Hands-on AI: Engineering and Leadership Sessions

Two connected sessions Stephanie led at Time4Learning’s October 2026 offsite: an hour-long, laptops-open workshop with her engineering team, and a 60-minute session for the senior leadership team on how the company approaches AI.

The workshop. The team compared three approaches to an AI-assisted development lifecycle: the AWS AI-DLC “mob” approach, Kiro’s spec-first approach, and a context-driven approach built on shared project knowledge. Then everyone installed the shared team plugin of guidance, reusable skills and tool connections, and practiced on prepared examples.

The leadership session. How the company governs, learns and builds with AI: an AI Council that brings together perspectives on useful work, learning quality, family trust and environmental responsibility; options for building AI skills across the organization; and how to tell whether AI is actually helping engineering.

Topics included:

• Where AI helps, and where it just adds review
• Choosing AI-assisted development practices that fit her team: clear requirements, small changes, recorded checks
• Lighter-weight options for environmental, privacy and licensing concerns, including local models with Ollama and llama.cpp
• Treating the team’s AI setup as shared code that anyone can improve with a pull request
• Building AI skills across an organization: outside experts, internal training, self-directed learning, or a mix
• Measuring whether AI helps: delivery time, bugs and repeat fixes, and engineering and QA time