When experts leave, quality should stay.
Governed evaluation preserves decision standards beyond individual memory.
Pain snapshot
- Critical judgment lives in a small set of experienced people.
- When experts leave, teams revert to inconsistent interpretations.
- Documentation captures facts but not nuanced decision standards.
- Edge-case outcomes become slower, riskier, and less predictable.
- Customer confidence declines when service quality shifts between teams.
Why typical AI approaches fail here
Promise: Preserve institutional knowledge in documents.
Where it breaks
- Documents store content but not decision judgment patterns.
- Teams interpret policy text differently under pressure.
- Complex cases still depend on unavailable experts.
Example: The right playbook is found, but teams still disagree on how to handle an exception.
Promise: Fill expertise gaps with instant answers.
Where it breaks
- Risk posture drifts as prompts and context vary.
- Edge-case strictness changes across sessions.
- No governed baseline for expert-level consistency.
Example: An edge-case approval is granted one day and denied the next with similar facts.
Promise: Keep operations moving despite turnover.
Where it breaks
- Workflow speed does not preserve expert evaluation quality.
- Judgment-heavy cases continue escalating unpredictably.
- Limited traceability makes knowledge transfer harder.
Example: Tickets move on time, but edge-case resolution quality drops after a senior departure.
Faster answers ≠ aligned decisions.
What changes with governed evaluation (IAYS)
Evaluation boundaries are defined before the model answers, so teams apply the same standards every time.
Only defined unknowns escalate, reducing noise while preserving oversight on genuine risk cases.
Decisions are linked to explicit rule sets, making reviews faster and policy updates easier to manage.
IAYS transforms probabilistic output into structured evaluation.
Pilot approach
One workflow, one agent, four implementation phases.
Target outcomes (illustrative)
Results vary based on workflow maturity.
Baseline: 68% Pilot: 90%
Baseline: 2.8h Pilot: 1.1h
Baseline: 16% Pilot: 8%
- Phase 1
Select workflow + capture edge cases
Define one workflow to improve and map the edge cases that currently create delays.
- Phase 2
Structure decision criteria
Turn policy and approval logic into clear governed criteria the agent can evaluate.
- Phase 3
Shadow-mode testing
Ship an agent in shadow mode and compare outcomes against current team decisions.
- Phase 4
Go-live with monitoring
Go-live with override controls, escalation visibility, and ongoing monitoring.