IT automation, which is itself a computerized implementation of a standardized process. Put bluntly, computers are dumb and lack creativity. They do only what they're told, so we have to know exactly what we want them to do before they can do it. This is why only standardized processes can be automated successfully.

The key to optimizing automation is to create powerful standards that enable you to get the most out of your systems. These standards define how your systems communicate with each other, transfer information, look at and analyze data, etc.

A good example is the standardization of naming conventions, which, in the enterprise IT world, is always something of a challenge. If there is no standard in place regarding what one system is sending across, then the receiving system will just be guessing what to do with information it is getting. And, if it can't get the information it needs out of the data stream it receives, then a separate, manually-updated lookup table may be required, compromising the efficacy of the automation you wish to set up.


Consider, for example, the host naming standards illustrated in the image above. The more the naming standard is designed with your needs in mind, the easier it is for your systems to analyze the data, and pull out the critical information needed, such as the affected frame, geographical location, application served, etc. If used consistently across the organization, this standard becomes a solid bedrock for the assumptions that tools downstream can make about the data, and for the automation processes you put in place on top — for example, to issue automated alerts, response and remediation actions when a server has an issue.

2. Reduce complexity - keep only what you need

A useful rule of thumb is that you should be able to sketch out or explain what your IT environment does and how it functions in around a minute

Complexity is inherent in the dynamic modern-day IT environments in which businesses operate, but that doesn't mean that we should not try to reduce it wherever possible. A useful rule of thumb is that you should be able to sketch out or explain what your IT environment does and how it functions in around a minute. If you can't, automation may just exacerbate complexity. So, when you contemplate automation, you should see it as an opportunity to take a step back, recognize areas of unnecessary complexity and identify what can be done to reduce it. To do this properly, you need to make sure you talk to the people that are doing the work on the ground to find out about their actual experience.

In the diagram below, we see an example of how complexity can be dealt with by moving to a SaaS environment. Operating many systems on-premise that need to be managed and maintained comes at a huge cost — both financially and in terms of efficiency. By moving to a SaaS environment, rather than having to maintain hardware, the operating system, bug fixes and patches, network bandwidth, firewalls, load balancers and the on-prem application software itself, you just need to take care of one thing only: the configuration of your SaaS apps!


Tool rationalization is another good example. By reducing the number of tools you work with, you reduce the complexity of your operations.

Now, you can focus your automation efforts on taking care of simplified tasks, saving time and reducing overhead in the long term.

Go to: The Road to Automation in IT Operations - Part 2

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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 ...

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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The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The Road to Automation in IT Operations-Part 1

Anirban Chatterjee

The buzz around automation continues to grow, in every industry, sector and vertical — and for good reason. In IT Ops, the impact can be instant and huge, with improvement measured in the tens or even hundreds of percent as automation enables even a very lean team to operate at an outsized level. Automation can facilitate faster production, the creation of new products and the delivery of more services — and all in a stable, predictable, scalable way. And today, with AI and Machine Learning in the mix (aka AIOps), the possibilities and potential for automation are almost limitless.

So, how do you ensure your journey to automated IT Ops is streamlined and effective, and not just a buzzword?


Here are 4 golden rules to help you do just that:

1. Set good standards

2. Reduce complexity

3. Define and simplify processes

4. Automate wisely

1. Set good standards — powerful and specific

In general, standards can be thought of as specifications and procedures that have been designed to make sure the materials, products, methods, or services people use every day are reliable. Standardization is particularly relevant to IT automation, which is itself a computerized implementation of a standardized process. Put bluntly, computers are dumb and lack creativity. They do only what they're told, so we have to know exactly what we want them to do before they can do it. This is why only standardized processes can be automated successfully.

The key to optimizing automation is to create powerful standards that enable you to get the most out of your systems. These standards define how your systems communicate with each other, transfer information, look at and analyze data, etc.

A good example is the standardization of naming conventions, which, in the enterprise IT world, is always something of a challenge. If there is no standard in place regarding what one system is sending across, then the receiving system will just be guessing what to do with information it is getting. And, if it can't get the information it needs out of the data stream it receives, then a separate, manually-updated lookup table may be required, compromising the efficacy of the automation you wish to set up.


Consider, for example, the host naming standards illustrated in the image above. The more the naming standard is designed with your needs in mind, the easier it is for your systems to analyze the data, and pull out the critical information needed, such as the affected frame, geographical location, application served, etc. If used consistently across the organization, this standard becomes a solid bedrock for the assumptions that tools downstream can make about the data, and for the automation processes you put in place on top — for example, to issue automated alerts, response and remediation actions when a server has an issue.

2. Reduce complexity - keep only what you need

A useful rule of thumb is that you should be able to sketch out or explain what your IT environment does and how it functions in around a minute

Complexity is inherent in the dynamic modern-day IT environments in which businesses operate, but that doesn't mean that we should not try to reduce it wherever possible. A useful rule of thumb is that you should be able to sketch out or explain what your IT environment does and how it functions in around a minute. If you can't, automation may just exacerbate complexity. So, when you contemplate automation, you should see it as an opportunity to take a step back, recognize areas of unnecessary complexity and identify what can be done to reduce it. To do this properly, you need to make sure you talk to the people that are doing the work on the ground to find out about their actual experience.

In the diagram below, we see an example of how complexity can be dealt with by moving to a SaaS environment. Operating many systems on-premise that need to be managed and maintained comes at a huge cost — both financially and in terms of efficiency. By moving to a SaaS environment, rather than having to maintain hardware, the operating system, bug fixes and patches, network bandwidth, firewalls, load balancers and the on-prem application software itself, you just need to take care of one thing only: the configuration of your SaaS apps!


Tool rationalization is another good example. By reducing the number of tools you work with, you reduce the complexity of your operations.

Now, you can focus your automation efforts on taking care of simplified tasks, saving time and reducing overhead in the long term.

Go to: The Road to Automation in IT Operations - Part 2

Hot Topics

The Latest

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 ...

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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

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 ...

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 ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.