Product Manager

AI Product Manager

Identify and prioritise opportunities

• Investigate user needs, business processes and market opportunities. Define the problem, target users, current performance baseline and intended benefits. Assess whether AI is appropriate and prioritise use cases against value, feasibility, data readiness, cost and risk. Develop the business case and investment priorities with stakeholders.

Develop AI product requirements

• Own the product requirements document for each use case. Specify intended behaviour, inputs and outputs, scope, user journeys, data requirements, integrations, limitations and measurable acceptance criteria. Describe human review, escalation and fallback needs. Translate business expectations into requirements that delivery teams can implement and evaluate.

Inform model and technology choices

• Maintain a practical understanding of the AI landscape and emerging technologies. Work with researchers, ML engineers and architects to assess models and solution approaches against product requirements. Frame trade-offs involving quality, response time, cost, explainability, security and deployment constraints. Request relevant benchmarks and experiments to support decisions and revisit choices as evidence changes.

Define features and the product roadmap

• Translate use cases into clearly articulated capabilities, features and a prioritised backlog. Define what each feature should do and how users will benefit. Build a roadmap covering discovery, experimentation, pilot delivery, release and improvement. Account for data preparation, technical integration and governance dependencies, and adjust scope and sequencing as evidence emerges.

Delivery and lifecycle responsibilities

Specify data requirements and readiness

• Define required data sources, formats, coverage, volumes, quality checks and labelling needs with specialist teams. Coordinate access and permitted use with data owners. Identify gaps and preparation dependencies, and incorporate them into delivery estimates and the roadmap. Document relevant data handling constraints for review by the appropriate control teams.

Establish product and solution evaluations

• Own the evaluation plan with data scientists, ML engineers and domain experts. Define metrics, baselines, targets, test populations, measurement methods and evaluation periods. Include representative, difficult and failure cases. Evaluate individual capabilities and the complete user workflow, including human intervention and downstream effects. Agree acceptance thresholds, release criteria and the evidence needed for each decision.

Monitor performance and improve the product

• Specify telemetry, user feedback and reporting requirements. Review quality, adoption, outcomes, failures and cost over time. Evaluate changes to models, prompts, data and workflows before release. Prioritise improvements based on evidence and coordinate responses when performance falls below agreed thresholds, including escalation or rollback where appropriate.

Coordinate responsible AI and release readiness

• Engage governance, risk, security, legal and compliance stakeholders early. Explain the product purpose, functionality, inputs and outputs, data handling, limitations and proposed controls. Incorporate their requirements into the backlog and release process. Coordinate evidence for review and ensure product claims reflect demonstrated capability. Maintain visibility of approvals, operational ownership and unresolved risks.

Define positioning and pricing

• Develop the value proposition and product positioning. Understand the full cost of data, models, infrastructure, integration, operations and human review. Compare relevant subscription, consumption and outcome-based pricing approaches where applicable. Assess cost per successful outcome and sensitivity to usage. For internal products, work with Finance and business owners on transparent consumption measures, cost allocation and transfer-pricing proposals.

Drive adoption and value realisation

• Align business and technical stakeholders around priorities, delivery expectations and product outcomes. Support training, change management and user adoption. Track realised benefits against the business case and review whether the product should expand, change or retire. Manage budget and supplier dependencies with the relevant owners and communicate decisions and progress clearly.

Accountability and decision boundaries

• The AI Product Manager owns product outcomes, requirements, priorities and the evaluation framework.
• Researchers, engineers and architects own technical implementation and specialist design decisions
• within their mandates. Governance and control functions retain their approval responsibilities. Finance
• and business owners agree pricing and internal charging arrangements with the product manager.

Qualifications and experience

• • Experience in product management, product discovery, business analysis, or software development lifecycle (SDLC) practices, including requirements elicitation, user story development, and solution design
• • Demonstrated experience in product roadmap development, backlog prioritisation, Agile delivery
• methodologies, and cross-functional product delivery
• • Foundational to intermediate knowledge of Artificial Intelligence (AI), Machine Learning (ML), and
• Generative AI, supported by relevant certifications (e.g., Microsoft AI certifications, Claude Associate, OpenAI, AWS, Google, or equivalent AI credentials)
• • Experience working with data-driven products, including data quality assessment, data governance, data modelling, and ensuring data readiness for analytics and AI use cases
• • Experience designing, managing, or supporting pilots, proof-of-concepts (PoCs), A/B testing, and model evaluation frameworks
• • Proficiency in analytics and performance measurement tools (e.g., Power BI, Tableau, Qlik, Azure Monitor, Application Insights, or equivalent) to track business, product, and operational outcomes
• • Knowledge of Responsible AI principles, AI governance frameworks, risk management, regulatory
• compliance, privacy, and ethical AI practices.
• • Experience developing business cases, financial models, ROI assessments, cost-benefit analyses, and pricing strategies for digital, data, or AI-enabled products and services
• • Strong stakeholder engagement experience, with a proven ability to communicate complex technical concepts to business audiences, facilitate decision-making, and support change management and adoption initiatives

Preferred Qualifications

• • Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, Business Science, or a related field.
• • Professional certifications in Product Management (PSPO, CSPO, SAFe POPM), Agile Delivery, Business Analysis (CBAP, PMI-PBA), or AI and Data disciplines.
• • Experience delivering AI, analytics, digital transformation, or enterprise technology initiatives
• within large, complex organisations.