As generative AI accelerates across every industry, organizations face a problem that model-level improvements cannot fix: humans are outsourcing their thinking before forming any conceptual structure of their own. This is automation bias operating at the workflow level. It is quiet, habitual, and measurable.

This session introduces the Somagraphic Learning™ Framework (SLF), a research-backed, IP-protected visual-cognitive approach that sequences human reasoning before AI engagement. Its three-stage cycle, Attempt, Map, and Refine, creates a structured orientation layer that preserves independent thinking and produces a traceable artifact of original reasoning before any AI output enters the process.

The primary deployable tool is the Map Before Machine card, a 10-minute structured pre-AI prompt. Prototype testing with STEM graduate learners and faculty indicates it shifts interaction from generation-based prompting to gap-based refinement prompting. That shift is observable, structurally consistent, and directly relevant to any organization designing human-AI workflows at scale.

The card requires no curriculum redesign and embeds directly into existing LMS platforms including Canvas and Moodle.
Scaling pathways include medical education, organizational strategy, and leadership sense-making contexts where structured pre-AI reasoning has direct application.

Key takeaways:
- Why sequencing human reasoning before AI is a responsible deployment variable, not a workflow preference
- How automation bias operates behaviorally and what a structural intervention looks like in practice
- A concrete, deployable pre-AI reasoning tool applicable across enterprise and professional contexts

Framework reference: doi.org/10.35542/osf.io/fnk7z_v1

This sits at the center of the Future of Work challenge: as AI reshapes how professionals think, decide, and produce, the question is no longer whether to use AI but how to ensure human reasoning stays in the loop before it does.