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 only using AI tools to accelerate your own work.
– Working fluency in AI product practice: prompt and context design, retrieval, agentic workflow patterns, guardrails, human-in-the-loop design, and model evaluation. You can discuss model trade-offs, data requirements, cost and latency with engineers without needing first-principles explanations.
– Experience defining what "good enough" means for an AI feature in measurable terms, and holding a product to it. – Experience creating or scaling a design system and driving its adoption across teams.
– Strong research and evidence practice: usability testing, interviews, journey mapping, surveys, A/B testing — run to a professional standard and taught to others.
– Strong customer research and evidence practice: conducting interviews and gathering insights for creating informed decisions.
– Product analytics fluency (for example Amplitude, Pendo, Tableau) and the quantitative literacy to define a metric that survives scrutiny.
– Experience defining and embedding operating practice — design ops, product ops, intake, process and documentation standards — that teams adopted willingly.
– Facilitation skills with senior and skeptical audiences, and experience designing and delivering enablement or training programmes.
– Commercial judgement: business-case creation, budget ownership, and vendor or partner evaluation.
– Excellent written communication: you make your case in a document that survives the meeting you were not in.
– Fluent English
Preferred
– Formal grounding in AI product development
– Experience with agentic development tooling in your own workflow, and a view on what it changes about how teams work.
– Familiarity with engineering productivity measurement (DORA, SPACE, DX Core 4) and clear views on what each does and does not measure.
– Experience with eval and observability tooling for LLM applications.
– Working knowledge of AI governance obligations in the EU and beyond, including AI literacy requirements for internal AI use.
– Experience of release and platform operations, CI/CD or environment management.
– Prior experience standing up a new function, or a founder background.

Employment type
Full-time

Work arrangement
Hybrid