Finneigan's AI profile
The Architect
This turns your assessment into three things: how you work now, where AI changes that work, and the next move to test before the workshop.
You do not just use AI. You design the systems that make it governable.
Your responses show a rare combination: direct AI building experience, insurance operations context, and regulated-industry caution. The opportunity is no longer personal fluency. It is turning that fluency into repeatable team capability.
High scores are not the story. The useful question is where the shape needs to become transferable to others.
You connect technology architecture to insurance operations and quickly spot where AI can remove manual friction.
You naturally separate AI-generated analysis from accountable human decisions, which matters in regulated insurance work.
You have moved from asking AI for output to designing systems where AI is one governed component.
Your answers do not show resistance to AI. They show advanced personal practice. The friction is translating what you know into a repeatable operating model that others can trust, learn, and use without needing your level of technical fluency.
Your workflows are mature, but the team needs patterns, examples, and boundaries.
You will need proof, auditability, and decision ownership before scaling.
Privacy, human oversight, team reskilling, and poor change management all show up repeatedly.
Manual review requires people to scan large datasets, apply quality rules, identify exceptions, and decide which issues need escalation.
AI performs the first pass, flags suspicious rows, explains why each item is unusual, and routes only the judgment calls to a person.
Use AI to classify exceptions and draft the reviewer note, while deterministic checks preserve consistency.
Review this bordereaux sample for anomalies. Flag rows where premium, rate, exposure, coverage, or effective dates look inconsistent. Return a table with issue, why it matters, confidence, and human review action.
Turn your diagnose-refine-verify habit into a simple team method for recovering poor AI outputs.
The AI output below is not good enough. Diagnose whether the issue is missing context, vague instructions, wrong format, weak constraints, or unsupported facts. Rewrite the prompt and list what must be verified.
Create a lightweight intake that classifies AI ideas by data sensitivity, decision impact, audit need, and human oversight.
Classify this AI use case for insurance operations. Identify data sensitivity, decision impact, required human control, audit evidence, and whether the work is safe for a pilot.
Bordereaux anomaly triage: a real operational workflow with clear data, quality rules, and escalation logic.
One AI output that sounded useful but failed verification, especially where wording or input order changed the result.
How do we scale individual AI capability into team practice without weakening governance or accountability?
Before June 22, choose one recurring bordereaux or QA task. Write the AI prompt, define the verification rule, and capture one example where the human reviewer overrules or confirms the output.
Pilot one AI-assisted review process with clear inputs, outputs, verification, and human approval.
Turn the pilot into a reusable team playbook with examples of good prompts, bad outputs, and verification steps.
Create an intake and review model for AI use cases across operations, separating safe pilots from high-risk decisions.
The assessment suggests you are ahead of the likely adoption curve, but that only matters if others can follow. Your leadership contribution is not proving that AI is powerful. It is showing where it is useful, where it is unsafe, and how the team should make the call.