• Translate experimental and model‑level results into clear business impact (e.g., fraud loss reduction, automation lift, approval‑rate improvements, operational efficiency). • Define what “success” means for each AI technique or model before it reaches production. • Frame why the work matters to internal stakeholders including Risk, Customer Success, Sales, Finance, and Executive Leadership. • Partner with Data Science to move models from offline experimentation to online testing and controlled rollout. • Help define evaluation frameworks, guardrails, and decision thresholds required for production use. • Ensure trade‑offs (accuracy, latency, cost, operational complexity) are explicit and intentional. • Act as the primary product interface between SAIL, Risk, and Engineering. • Ensure there is a predictable process for ramping traffic, managing risk approvals, and resolving blockers. • Secure engineering capacity and infrastructure alignment by grounding asks in committed outcomes and timelines. • Identify which customer segments or merchants are realistic early adopters for new decisioning approaches. • Define the value story: what problem is solved, how success is measured, and what changes operationally for the customer. • Partner with Customer Success to estimate upside, prerequisites, and deployment complexity. • Maintain a clear view of milestones, dependencies, risks, and decisions across active SAIL initiatives. • Keep stakeholders informed with the right level of detail to sustain momentum without over‑communicating. • Ensure experimental work does not stall due to ambiguity, misalignment, or lack of ownership.