Artificial intelligence (AI) and machine learning (ML) were first discussed by classical philosophers attempting to describe how the human brain works as a mechanical manipulation of symbols. Fast forward to the 1940s, and scientists began to explore AI and ML with mathematical reasoning and the introduction of the programmable digital computer. But even after the renewed interest in AI and ML, these concepts didn’t resonate with enterprises until many years later.
Was it because companies didn’t understand the importance of AI/ML, or that they simply didn’t grasp how the technology worked? Whatever the reason, times have changed and today’s enterprises are embracing AI/ML in a big way. Global spending on artificial intelligence systems will reach $35.8 billion in 2019, and more than double to $79.2 billion in 2022, IDC forecasts.
Gartner defines AIOps as the combination of “big data and machine learning functionality to support all primary IT operations functions through the scalable ingestion and analysis of the ever-increasing volume, variety and velocity” (the 3Vs) of IT-generated data. An AIOps platform collects data from multiple sources—regardless of the 3Vs—and makes sense of it all in real-time, correlating historical and real-time data as it relates to the business in an automated fashion.
So, logically speaking, which of today’s enterprises would opt out of automated data analysis? (Hint: none.)
Why You Need AIOps
With terabytes of data flowing from multiple devices to myriad destinations—the edge, cloud, and datacenters—IT teams increasingly are challenged to find, fix and forecast issues at a faster pace than ever before. For this reason, enterprises are exploring AI/ML solutions and looking to implement AIOps platforms.
AIOps can enhance a broad range of IT operations processes, including performance analysis, anomaly detection, IT service management and automation, and event correlation and analysis. It’s a boon for enterprises that need help with common IT issues—reducing MTTR, increasing efficiency, decreasing downtime and more.
AIOps, in fact, has become a necessity for every business, as creating an autonomous IT environment is no longer a luxury. The advantages of using mathematical and logical algorithms to derive solutions or forecast issues before outages affect the customer experience—or hurt the bottom line—is top of mind for most CEOs. With a market opportunity of $2.55 billion and growth expected to top $11 billion by 2023, AIOps in the enterprise is no longer a question of if but when.
The central function of an AIOps platform is to ingest data from multiple sources regardless of source or vendor, and to enable analytics at two points: Real-time analysis upon data ingestion, and historical analysis of stored data. Additionally, the AIOps platform stores the acquired data and provides access to this information, while initiating an action or next step based on the result of its analysis.
Some examples of top enterprises implementing AI/ML today:
- Walmart uses hundreds of bots to automate back-office processes.
- Western Digital reduces CapEx by using artificial intelligence to optimise test equipment.
- Bank of America and Harvard University’s Kennedy School collaborate on responsible AI development.
- 7-Eleven uses chatbots and researches voice interfaces to improve the user experience.
AIOps platforms enhance IT operations by combining big data, machine learning and visualization to deliver greater insights. It’s time to add this necessity to your enterprise’s shopping list. Learn how the AppDynamics AIOps platform can help your business succeed in an automation-driven world.