As AI Product Manager, you will be a key member of the AI leadership team, working alongside the Director of AI Transformation to deliver our vision for publishing, implementing cost savings and revenue growth. You will manage both new AI product initiatives and the ongoing delivery and optimisation of existing AI products. You will own the roadmap, prioritisation, MVP releases, experimentation with A/B tests, feature releases and sprint coordination. You will work closely with engineering, data, product design, delivery and content operations teams to turn strategic intent into working solutions.
Lead and execute the Publishing AI vision: define, manage and deliver AI-enabled publishing product features that align with the organisation’s strategic goals.
Work with the Director of AI Transformation and senior leadership to translate the strategy into product roadmaps, epics, MVP launches and feature releases.
Gather requirements from stakeholders (editorial and publishing teams) and convert them into epics, user stories and detailed requirements in Jira.
Work with the AI transformation team and Delivery to prioritise requirements (using story points, business value, cost/effort) and maintain the backlog in Jira.
Working with the Delivery lead to coordinate sprints and allocate resources, manage dependencies, risks, and cross-functional teams (engineering, QA, data, design).
Working with Delivery, write and maintain product epics, features, user stories and acceptance criteria; create Jira tickets for development tasks and track progress.
Drive experimentation: design and manage A/B tests, define success metrics, analyse results, iterate the product.
Manage full product lifecycle: planning, development, release, maintenance and continuous improvement of AI products.
For current AI product portfolio: work with the Delivery lead to maintain and enhance existing AI products, prioritise enhancements, ensure service stability, realise cost savings and revenue outcomes.
Understand the capabilities and limitations of AI/ML technologies; work with developers and data Intelligence to identify opportunities for automation, efficiency gains and innovation.
Monitor product performance and business KPIs, produce insights and recommendations for next phases.
Serve as the “voice of AI product” internally: communicate vision, status updates, roadmaps, trade-offs and next steps to stakeholders across the organisation.
Proven experience in a technology / software environment; preferably with experience delivering digital products in an agile environment.
Demonstrable experience working with agile teams: writing epics, managing backlogs, writing user stories, working in Jira (or similar) and using story points for prioritisation.
Experience coordinating sprints, managing resources, dependencies, multi-disciplinary teams (engineering + data + design + operations).
Strong ability to gather and translate business requirements into technical scope, epics and stories.
Experience of experimentation / A/B testing, analysing results and iterating based on data.
Good understanding of AI, machine learning concepts and what is feasible in that domain—able to identify where AI can deliver efficiencies and revenue growth.
Strong strategic mindset, able to translate vision into roadmap and feature-level deliverables.
Excellent communication skills: able to engage technical and non-technical stakeholders, influence decisions, clarify trade-offs.
Strong analytical skills and comfort working with data to drive decisions.
Familiarity with product metrics, success criteria, KPIs, usage/engagement metrics (especially for AI-enabled products)
Excellent organisational and prioritisation skills: able to juggle multiple streams of work, shifting priorities, resource constraints.
Experience in product management of AI/ML products or data-driven solutions.
Experience in publishing, content operations, media or related domain (since role involves publishing AI vision).
Proven track record of driving cost-savings and/or revenue growth via product initiatives.
Experience working with cloud platforms, model deployment pipelines.
Understanding of ethical AI, model risk, bias mitigation.
The opportunity to shape the AI transformation roadmap of a fast-moving organisation.