Start from the operating model and bottlenecks, not vendor labels. A workbench fits when you need a unified pre-bind workspace and referral control; extended PAS works when the PAS can cover most pre-bind needs without heavy add-ons; workflow platforms help when orchestration across multiple tools is the main constraint.

Look for configurability of appetite and authority, referral patterns, audit trails, exception handling, and upgrade-friendly configuration. Also assess integration maturity, observability, and how quickly teams can change rules and flows without creating technical debt.

Define a clear system-of-record per entity (submission, risk, quote, referral) and use APIs/events consistently. Keep pricing and policy state authoritative in their core systems, while the workbench orchestrates work and captures underwriting rationale — avoiding duplicated logic and data drift.

Treat AI as an assisted step with governance: confidence thresholds, human review for low-confidence fields, and logging of inputs/outputs. Ensure traceability from extracted data to decisions, and put controls around prompt/model changes to prevent silent behaviour shifts.

Customise where it differentiates: appetite, authority, referral logic, risk signals and audit requirements. Standardise workflow patterns, document capture, integration plumbing and UI patterns wherever possible to reduce upgrade friction, testing cost and long-term drag.

The usual failure modes are unclear outcomes, scope creep, weak SME availability, poor data quality, and underestimating integration. Mitigation is outcome KPIs, staged releases (intake/triage first), tight change governance for rules/models, and early end-to-end integration testing.