The AI Finance Program Lead is responsible for shaping and executing the enterprise AI agenda for Finance, turning business priorities into a governed portfolio of scalable AI solutions. The role leads the full lifecycle from opportunity discovery and use-case prioritization through design, delivery, adoption and value realization. Working across Global and local Finance Operations, IT, Data, Risk and business teams, the role ensures that AI solutions improve productivity, decision quality, controls, customer experience and employee experience while meeting responsible AI, data privacy, security and regulatory requirements.
Key responsibilities encompass, but are not limited to:
1. AI Strategy and Roadmap
- Translate Finance strategy and pain points into a multi-year AI vision, roadmap and investment plan aligned with enterprise technology and data strategies.
- Identify where predictive AI, generative AI, intelligent agents, machine learning and advanced analytics can create material business value.
- Define target outcomes, success measures and operating principles for AI-enabled Finance.
- Track relevant market and technology developments and convert them into practical opportunities for Finance.
2. Use-Case Portfolio and Governance
- Own the Finance AI opportunity funnel, including ideation, assessment, prioritization, sequencing and stage-gate decisions.
- Evaluate use cases against strategic fit, value potential, feasibility, data readiness, control impact, scalability and change effort.
- Maintain a transparent portfolio view covering scope, ownership, milestones, investment, dependencies, risks, benefits and decisions.
- Prepare recommendations for governance forums on initiating, accelerating, reshaping, scaling, pausing or stopping initiatives.
- Operate within the framework of Finance Projects Portfolio Management
3. Program Delivery and Solution Industrialization
- Lead cross-functional delivery from discovery and proof of value through minimum viable product, production deployment and scale.
- Establish clear program governance, workstreams, decision rights, delivery plans, budgets, quality standards and escalation paths.
- Coordinate Finance subject-matter experts, product owners, data scientists, engineers, architects, vendors and implementation partners.
- Ensure solutions are supportable, monitored and integrated into business processes, controls and the target technology landscape.
4. Responsible AI, Data and Controls
- Embed responsible AI requirements throughout the lifecycle, including human oversight, transparency, explainability, fairness and appropriate use.
- Partner with Data, IT, Cybersecurity, Privacy, Legal, Compliance, Internal Control and Audit to meet enterprise policies and regulatory obligations.
- Ensure robust data ownership, quality, lineage, access, retention and model or agent monitoring are built into each solution.
- Maintain auditable documentation, risk assessments, approvals and controls proportionate to the use case.
5. Adoption, Capability and Change
- Build adoption plans with Finance leaders, Change Management and Learning teams, ensuring new AI-enabled ways of working are understood and embedded.
- Create a Finance AI community and practitioner network to share knowledge, reusable assets, lessons learned and responsible experimentation practices.
- Strengthen AI literacy among Finance leaders and employees through targeted learning, practical guidance and coaching.
- Monitor user experience, adoption and behavioral change; address barriers and refine solutions based on feedback.
6. Value Realization and Performance
- Set business-case, baseline and benefit-tracking standards for the Finance AI portfolio.
- Ensure financial and non-financial benefits are measurable, attributable, validated and sustained after implementation.
- Report delivery confidence, adoption, productivity, quality, customer, risk and financial outcomes to senior stakeholders.
- Challenge assumptions and intervene early where delivery, adoption, control or value outcomes are at risk.
7. Stakeholder and Team Leadership
- Act as a trusted advisor to Finance executives and governance bodies on AI opportunities, choices, trade-offs and risks.
- Influence across a global matrix and align Global Process Owners, Finance Operations, regions, countries and enabling functions around common priorities.
- Lead multidisciplinary teams and external partners through clear outcomes, empowerment, disciplined execution and constructive challenge.
- Foster a culture of curiosity, experimentation, accountability and value-focused innovation.