Operations | Monitoring | ITSM | DevOps | Cloud

Infrastructure changes often trigger incidents, but troubleshooting these incidents is challenging when responders have to navigate through multiple tools to correlate telemetry with configuration changes. This lack of unified observability leads to longer mean time to resolution (MTTR), greater operational stress, and ultimately, negative business outcomes.

Bringing in Artificial Intelligence (AI) into customer support functions has become increasingly crucial. AI agent assistance represents a mix of human expertise and AI capabilities, aiming to enhance the quality and speed of customer service. It streamlines support operations while measurably improving customer experience by providing timely, accurate, and personalized assistance.

As an Observability Strategist at Splunk, I have the unique privilege of partnering with a diverse range of Splunk customers across various industries. This partnership offers me a deeper insight into their essential Observability use cases, how they are utilizing Splunk’s Observability solutions, and their specific needs to maximize both value and efficiency in their Observability practices.

Let us begin with an example that underlines the importance of patch management. Software vulnerabilities are real, irrespective of the business entity. One of the leading healthcare organizations providing data for more than 10 million people had an adverse situation in 2023. Their focus group revealed that while there was a nod to patch management, the prioritization was not taken seriously due to a lack of resources and perceived minimal risks.