AI for Product Managers
Shipping AI features is a product problem before it is a model problem. Learn to scope, evaluate and launch them well.
A practical track for product managers who are shipping AI features or deciding whether to. AI features behave differently from traditional software: they are probabilistic, their quality has to be measured rather than assumed, and their costs scale with every request. This track teaches product managers to find the right AI opportunities, write specifications engineers can build from, set quality bars before launch, design AI experiences users trust, manage cost, and launch responsibly. No coding is required, and the capstone is a launch plan for a feature from your own product.
Curriculum
What AI Can and Cannot Do for Your Product
A clear-eyed tour of today's AI capabilities for product teams: generation, summarisation, classification, extraction, search and agents. Why probabilistic features need different product thinking from deterministic ones. A simple scoring method for AI opportunities based on user value, feasibility, data availability, risk and cost, and how to spot problems that are better solved without AI.
Specifying AI Features
How AI specifications differ from normal ones. Defining inputs, expected outputs and good-enough quality. Writing down failure modes and what the product does when the AI is wrong. Error budgets, data needs, human review points and edge cases, captured in a template engineers can build and test against.
Technical Decisions for Product Managers
The decisions you will be asked to weigh in on: which model provider, hosted APIs versus open-weight models, retrieval-augmented generation versus fine-tuning, when an agent is justified, and build versus buy. How each choice affects quality, cost, speed to market, data residency and vendor lock-in, explained without code.
Evaluation and Quality
Why AI quality must be measured, not assumed. Building evaluation sets, using human review, and understanding automated scoring such as model-as-judge, including its limits. Setting launch quality bars, reading evaluation reports critically, and agreeing with engineering what blocks a release.
Designing AI User Experiences
Patterns that make AI features trustworthy: setting expectations, showing sources, expressing uncertainty, letting users correct and undo, and graceful fallbacks when the AI fails. Feedback loops that improve the feature over time, and how to avoid designs that encourage over-reliance.
Cost, Latency and Scale
How AI features are priced by usage and why unit economics matter from day one. The levers product teams control: model tiering, caching, prompt length, batching and limits. Balancing response speed against quality and cost for different user journeys.
Responsible Launch
Safety and misuse testing, privacy reviews, and the regulatory context product teams meet most, including data protection law in the UAE and EU and the EU AI Act's risk-based approach. Staged rollouts, human oversight, monitoring in production and incident response for AI features.
Capstone: AI Feature Launch Plan
Bring it together: opportunity, specification, technical approach, evaluation, experience design, cost and launch plan for one feature from your own product. Present to a panel of HYVE engineers and product leads for structured feedback.
Learning Outcomes
- ✓Identify where AI genuinely improves a product, and where a simpler solution wins
- ✓Write AI feature specifications that define inputs, outputs, failure modes and acceptable error rates
- ✓Work with engineers on model choice, retrieval versus fine-tuning, and build-versus-buy decisions
- ✓Define evaluation criteria and quality bars before launch, and read evaluation results critically
- ✓Design AI user experiences that set expectations, show uncertainty and keep users in control
- ✓Estimate and manage AI feature costs, including usage-based pricing, latency and infrastructure
- ✓Plan responsible launches covering human oversight, safety testing, privacy and regulation
- ✓Measure AI feature success in production and decide when to iterate, change models or retire a feature