Decision Integrity in AI-Enabled Mission Systems
AI does not only change capabilities. It changes how organizations perceive reality, allocate authority, and act under time pressure. This work focuses on keeping decisions tethered to actual conditions when systems degrade.
Who this work is for
- Defense and national-security organizations integrating AI into operational decision chains
- Mission planners and operators responsible for outcomes under degraded conditions
- AI governance, assurance, and safety teams managing high-consequence risk
- Program leadership overseeing autonomous or decision-support capabilities
The mission problem
Most evaluation frameworks emphasize performance metrics, accuracy, or throughput. Operational failure often occurs elsewhere: distorted situational awareness, invisible authority migration, overreliance after success, and loss of practical human control.
By the time these issues surface, they are typically systemic rather than technical.
Essential Reading Path
How after-action records transform events into authoritative narratives that may obscure what actually occurred, shaping future doctrine and training.
ReadWhy repeated operational success shifts decision authority toward systems, increasing vulnerability when conditions change.
ReadThe gap between nominal human oversight and the ability to intervene once tempo exceeds human and organizational limits.
ReadInstitutional pressures that encourage fragile solutions and shift risk rather than reducing it.
ReadThe mismatch between technological acceleration and deliberation capacity in complex operations.
ReadAnalytic model
Signal → Interpretation → Authority → Action → Recorded Learning
Systems can remain technically accurate at the signal level while degrading at interpretation or authority. When recorded lessons reflect the degradation rather than the cause, future decisions drift further from reality.
What differentiates this work
- Focus on decision integrity rather than platform performance
- Treat authority distribution as a core operational variable
- Analyze institutional learning pipelines, not just events
- Identify failure patterns that resemble success until conditions shift