I automated my job (and it made me a better leader)

· Source: The GitHub Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Operations & Process Management, Software Development & Engineering · Depth: Intermediate, medium

Summary

Ashley Willis, a senior director at GitHub, details her experience automating significant portions of her leadership role using the GitHub Copilot app, released June 23, 2026. Facing challenges with context-switching and managing disparate tasks, she developed approximately 40 automations to streamline her workflow. These automations, which run as scheduled prompts against her calendar, email, messages, and GitHub repositories, provide daily meeting preparation, ensure access to documents, and triage urgent items. They also track product launches, summarize daily accomplishments for performance reviews, manage team commitments, and handle maintenance tasks like Dependabot PR triage and stale work identification. Willis emphasizes that these tools, particularly beneficial for her AuDHD, free up mental energy for strategic thinking and genuine human connection, augmenting invisible labor rather than replacing human interaction.

Key takeaway

For leaders or professionals overwhelmed by context-switching and administrative overhead, consider implementing AI-powered automations. You can reclaim significant mental bandwidth by offloading repetitive tasks like meeting preparation, commitment tracking, and information synthesis. Start by identifying your biggest daily friction point and use an AI agent app to create a single, targeted automation. This approach allows you to focus on strategic thinking and genuine team engagement, improving consistency and reducing burnout.

Key insights

Automating routine leadership tasks with AI agents enhances consistency and frees mental capacity for strategic work and human connection.

Principles

Method

Identify high-friction tasks, then use an AI agent app (like GitHub Copilot app) to generate and refine scheduled prompts that integrate across work surfaces.

In practice

Topics

Code references

Best for: Director of AI/ML, Operations Professional, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by The GitHub Blog.