The market is full of misconceptions around AI—the most drastic being that automation will fix everything in an IT environment right away.
Anything impacting technology and processes takes formal planning. But the AI discussion also encompasses that all-important factor of people. How will your staff react to automation? And, how will your customers perceive AI? Every business choice has brand effects, so you have to make sure your company is communicating the right AI message.
You should not stall your time to automation, but rather invest in quick wins and build on automation to make everyone comfortable. Look at the car industry and self-driving vehicles. Tesla, for example, did not come out of the gate saying their car could drive itself. That would have caused doubt and concern from consumers. Tesla went through the process of having the car park itself, then back up in a driveway and park itself. Then they added opening the garage door and pulling into the garage. They gathered data around every piece of engineering to see what worked and what did not. They hired and trained people to enable the AI. While the self-driving car is still evolving, they used AI to figure out the basics first before delivering complete driving automation.
Likewise, we all need to take a step back to prepare for automation. We need access to the right data to determine what to automate, and we must understand how our knowledge and AI come together.
Despite Market Hype, AI Is Not Self-Sufficient
If you don’t have accurately discovered devices, infrastructure, application performance monitors, and network performance monitors, you will not pull the correct data to enable automation. If there is a mistake in the data that tells your AI a particular machine serves a different propose (i.e. production versus Dev), your AI will be inaccurate. Your CMDB has to be a source of truth, but 85% of CMDBs fail because 40% of the data can be inaccurate. Data accuracy has haunted IT for years, but now is not the time to accept those shortcomings.
Automation Is Not a General Solution
Most companies will not have the internal knowledge or infrastructure to handle expansive automation right away. Something as simple as automating the restart of a service if an application goes down takes planning. And, you cannot simply choose to automate the same processes as your competitors, because your organization is unique and might not have the right resources.
Solution: Define exactly what you will automate and figure out what that means for your staff and processes. Look at your own data, find those repeatable processes, and go for quick and easy wins. Implementing a single monitoring solution that pulls data directly from your machines and doesn’t rely on human intervention can improve your CMDB accuracy and identify those inconsistencies you want to correct with automation.
You Still Need a Failsafe of Tribal Knowledge
AI is not immune to malfunctions. AI takes over manual processes so your engineers can focus on things that bring more value to the business. The result of this is often positive, but there is a negative impact we overlook as long as the automation works. If that automation breaks two years down the road, do you still have resources to identify the problem or run the process manually while fixing the AI? There are problems with automation that we don’t even grasp yet, so we have to learn as we go.
Solution: Document the tribal knowledge of current manual process and the specific function of each piece of automation as it’s added. We all need to start automating to keep up with market trends, but we must focus on the little things first to build our automation knowledge. Processes and procedures are vital at this stage of the AI game.
You Don’t Have to Navigate This Road Alone
You need the right processes, an effective monitoring solution, and an understanding of your market to drive automation success. Speak with ScienceLogic experts to learn more about best practices and building a plan at your organization.
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