About This Course

A research-grounded methodology for discovering AI workflows — built for the people who actually do the work.

Why This Exists

Most AI training teaches tools. This course teaches process discovery — the skill of seeing your own work clearly enough to know what to change and what to protect.

The AI Process Lab is a canvas-based workspace where you map your real workflows, configure AI nodes, run experiments, and measure outcomes. This course is the structured path through that tool — from understanding your own processes to running rigorous experiments to building collective team intelligence.

The Research Foundation

The methodology draws from:

  • INSEAD/Harvard Field Experiment (2026) — A randomized controlled trial with 515 startups showing that structured workflow visualization (not technical training) was the primary driver of AI adoption success
  • MIT Sloan Task Chaining Research — Evidence that coordination costs at human/AI handoffs often outweigh per-step quality gains
  • Harvard/BCG "Jagged Frontier" Study — Demonstrating that AI capabilities are unpredictable and require structured experimentation to discover
  • CORE-Sandbox Framework (Te'eni et al.) — A peer-reviewed framework validating sandbox approaches for organizational AI learning
  • Ethan Mollick's Lab Framework — Principles for learning-by-doing with AI in professional contexts
  • IDEO AI Affordances — A 2x2 framework for matching AI interaction modes to task types
  • Ink & Switch Malleable Software — Design principles for tools that create a gentle slope from user to creator

Who This Is For

Non-technical team members who produce real deliverables: designers, social media creators, PR professionals, content strategists, investor relations specialists, and anyone whose work involves a repeatable process that might benefit from AI assistance.

You do not need to know how to code. You need to be willing to articulate your work clearly and test your assumptions honestly.

The Deliberate Friction Philosophy

This course is intentionally not easy. It requires you to:

  1. Articulate your process from memory before seeing any suggestions
  2. Write your own prompts rather than selecting from templates
  3. Evaluate outputs honestly using structured rubrics
  4. Document what failed as carefully as what succeeded

The friction is the learning. If the course felt effortless, it would not have changed how you think about your work.

Who Made This?

Built by Kairon — a martech consultant and creative technologist who builds tools for teams learning to work with AI. The AI Process Lab is the workspace; this course is the structured path through it.

The binding constraint on AI adoption is not technical skill, but the ability to see your own work clearly enough to redesign it.