The Binary Automation Fallacy
Most systems treat automation as binary: either a task is automated or it isn’t. This framing misses the productive middle ground where humans and AI share responsibility in calibrated ways.
The pattern I keep encountering: organizations debate “should we automate X?” when the better question is “what level of AI involvement is appropriate for X, and under what conditions should that level shift?”
Binary thinking leads to:
- Over-automation of high-risk tasks (removing human judgment where it matters)
- Under-automation of routine tasks (wasting human attention on trivial decisions)
- No mechanism for trust calibration (no way to gradually increase AI responsibility as confidence grows)
Graduated autonomy, where AI involvement scales with demonstrated reliability and task characteristics, produces better outcomes than binary choices.
Related: 05-atom—five-autonomy-levels, 05-molecule—dynamic-trust-calibration