Although mainstream applications for artificial intelligence (AI) are being developed rapidly, there has been an unfortunate tendency among analysts, futurists and the technology media to focus on the most fantastic use cases. Self-driving cars, smart drones and self-aware computers are certainly fascinating subjects, but they have very little practical application for the average business technology user.
The danger is that AI may gain a reputation as an overhyped technology. In truth, there are practical and valuable uses for AI that can drive significant benefits right now.
In particular, organizations of all sizes and across all industries can realize a host of operational and economic benefits from the use of Algorithmic IT operations (AIOps) platforms. As we discussed in our last post, AIOps make it possible to automate many network management functions that have traditionally required human intervention.
AIOps platforms utilize machine learning, advanced data analytics and a variety of other algorithms to enable IT systems to analyze data sets, identify patterns and make autonomous decisions — eliminating the need for programmers to write code for every function.
Given their ability to collect massive amounts of data and analyze it rapidly, AIOps platforms are often considered an essential foundation for Internet of Things (IoT) initiatives. But AIOps is not only for IoT and big data scenarios. These platforms can enhance a broad range of IT functions through increased automation that limits or even eliminates error-prone and time-consuming manual processes. AIOps can apply to anything from infrastructure and cloud provisioning to application deployment and configuration management.
One of the things that separates AIOps platforms from broader data analytics solutions is the ability to integrate with IT operations management (ITOM) toolsets via application programming interfaces (APIs). This enables AIOps systems to not only analyze data and recommend actions but actually trigger those actions automatically. As a result, IT can detect and resolve issues rapidly — in fact, AIOps can often predict and correct problems before they occur.
What’s more, the machine-learning capabilities in AIOps means the system doesn’t just detect and correct problems — it remembers the event and applies the solution automatically if the problem happens again. It eliminates the time-consuming process of incident review and documentation into a knowledgebase article (which is prone to be lost or forgotten after the fact).
Gartner predicts that by next year, 25 percent of global enterprises will have strategically implemented an AIOps platform supporting two or more major IT operations functions. Technologent has developed its Continuous Intelligence Enterprise Monitoring (CIEM) practice to help organizations take advantage of the AIOps strategy.
CIEM builds upon our experience in application performance monitoring enhanced with big data and next-gen algorithmic technologies. Our subject-matter experts help customers assess their environments and implement strategic tools that will work in concert to provide continuous insight into the performance of critical services.
For decades, AI was usually associated with science fiction, often represented by human-like robots such as the Star Wars droids. Even has AI has matured into a valuable tool for IT operations, an overemphasis on the particularly fantastic possibilities has often created unrealistic expectations. However, AIOps platforms are proven to provide real-world benefits for IT organizations through increased automation, better problem resolution and reduced complexity.
March 20, 2018