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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.

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...

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

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...