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The Road to Automation in IT Operations-Part 2

Anirban Chatterjee

How do you ensure your journey to automated IT Ops is streamlined and effective, and not just a buzzword? The Road to Automation in IT Operations - Part 1 covered golden rules #1 and #2. Part 2 starts with #3.

3. Define and simplify processes - more intelligence, fewer steps

Similar to the previous point, simply automating complicated or bad processes can lead to more complication and overhead. To avoid this unfortunate outcome, you need to begin by identifying the simplest route between your available input and the goal output, free from the baggage of past decisions and tradeoffs. This fresh assessment will direct exactly what your automation will be doing for you in the future. It is here also that all the work you've done in the previous two steps — standardizing and reducing complexity — really pays off, since it allows you to simplify your processes even more.

By defining the processes that are important to your IT Ops team or workflows, you can make sure that they are simple, efficient and robust. Questions to ask yourself as you do this include:

Is this process actually making work easier and more efficient, or is it causing more problems than it solves?

Is there a step along the way that is taking too long?

What can we do to clear any bottlenecks?

Is there any part of our processes that is being unnecessarily duplicated and can be eliminated (as in the diagram below)?

What intelligence can we put up front, to minimize the number of follow-up steps required?


This stage is absolutely critical because, as automation scales up our operations, it doesn't just multiply what we have been doing well with our manual processes; it also multiplies any problems, glitches or defects. So, it's best to head them off at the pass.

4. Automate wisely - choose the tools that best fit your needs

Our last guiding principle concerns automation itself. This is where we realize the true value of the previous three principles — in short, it's where the magic happens. So, take your time to wisely select the tools you use to implement your automation.

As much as we try to keep everything simple, IT environments will alway remain noisy, complex and fast moving. The key to developing resilient automation is to implement technologies that enable us to deal with these inevitabilities as best we can — and it is here that AIOps shines.

Understand what goals you aim to achieve with your automation — and ask what the AIOps platforms you are considering can do for you from that perspective:

Can they help you with your naming conventions?

Are they suited to working both on-prem and in the cloud?

Can they easily integrate with your existing tools?

Will their communication capabilities adequately support the processes you are aiming to put in place?

Can they add the information to alerts through enrichment?

Do their AI and ML provide you with adequate flexibility and transparency to implement your tribal knowledge?

These and other questions are important to make sure you are properly equipped as you begin your automation journey. And, if you've done a good job in instrumenting, you'll get actionable data from the automated process as it runs, and over time you'll identify areas for your team to further improve and simplify its flow.

Automation is the future of IT Ops, and not just because it makes your IT Ops workflows and teams more efficient. By taking care of mundane, repetitive tasks, it also elevates the human role, freeing up staff to do the more interesting, innovative parts of their job that can really drive your business forward. Following these four guiding principles, will help you safely navigate your automation process.

Hot Topics

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

The Road to Automation in IT Operations-Part 2

Anirban Chatterjee

How do you ensure your journey to automated IT Ops is streamlined and effective, and not just a buzzword? The Road to Automation in IT Operations - Part 1 covered golden rules #1 and #2. Part 2 starts with #3.

3. Define and simplify processes - more intelligence, fewer steps

Similar to the previous point, simply automating complicated or bad processes can lead to more complication and overhead. To avoid this unfortunate outcome, you need to begin by identifying the simplest route between your available input and the goal output, free from the baggage of past decisions and tradeoffs. This fresh assessment will direct exactly what your automation will be doing for you in the future. It is here also that all the work you've done in the previous two steps — standardizing and reducing complexity — really pays off, since it allows you to simplify your processes even more.

By defining the processes that are important to your IT Ops team or workflows, you can make sure that they are simple, efficient and robust. Questions to ask yourself as you do this include:

Is this process actually making work easier and more efficient, or is it causing more problems than it solves?

Is there a step along the way that is taking too long?

What can we do to clear any bottlenecks?

Is there any part of our processes that is being unnecessarily duplicated and can be eliminated (as in the diagram below)?

What intelligence can we put up front, to minimize the number of follow-up steps required?


This stage is absolutely critical because, as automation scales up our operations, it doesn't just multiply what we have been doing well with our manual processes; it also multiplies any problems, glitches or defects. So, it's best to head them off at the pass.

4. Automate wisely - choose the tools that best fit your needs

Our last guiding principle concerns automation itself. This is where we realize the true value of the previous three principles — in short, it's where the magic happens. So, take your time to wisely select the tools you use to implement your automation.

As much as we try to keep everything simple, IT environments will alway remain noisy, complex and fast moving. The key to developing resilient automation is to implement technologies that enable us to deal with these inevitabilities as best we can — and it is here that AIOps shines.

Understand what goals you aim to achieve with your automation — and ask what the AIOps platforms you are considering can do for you from that perspective:

Can they help you with your naming conventions?

Are they suited to working both on-prem and in the cloud?

Can they easily integrate with your existing tools?

Will their communication capabilities adequately support the processes you are aiming to put in place?

Can they add the information to alerts through enrichment?

Do their AI and ML provide you with adequate flexibility and transparency to implement your tribal knowledge?

These and other questions are important to make sure you are properly equipped as you begin your automation journey. And, if you've done a good job in instrumenting, you'll get actionable data from the automated process as it runs, and over time you'll identify areas for your team to further improve and simplify its flow.

Automation is the future of IT Ops, and not just because it makes your IT Ops workflows and teams more efficient. By taking care of mundane, repetitive tasks, it also elevates the human role, freeing up staff to do the more interesting, innovative parts of their job that can really drive your business forward. Following these four guiding principles, will help you safely navigate your automation process.

Hot Topics

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...