Own product delivery outcomes and the integrated delivery plan for the Becoming a Client journey, tying work to the product roadmap and planned releases with critical path dependency, capacity, decision, and transparency artifacts (e.g., documented decisions and traceability from commitments to shipped increments and outcomes).

Drive trade-off decisions across scope, timing, capacity, and risk to protect committed releases and measurable outcomes.

Establish and run a durable operating rhythm for quarterly planning and sprint and release execution, including health reviews, release predictability, structured burn-down of impediments, proactive escalation, and corrective re-planning when delivery is at risk.

Set and run product delivery governance forums with clear ownership, decision rights, and escalation paths, providing exec-ready communications on progress, risks, dependencies, and release readiness.

Orchestrate cross-product and cross-team dependencies across onboarding steps (pre-acceptance, due diligence, documentation, account opening, product enablement, and early-life servicing handoffs), ensuring sequencing, integration, and timely decisions across the end-to-end journey.

Enforce delivery discipline and go-live readiness across teams, including backlog readiness, clear requirements and acceptance criteria, definition of done, sprint and release health, and launch validation.

Enable cross-functional delivery execution by removing blockers and aligning Product Management (strategy and prioritization) with Technology and Operations partners (build and run).

Embed controls-by-design by partnering with Compliance and Operational Risk to incorporate KYC/AML obligations, data privacy expectations, record retention needs, and auditability constraints as first-order requirements.

Operationalize controls through requirements and non-functional requirements, testing and validation, evidence collection, post-release monitoring and change management, and clear proof of control design and operating effectiveness (including procedures and training).

Identify, prioritize, and scale AI-enabled delivery and onboarding use cases that improve speed, quality, and operational effectiveness (e.g., requirements and story synthesis, workflow insights, document classification and quality checks, test asset support, control evidence packaging, and KPI automation).

Establish responsible AI adoption guardrails with Compliance, AML, Operational Risk, Information Security, Privacy, Legal, Data Governance, and Model Risk Management (as applicable), including human-in-the-loop controls, auditability and traceability of outputs, appropriate data handling, and alignment to firm standards for responsible use.

Define, track, and drive executive-ready success metrics across outcomes and delivery performance, including delivery predictability and flow, quality and rework, onboarding cycle time and data quality, controls and audit readiness, and adoption of new ways of working (including AI-assisted workflows).