Distinguish AI-powered feature development from conventional feature development, including its failure modes.
Digital Transformation & Artificial Intelligence
Product Management for AI-Powered Features and Services
Learn to define, build and iterate AI-powered product features, from evaluating model fit and designing prompts to setting quality metrics and managing risk.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Building a feature powered by a language or generative model requires product management habits that differ from conventional software, because outputs are probabilistic, quality is a spectrum rather than a pass-or-fail test, and failure modes such as hallucination or bias have no direct equivalent in deterministic code. This course teaches product managers to run discovery that tests a genuine user problem before committing to an AI-powered solution, to select a model and design prompts, retrieval or guardrails suited to the feature's accuracy and safety needs, and to define quality metrics and an evaluation set the feature must pass before release. Participants then work through rollout strategy, including phased releases, human oversight and rollback mechanisms, and the production monitoring and feedback loops needed to catch quality drift after launch. The course closes with the cross-functional and portfolio skills an AI product manager needs: coordinating realistic timelines with data science, clearing usage questions with legal, managing unit economics as adoption scales, and sequencing a roadmap that builds capability feature by feature. Exercises use a realistic AI feature brief so participants leave with a discovery-to-launch process they can apply directly.
Expected Learning Outcomes
Frame a product discovery process that tests genuine user problems before committing to an AI-powered solution.
Select a model and design prompts, retrieval or guardrails appropriate to a feature's accuracy and safety needs.
Define quality metrics and an evaluation set that a feature must pass before release.
Design a phased rollout with human oversight and a rollback mechanism proportionate to feature risk.
Build monitoring and feedback loops that detect quality drift and prioritise post-launch improvement.
Manage cross-functional trade-offs with data science, legal and design when scaling AI-powered features.
Who Should Attend
Product managers adding AI-powered features to an existing product or service.
Product leaders building a roadmap of generative AI capabilities across a portfolio.
UX designers and researchers partnering with product on AI feature discovery.
Engineering leads coordinating with product on model selection and evaluation.
Founders and product leads at technology companies building AI-native products.
Programme managers overseeing responsible rollout of AI features to customers.
Course Modules
Select any module to see its sessions and points.
01Foundations of Product Management for AI Features
2 sessions · 8 points
Session 1What Makes AI Product Management Different
- Distinguish deterministic feature behaviour from the probabilistic, variable outputs typical of AI-powered features.
- Reframe product requirements as acceptable ranges of output quality rather than single fixed specifications.
- Identify where an AI feature's failure modes differ from a conventional feature's, including hallucination, bias and drift.
- Assess when an AI-powered approach adds genuine value over a simpler rules-based or manual alternative.
Session 2Discovery and Problem Framing for AI Features
- Run discovery interviews that separate a genuine user problem from enthusiasm for a specific AI capability.
- Define a problem statement and success criteria before selecting a model or technique to address it.
- Assess data availability and quality early, since it often constrains an AI feature more than the choice of model.
- Prioritise candidate AI features using a framework that weighs user value, technical feasibility and risk exposure.
02Designing and Evaluating AI-Powered Features
2 sessions · 8 points
Session 1Model Selection and Prompt or System Design
- Compare candidate foundation models and smaller specialised models against latency, cost, quality and data-handling requirements.
- Design prompts, retrieval strategies or fine-tuning approaches appropriate to the feature's accuracy and consistency needs.
- Specify guardrails that constrain model output to safe, on-brand and policy-compliant responses.
- Document model and prompt decisions so they can be revisited as new models or techniques become available.
Session 2Evaluation, Quality Metrics and Testing
- Define quality metrics specific to the feature, such as factual accuracy, relevance, tone or task completion rate.
- Build an evaluation set of representative and edge-case inputs to test a feature before and after changes.
- Run structured human evaluation alongside automated scoring to catch quality issues automated metrics miss.
- Set launch thresholds that a feature must meet on defined metrics before it is released to users.
03Shipping, Monitoring and Iterating AI Features
2 sessions · 8 points
Session 1Rollout Strategy and Human Oversight
- Design a phased rollout, such as an internal pilot, a limited beta and a staged general release, appropriate to feature risk.
- Define where human review or approval sits in the feature's workflow, and how that changes as confidence in the feature grows.
- Prepare user-facing communication that sets accurate expectations about what the AI feature can and cannot do.
- Plan a rollback or kill-switch mechanism that disables a feature quickly if it behaves unexpectedly after release.
Session 2Monitoring, Feedback Loops and Iteration
- Instrument production monitoring that tracks quality metrics, cost and usage patterns for a live AI feature.
- Build feedback channels that let users flag poor-quality outputs and feed that signal back into evaluation and improvement.
- Detect model or data drift that degrades feature quality after launch, and define a response process.
- Prioritise a backlog of prompt, model and workflow improvements based on production evidence rather than assumption.
04Cross-Functional Leadership and Responsible Scaling
2 sessions · 8 points
Session 1Working with Data Science, Legal and Design
- Coordinate with data science and machine learning teams on realistic timelines for model selection, tuning and evaluation.
- Work with legal and compliance to clear data usage, disclosure requirements and third-party model terms before launch.
- Partner with design to communicate uncertainty, confidence and limitations within the product interface itself.
- Manage disagreements between product, engineering and legal on acceptable risk thresholds for a feature's release.
Session 2Scaling, Cost Management and Roadmap Strategy
- Forecast the unit economics of an AI feature, including inference cost per user, and how that scales with adoption.
- Plan model or vendor migration paths so a feature is not permanently locked to a single provider's pricing or roadmap.
- Sequence a roadmap of AI-powered features that builds organisational capability incrementally rather than in one large release.
- Present a portfolio view of AI features to leadership that ties investment to measured user and business outcomes.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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