Pre-sales answers shouldn’t leak margin.
When discount decisions drift, governed evaluation protects margin without slowing sales cycles.
Pain snapshot
- Discount requests are handled differently across reps, managers, and regions.
- Helpful exceptions accumulate and quietly erode gross margin.
- Quote turnaround slows when unclear deals require repeated approvals.
- Leadership discovers margin leakage only after month-end reporting.
- Sales velocity and pricing discipline end up in constant tradeoff.
Why typical AI approaches fail here
Promise: Show pricing policy instantly.
Where it breaks
- Policy snippets do not enforce thresholds in live deals.
- Teams interpret the same rule differently under quota pressure.
- Non-standard terms still require manual intervention.
Example: A rep references the right pricing page but still applies an unapproved discount tier.
Promise: Give immediate pricing guidance.
Where it breaks
- Risk posture changes across sessions and deal context.
- Recommendations drift between conservative and aggressive discounting.
- No stable governance boundary for margin protection.
Example: Two similar enterprise quotes receive different discount recommendations on the same day.
Promise: Route quote approvals faster.
Where it breaks
- Routing does not decide if an exception is allowed.
- Edge-case quotes still escalate to senior approvers.
- Limited traceability slows pricing policy refinements.
Example: Approvals move quickly through the system, but final discount calls remain inconsistent.
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: 18% Pilot: 4%
Baseline: 41% Pilot: 46%
Baseline: 90m Pilot: 25m
- 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.