Leveraging AI for Efficient On-call Scheduling

Introduction

Regardless of industry specifications, creating and maintaining a highly functional incident management process is crucial for organizations of all sizes. The various potential applications of Generative AI in this process can significantly enhance the efficiency, accuracy, and speed of incident detection, analysis, and resolution. GenAI can be utilized across all stages of the incident management process, including preparation, response, communication, and learning.

In this article we will start with the preparation stage.

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Prepare: Using AI Assistants for On-call Scheduling

Creating an on-call schedule that balances team needs and ensures coverage is crucial for incident management. AI Assistants can streamline this process. By employing AI Assistants, complex scheduling requirements, such as follow-the-sun rotations, become manageable. An intuitive chat interface powered by an LLM can guide users through setting up their schedules, asking relevant questions to understand specific requirements and preferences. This AI-assisted approach simplifies scheduling, making it less time-consuming and more tailored to the unique dynamics of each team.

The AI Assistant engages the user in a conversation to gather necessary details for the schedule. This involves asking about involved team members, rotation types, and on-call coverage. The Assistant's ability to parse natural language enables it to understand and categorize user responses into structured data that can be used in the next steps. The process begins with understanding user inputs and then executing functions to generate the schedule.

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Steps for Creating an On-call Schedule

1. Understanding User Inputs:

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The Assistant initiates the process by engaging the user in a conversation to gather all the necessary details for creating the schedule. This involves asking about the team members, types of rotations, and on-call coverage. Thanks to its natural language processing abilities, the Assistant can understand and organize the user's responses into structured data for the next steps. The instructions for this conversation are provided to the Assistant.

2. Executing Functions to Generate the Schedule:

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After processing and organizing the input data, the Assistant uses the function calling feature to run a custom function specifically designed for schedule creation. This function takes the prepared data and designs the on-call schedule, ensuring all requirements and constraints are satisfied. The end result is a JSON document that represents the finalized on-call schedule.

This use of OpenAI's function calling feature highlights the Assistant's capability to connect conversational input with programmatic output, allowing for complex task automation like schedule creation within a conversational interface.

Below is a sample conversation with ilert AI to generate a follow-the-sun schedule:

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Besides AI-assisted on-call scheduling, LLMs can be leveraged to respond to incidents by reducing noise through intelligent alert grouping, enhancing incident communications, and creating thorough postmortem analyses.

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