How AI can help IT teams see through the clouds of complexity
Businesses understand the importance of providing seamless customer journeys, but we’ve seen a growing spate of digital service outages and software performance problems in recent months. There have been online banking outages that have left customers unable to pay bills on time, while problems with major payment systems have left shoppers unable to use their bank cards at the checkouts.
These problems seriously disrupt peoples’ ability to live their day-to-day lives, so they’re becoming a growing concern for businesses and consumers alike. So, if businesses understand the importance of providing seamless customer journeys, why are outages happening more often?
The complexity conundrum
The soaring complexity of technology ecosystems is the biggest contributor to the rise in software performance problems. Modern digital services reside in complex hybrid multi-cloud environments, spanning multiple platforms and technologies. They’re powered by applications running in dynamic microservices and containers, creating constant change. A single web or mobile transaction now crosses an average of 35 different technology systems or components, compared to 22 just five years ago. With digital transactions crossing such a diversity of components in a dynamic technology stack, it’s gone beyond human capability to manage performance effectively. They struggle to maintain visibility into everything that’s happening in their environment, and to find the root cause of any performance problems that arise quickly.
Unfortunately, this trend is showing no signs of slowing. Digital ecosystems are becoming even more complex, and IT teams are under more pressure than ever to quickly identify and resolve the root cause of any problems before customers feel any impact. If they fail to do so, the spate of digital performance problems and service outages that we’ve seen recently will only occur more often.
Taking the guesswork out of performance
There’s a number of reasons why it’s become impossible for businesses to manage the complexity of their digital ecosystems manually. Firstly, new technologies, infrastructure and platforms are constantly being layered onto IT stacks, requiring more monitoring tools to provide visibility and enable IT teams to manage performance. However, the digital ecosystems that have arisen around these IT stacks are highly dynamic. Whilst this creates the agility that businesses need to thrive, it also makes it impossible for humans to stay on top of performance using traditional monitoring tools.
On top of this, traditional monitoring tools are bombarding teams with alerts, most of which are just white noise. Given that it’s impossible for humans to overcome this challenge manually, organisations need to be able to automate as many IT operations processes as possible. They need the ability to automatically detect issues in real-time and, most importantly, use AI to pinpoint the root cause with precision. These capabilities can help organisations onto the path of auto-remediation, so their monitoring system can detect problems and apply fixes to prevent or resolve the issue before it escalates into a full-blown outage.
No turning back
While moving to the cloud has made businesses far more agile, it’s added exponential complexity to their digital ecosystems. This has had a huge impact on organisations’ ability to successfully monitor performance and rectify any issues quickly and efficiently. AI is crucial to combatting the problem. It can make the process of detecting and rectifying software performance problems much faster and more effective. Ultimately, this will enable IT teams to provide more consistent and positive user experiences, relegating the nightmare of major outages to the past.