Who Is an AI Agent Actually For? A User-Type Field Guide

Short summary

AI agents mean different things across organizational roles—non-technical users want simple reliability, tinkerers build partial automations, leaders make strategic promises, QA teams validate edge cases, and IT inherits deployment responsibility. Each group has divergent mental models and pain points. Deployment failures often stem from the gap between leadership promises and engineering capacity, especially when QA input is excluded.

• •Different personas (non-technical users, power users, leadership, QA, IT) have fundamentally different mental models of what AI agents are and how they fail
• •Each role faces distinct challenges: trust for everyday users, hidden bugs for tinkerers, promised ROI for executives, reproducibility for QA, inherited responsibility for IT
• •Organizational failures occur when leadership promises what engineers can't deliver and QA concerns are overlooked in decision-making

Generated with AI, which can make mistakes.