There is not an industrial sector or a company that is not being transformed today thanks to software, which makes it possible to offer new digital services, in order to conquer new markets and reduce operational costs. A quest for continuous improvement on which organizations are prepared to spend nearly $1.2 billion this year, according to IDC. If the goal is to provide their customers and users with better experiences, a week goes by with software performance issues leading to application downtime.
In an ultra-connected digital world, where everything is accessible all the time, a few milliseconds of unavailability are enough to lose millions in revenue. And as our dependence on software increases, the margin for error shrinks. Faced with the chaos that performance issues can cause, IT teams must quickly identify the cause in order to work to resolve them before users are impacted. At the same time, the software landscape continues to evolve to accelerate the pace of innovation while enterprise applications and the hybrid cloud environments they run on become increasingly complex and dynamic.
Organizations now depend on thousands of tightly connected services, operating with millions of lines of code and billions of dependencies. A delivery chain is so complex that it becomes challenging to identify a particular point of failure precisely. If this complexity is not controlled, performance problems will inevitably multiply and worsen, creating far too significant a risk for the company.
Agility, The Other Side Of The Coin
The galloping adoption of the cloud is obviously no stranger to this escalation of complexity. In cloud IT stacks, everything is determined by software. Applications are designed as microservices that run in containers, networks and infrastructure are virtualized, and all resources are shared across applications. This approach has also been widely favored in the digital transformation strategies of companies and has allowed them to gain agility and capacity for innovation.
However, the disadvantage of this approach is that it needs to take this extreme complexity into account. To fully understand their applications, IT teams need to understand the entire stack to have visibility on each level and not just on the application layer. Otherwise, they find themselves unable to identify the cause of a problem and correct it quickly: how can you resolve an incident when you cannot determine where it comes from and why? As applications become more and more structuring for the business, the consequences of this inability to quickly detect and resolve performance problems are increasingly damaging.
If the unavailability of an online banking site, for example, proves annoying today, what would happen tomorrow to a bug in the code of your autonomous car? The consequences could be catastrophic. Companies must act now if they want to maintain control over the future performance of their applications and avoid unfortunate consequences for their business.
Did You Say AI?
IT teams can rest assured a new generation of artificial intelligence has emerged in recent years: AIOps. AIOps tools automatically identify and sort problems and prevent IT teams from being overwhelmed by alerts raised by their monitoring solutions. This global AIOps market should even reach $11 billion by 2023, which reflects a natural appetite for these capabilities.
But these solutions also have their limits, so much so that we are seeing new, more holistic monitoring approaches emerge, which combine the functionalities of AIOps and deterministic AI. The principle: access software intelligence based on real-time analysis and contextualization of performance data. IT teams get instant answers, allowing them to correct performance issues before they impact users. Teams are thus better equipped to deal with software complexity and, therefore, benefit from better visibility over their cloud environments.
Let’s go a little further… Ultimately, AI will be able to slow down performance degradation and prevent them from turning into real problems. To get there, monitoring solutions driven by artificial intelligence will need to be fully integrated into companies’ cloud ecosystems. The AI capabilities will then make it possible to extract all tracking data into a single platform, analyze it in real-time and provide immediate and precise responses that will trigger an autonomous resolution of the problem without intervention. It is not necessary – what is often called application “self-healing”.
Navigate Smoothly Into The Future
It’s no secret: user experience is crucial for businesses today. As romantic as it may seem, AI is becoming the key to enabling businesses to deliver an optimal experience, relegating performance issues to ancient history. Whether thinking in the short or long term, AI capabilities will allow businesses to ensure that performance issues are managed quickly and effectively. And this, while minimizing their impact on the user experience, as well as on the results and reputation of organizations.
Also Read: How Artificial Intelligence Is Improving The Productivity Of Employees?
