AI TRAINING
AI for Product Leaders: Shipping AI Features
Ship AI features confidently by mastering discovery, evaluation, and go-to-market strategies for AI-powered products.
What it covers
This programme equips VP-level and senior product managers with a structured approach to discovering, defining, and delivering AI features that create measurable user value. Participants learn how to write AI-native PRDs, design evaluation frameworks for non-deterministic outputs, and navigate pricing and packaging decisions unique to AI products. Sessions combine real-world case studies, hands-on PRD workshops, and peer critique to build immediately applicable skills. By the end, leaders can confidently drive AI feature roadmaps and communicate AI capabilities and limitations to customers and stakeholders.
What you'll be able to do
- Write a complete AI-native PRD including model behaviour specs, edge-case handling, and evaluation criteria for a real feature on your roadmap
- Design a structured evaluation rubric (human + automated) that measures whether an AI feature is ready to ship
- Select and justify a pricing model for an AI feature given different customer segments and usage patterns
- Craft customer-facing release notes and support documentation that accurately convey AI capabilities without over-promising
- Build a risk register specific to AI features covering hallucination, data privacy, latency, and model deprecation
Topics covered
- AI feature discovery: identifying problems worth solving with AI vs. deterministic logic
- Writing AI-native PRDs: specifying model behaviour, fallback paths, and confidence thresholds
- Evaluation frameworks for non-deterministic outputs (LLM-as-judge, human eval, red-teaming)
- Pricing and packaging AI features: consumption-based vs. seat-based vs. value-based models
- Managing AI-specific product risks: hallucinations, latency, model drift, and versioning
- Customer communications and expectation-setting for AI capabilities and limitations
- Roadmap sequencing: build vs. buy vs. integrate LLM/ML providers
- AI product metrics: defining success beyond accuracy (user trust, task completion, retention)
Delivery
Delivered as a four-week blended programme: two 3-hour live virtual workshops per week supplemented by async case study reviews and a group project. A capstone session involves peer review of each participant's AI feature PRD. All sessions are recorded. Optional in-person intensive format available (3 days on-site). Materials include PRD templates, evaluation scorecards, and a pricing decision tree. Hands-on exercises account for approximately 60% of learning time.
What makes it work
- Embedding evaluation checkpoints into the definition-of-done before any AI feature enters beta
- Running structured discovery sessions with actual users to validate that AI adds value over simpler rule-based approaches
- Aligning pricing model design with finance and engineering early to account for variable inference costs
- Establishing an AI comms playbook that sets honest expectations and includes clear user controls and feedback mechanisms
Common mistakes
- Treating AI features like deterministic features — writing PRDs without specifying acceptable model behaviour ranges or fallback states
- Skipping structured evaluation and shipping based on demo performance alone, leading to quality regressions in production
- Defaulting to seat-based SaaS pricing without accounting for inference cost volatility, eroding margins on AI features
- Over-promising AI capabilities in launch communications, generating customer distrust when outputs vary
When NOT to take this
This programme is not the right fit for junior PMs who do not yet own a roadmap or have authority over feature scoping — the pricing, packaging, and stakeholder communication modules assume decision-making authority that early-career PMs typically lack.
Providers to consider
Sources
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