AI-driven IT automation is key to resilience and adaptability

Automation and artificial intelligence can be used to remedy common occurrences, even before respondents are called.

Thanks for the modern world code; Since every company is now a software company, it is much more important than ever to respond quickly if something goes wrong. This is why response to events is an essential process in any organization today.

Unfortunately, the traditional manual approach is highly ineffective. The result is too much average repair time (MTTR) which not only damages customer loyalty but also the end result and especially the morale of the employees.

Fortunately, automation and machine learning (ML) capabilities can get companies off these routes. Teams seek to achieve better results at all levels by reducing repetitive work and human error, optimizing responsive productivity, and adopting automated event response systems.

To take full advantage of this new approach and build a culture of resilience, teams should look for opportunities to improve and upgrade business processes using technology that can eliminate heavy lifting, save the human cycle and create an advantage.

How manual processes damage elasticity

Many companies have accelerated their digital transformation plans over the years, in some cases. However, we have learned that speed can be detrimental, and it is not uncommon for speeding to result in even greater exposure to operational risk.

The infrastructure supporting new digital services may contain millions of line codes and billions of dependencies, so digital crashes are inevitable. Studies show that from 2019 to 2020 complex cases increased by 19%.

To keep pace with the innovations needed to ensure high availability and a great consumer experience, companies must invest in best practices and have strong mechanisms in place to streamline incident response to address and resolve security issues.

In the present manual, the response to reactive phenomena will not allow the magical infrastructure and activities to achieve the adaptive elasticity described by Gartner.

Use the opportunities to take advantage of automation in response to events

In many organizations, the tools, scripts, and manual commands that respondents use to resolve incidents are only remembered by a handful of subject matter experts (SMEs). These events may require manual intervention. As a result, response to events is not quick or effective. Often, companies waste valuable resources by calling on dozens of respondents to resolve an incident. It does not solve the underlying problem.

Additionally, manual processes may result in copy-and-paste errors, unnecessary repetition of steps, limited collaboration between technical and customer support teams, and the use of inaccurate documentation. This results in a long MTTR, unhappy customers and frustrated employees.

Another option for organizations would be to automate their response to events as much as possible, which would benefit their resilience and ability to learn from events and enable active continuous improvement of the system.

A good example is the automation of runbooks driven by machine learning. At a very early stage, response to events involves repetitive tasks such as restarting the server, copying works of art, running scripts, and managing files. By intelligently capturing these processes and logging into runbooks, they can be executed automatically by non-SME stakeholders.

This kind of democratization of the response to events can have a significant impact on the MTTR. First responders spend an average of 15 minutes to classify an alert when it first occurs before raising it to an EMS that takes 15 minutes to perform diagnostics. Conversely, by running workflows in advance, first responders can collect this information immediately and potentially repair recurring problems using automated repairs. If this is not possible, they can immediately escalate the problem to EMS with the information needed to resolve the issue.

In the most mature companies, automation and artificial intelligence (AI) can be used to remedy common occurrences even before respondents are called. In this situation, only unusual and complex cases are extended to SMEs and developers.

Step by step

All this does not happen overnight. Yes, tools are very useful for achieving these goals, but organizations also need to overcome cultural barriers, which can take more time. The key is to start small, with a reasonable goal, to learn as you go. Companies must walk before running.

Their simple, low-risk diagnostics should start automatically which does not affect service delivery or availability and requires minimal processing. By automating order fulfillment, log data collection, and other common troubleshooting steps, parties can reduce MTTRs and avoid sending specific responders when nothing in common is found.

From there, companies can go knee-jerk for the most common problems (e.g., deleting temporary files to free up disk space). Once these common problems have been coded, they can be automated into multi-step sequences to remedy common occurrences. Then, they only automate complex actions with potentially serious performance or availability effects after successfully performing those initial steps.

The thing is, machines are faster than humans at certain tasks and they don’t mind taking on tedious, repetitive tasks. Companies are able to use it to their advantage through AI, ML and automation to improve the resilience and adaptability of IT systems as well as unleash their event response team talent. This is not only to consolidate customer satisfaction and brand image, but also to motivate employees who will be able to devote more time to innovation. In the post-epidemic digital world, innovation will be the key to survival.

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