AI Product Ops & AI Enablement Lead

Strategy and portfolio – The group’s product vision, direction and strategy for a 12–18 month roadmap: what gets built, in what order, and why. – Portfolio prioritization and capacity allocation, including how many tools the group can responsibly carry at once. – The build-versus-buy recommendation for every initiative, against a buy-or-configure-first default, and the business case behind it. – The sunset decision for tools that do not earn their adoption. – A two-to-three year view of how AI changes product and engineering work, and a roadmap that stays consistent with it. Building AI products – End-to-end product definition for the group’s AI tooling: problem, users, the workflow it replaces, the adoption path, the measurement plan, the maintenance owner. – AI-native specifications a strong engineer can build from — task boundaries, context sources, failure modes, guardrails, human-in-the-loop points, and the quality bar in numbers. – The product calls that shape the architecture: workflow versus agent, retrieval versus fine-tuning, model selection per use case, and the cost and latency budget. – The quality bar and evaluation strategy: what "good enough" means before the build starts, a failure taxonomy built from real usage, and the regression discipline when prompts or models change. – How the human enters the loop, how uncertainty is shown, and what happens when the system does not know — the decisions that determine whether people trust the tool. – The incident and rollback plan for non-deterministic failure, written before launch.   Adoption – The adoption outcome, measured as instrumented depth of use — not seats provisioned or enthusiasm in a demo. – The adoption path for each tool: pilot teams, a champion in each team, onboarding, office hours, handover to support. – Evangelizing the work across product and engineering: the demo, the prototype, the case made repeatedly and well. – Facilitating the sessions where practice actually changes and leaving them with commitments rather than sentiment. – Change management, rollout, training and enablement content. – Honest reconciliation of instrumented usage against self-reported benefit, and ownership of the gap. Practice and craft – The product operating standards for the organization — intake, prioritization inputs, PRD conventions, definition of done, documentation, decision log — kept deliberately light. – Coaching and mentoring product managers, particularly those earlier in their careers, in continuous discovery, outcome framing, evidence-based decision-making and AI-native practice. – The AI literacy curriculum for the product organization — designed and taught or outsourced. – Recurring forums where teams share what they discovered and what they decided. Measurement and reporting – The definition, baseline and instrumentation for the programme’s success metrics, quantitative and qualitative. – A metric-led reporting cadence to senior leadership, including the results that did not work. – The trade-off framework — speed, reliability, cost, data risk — communicated in writing. Governance partnership – The product-side data decisions: what data each surface accepts, which model serves which use case, and the guardrails that go with it. – Partnership with Legal, Privacy, Security and IT on data classification, model approval, access and responsible-AI standards. Required – Senior product leadership experience in enterprise B2B SaaS, with end-to-end ownership from discovery through delivery, and the ability to show what changed as a result. – Experience leading and growing cross-functional teams, and mentoring product and design practitioners. – Demonstrated ability to deliver through people who do not report to you — with concrete examples of aligning stakeholders who had different priorities, and of moving an organization to a new way of working that stuck. – Hands-on experience designing and shipping generative or agentic AI capabilities in a live product — not