Sarah Chen
Sales Ops Admin
Eight identified risks across six categories, scored by likelihood and impact. Each risk has a named owner and a specific mitigation plan.
2
Critical (8–9)
5
Moderate (4–6)
1
Low (1–3)
Risk Heat Matrix
Impact →
Seller Adoption Resistance
Sales teams resistant to new payout visibility and plan changes driven by AI recommendations may disengage or dispute more frequently.
CRM / ERP Data Inconsistency
Dirty or incomplete historical data in Salesforce and SAP may undermine AI model training accuracy and cause crediting errors in Phase 1.
Legacy System Integration Delays
Connecting 7 systems including SAP, Salesforce, Workday, and custom commission tools may encounter undocumented APIs and brittle data contracts.
Phase 1 Delivery Slip
Scope creep or resource constraints could delay the Phase 1 foundation, pushing the AI layer launch into a new fiscal year.
Budget Overrun (Phase 2–3)
AI model development costs and infrastructure scale-up in Phases 2–3 may exceed initial estimates as compute and talent costs rise.
Model Bias in Plan Recommendations
AI recommendations for quota and plan design may inadvertently encode historical biases, creating inequitable outcomes across seller segments.
Comp Ops Team Skill Gap
Existing compensation operations team may lack the technical skills to administer AI-driven systems, creating a dependency on vendors.
Regulatory Non-Compliance (SOX / GDPR)
Automated crediting and AI-generated payouts must maintain full audit trails to satisfy SOX requirements and data privacy regulations.