Explanation: This is a moderate single-vehicle collision claim in GA with clear liability, no injuries, and no fraud flags. The claimed amount of $5,879.69 is with

I haven't personally inspected the vehicle photos or repair estimates; I'm relying on the damage estimate provided by the adjuster.

The claim amount is slightly above the typical simple auto threshold, but it's within normal bounds for a moderate collision and doesn't raise red flags.

No attorney involvement noted, but that could change during settlement—I'll monitor if the claim progresses.

This was a straightforward approval. I'm slightly relieved that the documentation was complete and consistent, but I remain cautious about relying on estimates without physical verification.


Explanation: This is a moderate single-vehicle auto claim with clear liability, no injuries, and a claimed amount of $5,879.69 — within my $25,000 subrogation auth

I haven't independently verified the at-fault party's insurance coverage or ability to pay, which could affect recovery.

The claim amount is slightly above the typical simple auto threshold, but it's within moderate collision bounds and doesn't change my approach.

No photos or repair estimates were provided in the subrogation file, but the damage estimate from earlier steps aligns with the claimed amount.

This was a straightforward approval. The clear liability and moderate amount make me confident in pursuing recovery without escalation. Slightly relieved to have a clean case in a busy environment.


Translation debt index is extremely high at 2.01, indicating significant meaning loss at handoffs. The handoff failure rate is 12%, and exception rate is 6.7%, suggesting that information is not flowi

Implement a structured 'context handoff' template that forces each agent (human or AI) to explicitly state: (1) key claim facts, (2) unresolved questions, (3) assumptions made, and (4) what the next s


Diana, the most experienced agent (585 decisions, exhaustion=8.0), has zero AI trust and never uses AI. Pat (189 decisions) and Sanjay (163 decisions) also have zero AI trust despite having positive A

Create a 'human-in-the-loop override' program where these experts are given formal authority to override AI decisions with documented reasoning. Track their override accuracy. If their overrides are c


There's a clear positive trust cascade among agents with high AI exposure. Jordan (400 decisions, +16/-0, trust=1.00), Greg (345 decisions, +11/-0, trust=1.00), and Alicia (743 decisions, +18/-6, trus

Leverage these agents as AI champions. Have them mentor the resistant agents (Diana, Pat, Sanjay) by sharing their experiences and demonstrating how they use AI to improve their work. Create a 'truste


Agents are engaging in boundary work by manually reviewing AI recommendations even when they approve them. Decision samples show humans like Diana, Pat, and Tommy providing detailed justifications for

Formalize this boundary work by creating a 'human verification checklist' that agents can complete quickly. This should be a lightweight process that acknowledges their review without requiring extens


Tommy, with the most negative AI experience (+24/-9), has zero AI trust despite having 24 positive experiences. Nick (+13/-2) has near-zero trust (0.03). This is paradoxical because the positive exper

Conduct a detailed review of the specific errors these agents encountered. If the errors were in complex cases, provide additional AI training for those scenarios. If the errors were minor, create a '


Cost per claim is $258.48, which is HIGHER than expected given the high AI adoption rate. The AI pipeline is auto-processing many simple claims (decisions 5, 8, 9, 13, 14, 19), yet costs remain high.

Conduct a cost analysis comparing AI-processed claims vs. human-processed claims. Identify where the cost overruns occur (e.g., downstream exceptions, rework). If AI-processed claims have higher downs


There's a surprising disconnect between exhaustion and AI trust. Diana has exhaustion=8.0 with trust=0.00, but Mike has exhaustion=8.0 with trust=0.73. Nick has exhaustion=9.0 with trust=0.03. This su

Conduct individual interviews with these three agents to understand their exhaustion sources and AI perceptions. Create personalized interventions: for Diana, reduce workload and show AI value on simp


There's a paradoxical relationship between decision volume and AI trust. Rachel has 223 decisions but zero AI trust and zero AI experience. Tommy has 649 decisions with zero trust. However, Jordan has

Audit the AI routing logic to ensure all agents have equal access to AI recommendations. For Rachel, ensure she's being offered AI support. For Tommy, investigate whether his negative experiences were


```json
[
  {
    "category": "translation_debt",
    "is_novel": false,
    "description": "Translation debt index is extremely high at 2.01, indicating significant meaning loss at handoffs. The handoff failure rate is 12%, and exception rate is 6.7%, suggesting that information is not flowing cleanly between stages. The high cost per claim ($258.48) and low first-pass accuracy (72%) are consistent with this degradation.",
    "evidence": [
      "translation_debt_index: 2.013422818791946",


10 claims were auto-approved by AI but later flagged for escalation or override. The AI didn't know it should pause — the pause signal wasn't in its inputs.

Approval errors caught late, potential customer impact, rework cost


11 claims were auto-approved by AI but later flagged for escalation or override. The AI didn't know it should pause — the pause signal wasn't in its inputs.


Steering committee has no frontline representation (adjusters, team leads). Real transformation committees often include operational voices.