To support this next horizon of growth, we are seeking an exceptional Manager, AI Strategy & Delivery to shape, govern and drive the enterprise AI agenda.

This is a career-defining opportunity to stand up and own AI strategy at scale — ensuring AI is safe, explainable, commercially impactful and embedded across the business.

• Reporting to a senior digital leader, you will lead the development, execution and governance of the organisation’s AI roadmap. Your remit spans strategy, delivery, guardrails, adoption, literacy and benefits realisation.

• You’ll play a pivotal role in identifying high-value AI opportunities, embedding Responsible AI practices, and uplifting organisational readiness — ensuring the organisation benefits meaningfully from both classical ML and emerging GenAI capabilities.

• This is a role for an AI leader who can confidently move between board-level strategy, enterprise governance, and hands-on delivery leadership.

• Proactively identify, evaluate and capture AI opportunities across the business, ensuring
  the company maximises value from emerging AI technologies.

• Develop and own the enterprise AI roadmap and portfolio aligned to business priorities;
  and benefits tracking.

• Establish, chair and continuously improve governance committees for AI, setting
  standards for model risk, evaluation, approval and Responsible AI practices.

• Drive delivery of priority AI services (GenAI assistants, ML models) through the Data
  delivery teams (Data Science and Digital Innovation); ensure handover-to-run and SLAs.

• Set MLOps standards (registry, feature store usage, CI/CD, monitoring, rollback) in
  partnership with Data Science & Platform Enablement.

• Lead AI literacy and enablement (training pathways, communities of practice, playbooks)
  to uplift safe and effective AI usage across business units.

• Embed Responsible AI controls (privacy, safety, fairness, transparency) and coordinate
  with Legal for compliance.

• Partner with the Data Governance to ensure certified datasets/semantic models underpin AI solutions; ensure lineage and data quality hooks.

• Manage vendor and partner engagements (e.g., cloud providers, consultancies);
  negotiate outcomes, cost and value.

• Report progress, risks and value to senior leadership; maintain dashboards for adoption,
  safety and performance.

• Foster reuse (prompts, features, evaluation suites) to reduce time-to-value and improve
  quality.

• Balancing rapid experimentation with Responsible AI guardrails and regulatory
  expectations.

• Driving adoption and change across diverse business units with varying literacy levels.

• Ensuring data readiness (quality, lineage, access) for AI at scale.

• Avoiding fragmentation of tools and approaches across squads and vendors.

• Attracting and retaining scarce AI/ML talent in a competitive market.