Lead Product Manager, Enterprise AI & Automation

Responsibilities

• Map how work flows across the enterprise to identify where AI can deliver step-change improvements — not just incremental automation, but fundamentally better ways of operating.
• Define functional and technical
• Design and lead the Plan and Commit process with business stakeholders for AI and automation technology requests.
• Define criteria for prioritizing automation projects and enhancements based on strategic alignment, risk reduction, ROI, and operational impact.
• Conduct feasibility assessments and cost-benefit analysis for proposed AI initiatives.
• Maintain a visible backlog of active and pending AI and automation initiatives, giving stakeholders transparency into status, effort, and expected value. IT Product Delivery
• Oversee QA and UAT processes to ensure systems meet business requirements before deployment.
• Coordinate deployment windows to minimize business disruption and risks.
• Establish hypercare programs for major system go-lives, including elevated incident SLAs and daily status check-ins during stabilization.
• Develop Business Systems Product Management competencies across the BT organization to ensure consistent and high-quality product practices across the BT organization.
• Lead vendor selection and commercial negotiations in partnership with Procurement and relevant stakeholders, and own ongoing vendor management for all Enterprise AI tooling contracts.
• Own and execute the Enterprise AI rollout plan in partnership with Corporate Communications and Learning and Development, ensuring deployment is phased, governed, and adoption-focused across all business units.
• Drive change management for AI initiatives — the best solution is worthless if nobody uses it. Build the channels, rituals, and feedback loops that make AI visible and valued across the organization.
• Establish and manage the internal AI organizational capability—including an AI Champion Network and Community of Practice—to drive centralized knowledge sharing, collective skill development, and accelerated, peer-led adoption across all business units.
• Define adoption metrics and post-launch stabilization plans for all major AI deployments.
• Design and own the Enterprise AI metrics framework; measure, report, and continuously improve the quantifiable business value delivered by the AI program.
• Report progress and outcomes to the senior leadership on a regular cadence.