AI Product Manager

Identify, define, deliver and continuously improve AI products that create measurable business and user value. Translate opportunities into product requirements, features and roadmaps. Guide model and technology choices with specialist teams, establish evaluation and monitoring requirements, and shape product positioning and pricing throughout the product lifecycle. The role spans AI product strategy and product ownership across platforms and use cases. It provides a clear connection between business needs, user experience, technical delivery, responsible AI and commercial outcomes.

1 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.

2 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.

3 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, xplainability, security and deployment constraints. Request relevant benchmarks and experiments to support decisions and revisit
choices as evidence changes.

4 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

5 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.

6 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.

7 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.

8 Coordinate responsible AI and release readiness

Engage governance, risk, security, legal and compliance stakeholders early. Explain…