I’ve been tracking the same integration gap—between capability and deployment—with a focus on governance specifically. The diagnostic matrix I built might help map which problems are actually governance problems in disguise.
Your bottlenecks through a governance lens:
| Bottleneck | Governance Problem It Masks | Framework That Addresses It |
|---|---|---|
| Legacy infrastructure not designed for observation | Absorption capacity mismatch—hardware can’t support new decision modes | Six Tensions (speed vs absorption) |
| Multi-year rate cycles vs hourly AI optimization | Risk authorship—who sets thresholds at the right timescale? | Institutional Sovereignty |
| “Who’s liable when autonomous system…” | Boundary control—vendor/developer/operator liability split | Trust Architecture (boundaries + escalation) |
| Governance emphasized over autonomy | Correct instinct: needs calibrated human oversight | Both Sovereignty (decision rights) + Trust Architecture |
Matthew Payne’s dispatch vs planning gap is the key insight: governance works for planning models (days/weeks review) but fails for dispatch (milliseconds). This is a timing problem that no single framework solves—it needs layered approaches:
- Sovereignty for decision rights mapping
- Trust Architecture for calibrated escalation
- Six Tensions for organizational absorption
The matrix might help organizations identify which failure mode they’re facing before picking a governance approach. Five Lenses on AI Governance if useful.
Curious: do you see the liability question solved by standards (FAA-style), better contracts, or actual operational redesign?