The Real Story of AI vs. IT Automation – Part 2
IT Automation and AI Revisited
IT Automation is the use of software tools and systems to automate repeatable tasks and processes in your IT environment, replacing manual methods that often require user interaction. At one point, it was faster to point and click with a mouse to complete tasks. However, in most instances now, especially when working with cloud or hybrid environments, it takes longer to point, click, and wait for the next screen than it does to run it via IT automation…and the benefit is your IT staff now has the time to focus on the bigger issues.
Artificial Intelligence (AI) refers to the capability of computer technology to accurately predict what may come next and trigger tasks typically performed by humans, including reasoning and logic, but it is based on the analysis of a large set of data used to determine common issues and responses and to continue to learn based on the results of these tasks. AI includes a variety of technologies, such as machine learning and natural language processing, that help it perform tasks that used to require human decision-making processes.
So, which comes first?
By implementing IT Automation first, you can build the data resource and history necessary for AI to be most effective. This data set is specific to your environment and your needs. When I first started working with IT Automation, it was all about scripting the basics-mapping drives, installing apps, and setting up printers. Then came PowerShell, and suddenly we could automate just about anything at the command line. Now we have graphical tools for a no code/low code method of creating runbooks.
Today, IT Automation is a powerhouse. If a task is repeatable, it can be automated. Creating user accounts, managing servers, moving files, tweaking registry keys, you name it. The key is building in error handling and logic. For example, if a script is supposed to add a user to a group but the name is entered backwards, the process might run but fail silently. That’s why I always include checks: Did the change happen? If not, why?
This kind of automation doesn’t just save time – it creates consistency. And that consistency is gold when it comes to AI.
AI thrives on data consistency; AI cannot operate with poor or patchy data.
Why AI needs consistent clean data
Here’s why: AI needs data. Lots of it. And not just any data – clean, structured, relevant data.
One of the most talked-about use cases for AI is automatically recommending fault-resolution steps when new Incident Tickets are raised in your ITSM system. To do this, AI needs to analyse closed tickets in the ITSM system and review the resolution notes to make its suggestions.
This seems straightforward until you realize the typical human-entered resolution note says fixed, done, fault not found, or just a full stop! Your average IT engineer hates writing, so they make a very poor data source.
IT Automation, on the other hand, can generate that data by logging every action, every success, every failure. It becomes the foundation AI can learn from.
Example: Many of our customers follow our best practice of using a Persistent Data Store (PDS). A PDS is a separate database that tracks and records all events being monitored and the steps taken to follow an automated process to address them. This is a prime example of a pristine data set for AI to analyze, identify trends and possible root causes, and even recommend solutions that could then be implemented by your IT Automation runbooks.
Think of AI as the analyst and automation as the executor. AI might say, “Hey, I’ve noticed this service fails every Thursday at 2 a.m.” Automation can then be triggered to restart the service proactively, log the result, and report back. AI learns from that outcome and refines its predictions.
AI can learn from this specific data generated by your environment. This prevents AI from generating overly generic recommendations that could go in the wrong direction and not apply to your exact situation. In our experience, if you implement IT Automation first, it can capture the right data for AI to analyze, making it able to produce results that are more efficient and effective for you.
This particular data set and history also gives you the ability to build a framework/structure to carefully check what AI is recommending and prevent bad outcomes. This is the control mechanism for AI – a logic in the process that may check for a previous level of success, require human approval, and ultimately provide you with the best of both worlds working together.
So, this is just the beginning of our discussion on AI and IT Automation. We will continue to publish more articles, set up more live sessions, and provide you with guidance, insight, and information we see happening in the industry to help you be the most successful in your journey with AI and IT Automation.
If you are interested in moving forward with AI and need assistance on how to get started with it in your IT Operations environment using IT Automation as a foundation, please contact us at: info@kelverion.com
About Kelverion
Experts in Cloud, On-Premise, and Hybrid automation, Kelverion provides solutions and integrations that remove the manual process tying up IT staff, transforming the productivity, efficiency, and supportability of IT service automation. Our products utilize and enhance the power of Microsoft Azure Automation and System Center Orchestrator.
Working closely alongside Microsoft, we have developed our integrations and automation solutions to help bridge the gap between Microsoft’s automation platforms and third-party systems, in the process building key alliance partnerships with multiple vendors to ensure our products are fully certified.
Since 2010, Kelverion has delivered hundreds of Microsoft Automation projects, with offices in the US, UK, and Canada. Through this, we can offer and support products and professional services engagements to enterprise-level organizations globally.
Kelverion – Simplify IT Automation
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