Here's how you can enhance your response to failure incidents using automation and artificial intelligence.

Automation is the key to success for any business in the field of IT. In the era of machines and artificial intelligence, automation is the element to reduce errors and reduce time and accuracy in implementation.

from my experience, we can develop AI monitoring algorithm model to detect/capture all our critical systems failures in our operation production environment pro-actively and submitting an incident through our ticketing system to technical teams to validate this such failures alerts and fix it before impacting our business users but also, we can utilize AI capability to do fixing for the minor detected issues which it is very easy to address and fix by AI capabilities.

From experience one can develop a workflow after the issue has been prioritized but many managers do not realize or provide proper "measurable" analysis because they do not know the competencies of human capital resources they have on the team which creates a bottleneck effect to providing a scalable solution(s).

Although AI can be used to suggest which team members should be involved, a persons internal drive to address a problem or issue can not be identified by AI, it is the managers role to determine the emotional intelligence their team member(s) have to drive solutions associated to problems or issues. (ITIL4 foundation)

Failures are often seen as setbacks, but they are invaluable opportunities for growth and learning. Embracing failures can lead to profound insights and innovations that success alone cannot offer. one of the several key ways to leverage failures as stepping stones is by finding the RCA.

Proactive IT operations involve using continuous monitoring tools to gather real-time data on system performance, user behavior, and security incidents, with dashboards and alerts providing instant visibility into critical metrics. Predictive analytics with machine learning algorithms analyze historical data to identify patterns and develop models that forecast system failures, capacity constraints, and security threats, enabling timely intervention.