Finance AI Delivery Lead

As AI Delivery Lead for Finance, you will plan and lead our first AI deployments, establish the reusable delivery and governance capability behind them, and shape the permanent Finance AI Delivery function. This is a new and exciting opportunity for an experienced individual to get hands on with delivery, while setting the direction and building out this new function over the longer term. This is a high-impact, cross-functional leadership role. You will turn an ambiguous opportunity into a clear strategy and sequenced portfolio, stay close enough to delivery to unblock the hardest work, and build confidence that Finance AI solutions are valuable, reliable and controlled.

You will work within a centrally governed, federated model: partnering with Finance AI Champions to identify opportunities, while Finance process owners remain accountable for business outcomes, data, assumptions, human review and operational readiness.

What you will do

• Set the direction: define and communicate the Finance AI strategy, delivery roadmap, standards and operating approach, making clear choices about what Finance should build, buy or enable locally
• Own the opportunity portfolio: identify and size Finance workflows suitable for AI automation, then prioritise them using value, risk, readiness and delivery complexity
• Lead the first deployments: select and design an initial Accounting-focused pilot and lead it end to end, from process discovery and control design through implementation, operation and evaluation
• Make outcomes measurable: set success, failure and stop criteria before delivery begins, covering expected benefits, accuracy, control performance, adoption and the conditions for redesigning or retiring a solution
• Build reusable capability: create and maintain reusable prompts, skills, workflow templates, data context and implementation patterns so each deployment makes the next one faster and safer
• Set the production bar: define when standalone tools are appropriate and when repeatable or material workflows need a managed or agentic platform, with clear expectations for access, versioning, logging, monitoring, evidence and rollback
• Stay technically hands-on: prototype and inspect solutions when needed, including writing small Python scripts, calling Model Context Protocol (MCP) servers and other tools, and building no-code agents to test ideas and unblock delivery
• Lead governance through delivery: partner with Risk & Controls and process owners to embed human review, segregation of duties, IUC/EUC requirements, regulatory considerations and independent challenge where appropriate
• Create alignment across Wise: secure and project-manage contributions from Accounting, Controls, Analytics, Data Science, Engineering, Product and Platform when priorities, dependencies and trade-offs compete
• Build the team: determine the capabilities, roles and capacity required for a permanent Finance AI Delivery function; hire, coach and empower the team as it grows
• Learn and scale: monitor deployed solutions, manage issues and performance drift, share evidence of what worked and failed, and recommend what Finance should scale next