Cut ramp time without weeks of shadowing.
Governed evaluation builds consistent judgment faster for new hires.
Pain snapshot
- Senior team members spend large portions of each week handling repeat training questions.
- New hires can recite procedures but struggle with real-world edge cases.
- Quality dips when experienced staff are unavailable for ad-hoc coaching.
- Ramp timelines vary widely across managers and teams.
- Early-stage mistakes create avoidable rework and customer friction.
Why typical AI approaches fail here
Promise: Find training content quickly.
Where it breaks
- Content access does not build decision judgment in context.
- Interpretation changes from one trainer to another.
- Edge-case decisions still depend on senior staff.
Example: A new hire finds the right SOP but still escalates a common exception.
Promise: Answer onboarding questions instantly.
Where it breaks
- Guidance style and strictness vary across sessions.
- Risk posture shifts under ambiguous scenarios.
- No stable governance layer for training standards.
Example: Two new hires ask the same question and receive different recommendations.
Promise: Track onboarding tasks automatically.
Where it breaks
- Task completion does not prove decision consistency.
- Complex scenarios still require manual interventions.
- Limited traceability slows quality coaching loops.
Example: All training tasks are marked complete, but first-month errors remain high.
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: 21 days Pilot: 8 days
Baseline: 12h Pilot: 4h
Baseline: 9.5% Pilot: 3.2%
- 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.