If you’re like most IT leaders, you realize that automating repetitive, low-level incident response actions is key to unlocking enhanced workforce productivity, improved IT services, minimized downtime, better user experiences, cost savings, and the freedom to focus on innovation. Yet you don’t know where to start – or maybe aren’t sure of the best approach.

Large Language Models (LLMs) are advanced artificial intelligence models designed to comprehend and generate human-like language. With millions or even billions of [parameters, these models, like GPT-3, excel in natural language processing, understanding context, and generating coherent and contextually relevant text across various applications.

We're pleased to announce incident.io can now be purchased through the AWS Marketplace!

Before I stumbled into the tech industry (a story for another day), I spent several years in the customer service world as a server and front-of-house manager in restaurants. It was in these jobs that I first honed some critical skills that would later lead me on the path to incident response.

Tech companies face a daunting challenge: a staggering 90% of their IT teams are stuck doing mundane, repetitive tasks, leaving only 10% to focus on strategic innovation. Companies know that automation is the solution to these repetitive, low-level incident response actions; however, many need support to begin automating.

IT incident management aims to swiftly identify, address, and resolve IT disruptions to restore normal service operations. Tracking IT incident management key performance indicators (KPIs) is a vital step toward minimizing disruptions for customers and users. But there are several different KPI and metrics choices, and it’s not easy to identify the right ones that can drive meaningful improvements in incident management.