The Real Story of AI vs. IT Automation
How did we first hear about AI?
We have noticed that many people are uncertain where the AI and IT Automation lines are drawn. After a recent trip to Experts Live! In New York, I realized it was because the terms used to discuss both topics are almost interchangeable. They discuss building an AI agent in the same way we discuss creating a runbook. So, I wanted to start a conversation to begin differentiating between the two topics and, at the same time, show how they can work together.
What do we know about AI, and how is it different from IT Automation? Let’s start with possibly the first place we saw AI in action…and that would be the movie theaters. I’ve always been fascinated by the way pop culture shapes our understanding of technology. Think back to the first time you saw Terminator; there was Skynet. This was a central intelligence system responsible for global defense that ultimately saw humanity as a threat, which led to the ‘Judgement Day’ and a nuclear war against humanity.
Or we can go back even further to 2001: A Space Odyssey; there was HAL. HAL got to the point that it thought its own self-preservation was more critical than Dave’s and tried to prevent Dave from doing things by not opening the pod or by asking several questions to begin putting together possible scenarios about what Dave was attempting to do and block those attempts. These cinematic AIs were powerful, eerie, deeply humanized in their logic, and even created a persona. But here’s the thing: those stories, while entertaining, have also blurred the lines between what AI is and what Automation does.
This is how many have learned what they know about AI, but now we will break it down and explain how it works and how it can be used to help with IT Automation, using controls to keep it from going too far.
So, what is the difference between AI and Automation?
AI needs a collection of data to analyze. It can then summarize and identify repetition in that data, allowing it to begin to establish patterns of behavior. This is where we must be careful – this data must be accurate and applicable to what we are trying to learn. If there is incorrect information in this set of data, AI does not know how to determine what is correct and what is not… it can only summarize and try to establish some pattern.
Automation only does what we tell it to do. For example, if we run this process repeatedly, such as restarting a service that has stopped, it will do so. But it does not know what to do if service continues to stop unless we build in the logic to say only try this restart 3 times. Then we can leave it for more logical processes to kick in, or just for human intervention. Either way, it did not come up with anything we did not tell it to do.
One of the most common forms of IT Automation everyone is familiar with is ‘Updates’. Yes, everything, including our mobile phones, tablets, laptops, desktops, and even the old cable TV boxes, requires updates. We all hated them so much and kept postponing them, but then companies got smart and started automating them. Sure, they might give us a little grace period to do this later, but eventually, it will update regardless of what we say.
So where do AI and Automation meet?
Here is where things get exciting! Almost anything that can be performed with a repeatable process can be automated. The key is finding the right tools and methods to capture the data needed to perform these tasks, as well as maintaining some form of error logging in case something does not work as anticipated.
IT Automation is the process of using software and systems to perform repetitive IT tasks with the same level of detail and accuracy while reducing the manual steps necessary to accomplish the same task. This automated process runs the same way every time, which is excellent for reducing human error and potential oversight. However, it also means we must account for all possibilities, or at least ensure we process only the correct ones.
Think of a process like adding a user to a group. The same basic steps are performed every time, so it should be easy to automate. But what if someone entering the data to be used in the process, like the user’s name, reversed the first and last names? That means the automation cannot find the username to add to the group in AD or EntraID, but the process ran; it just didn’t update what you expected.
So now we need to add another layer of error handling to report back not just that the job was completed, but whether it was successful. Or was there an issue, and if so, what was it that prevented it from completing successfully? This is sending the data back that was changed. If the returned information were blank, the data had not been changed, and it would be a reason for someone reviewing the logs to investigate that issue further.
Artificial Intelligence (AI) refers to the capability of computer technology to accurately predict and perform tasks typically performed by humans, including reasoning and logic. The thing is, AI prediction is based on analyzing a large dataset to identify common issues and responses, and to continue learning from 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.
The internet, in general, is filled with all kinds of information. We have all learned that if we do not ask Google the correct question, we do not get an accurate answer.
AI is the same: if you don’t give it a large amount of detailed, accurate data to analyze, it won’t give you precise answers, and even then, it doesn’t act on the data it finds. That’s where you need automation.
Once AI has analyzed the dataset and identified the items to be processed, IT Automation can be used to trigger the action, track the process, capture the results, and report back on what has been done and whether it was successful or not. Then AI can add this to the dataset for more accurate results next time.
Stay tuned for part 2 where we discuss Why IT Automation Is the Foundation for AI Success.
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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