AI is seemingly improving or modifying every digital component of our lives.
AI’s digital omnipresence may have an impact in the world of Application Performance Monitoring (APM) too. Here’s why AI in APM makes a great deal of sense.
In today’s digitized world, software applications are a dime a dozen. Their overall quality and real-world performance may range from brilliant to abysmal, depending on the research and innovation invested in their creation. Application Performance Monitoring (APM) is the process of testing how competent, smooth, system-friendly and user-oriented a web application can be in real-time. Traditional APM systems have their own set of drawbacks, which can be overcome by the use of intelligent tools based on AI. Here are a few reasons why AI in APM is the way forward for application performance management:
AI-Based APM Systems Are Solution-Oriented
Traditional APM systems are highly reactive in nature. So, they excel at accumulating data based on the performance statistics of a software application and analyzing them. While this is a tried and tested method, it forces data analysts and other experts to manually sort the accumulated data until they find a coherent output regarding the performance of a specific web application. This ‘sorting’ process may waste a lot of invaluable organizational time. AI, in contrast, intelligently sifts through large swathes of data to find patterns and anomalies that allow it to compute the performance figures of a software application.
Then, AI-powered APM systems allow organizations to get to the root of software-related problems quickly by letting them know about the underlying factors that are negatively affecting the performance of a web application. AI’s unerring efficiency, speed of delivering results, and exactness in finding issues in the source code of web-based applications (as hard as finding a needle in truckloads of haystacks) boost an organization’s APM efforts.
The most significant advantage of letting AI control APM is that, unlike traditional systems that place emphasis on reactive problem-solving, AI proactively focuses on the shortest route to the solution to optimize the process while wasting minimal time, money, and effort. AI understands that problems in an application’s performance are linked to several seemingly isolated factors. Putting all the data and ‘logical thinking’ to use, an AI-powered system provides solutions to solve such problems by pointing out which component is causing performance issues in an application.
AI-powered APM systems have massive computational power when compared to their traditional counterparts. As a result, an AI system can run several permutations and combinations to understand and display the relationship between an organization’s various data security layers. By running several combinations, the technology can determine the security issues in a web application. With information about the same, organizations can make updates to their apps to make them better. As we know, AI has several applications in various fields such as healthcare and B2B operations. Similarly, the deployment of AI in APM promises to yield highly positive results too.