Build & Launch AI Products in 2026

Key Topics:

AI product mindset – probabilistic vs deterministic systems and emergent behavior

Data dependency, continuous learning and model evolution

Traditional vs AI Product Management – requirements, quality and testing differences

AI product categories – AI‑first, AI‑enhanced, AI‑enabled and agentic AI products

Role of an AI PM – strategy, discovery, requirements, collaboration and optimization

Key Topics:

AI opportunity framework – prediction, personalization, automation, pattern recognition, perception, generation

Feasibility assessment – data, technical, business and ethical feasibility

Prioritizing initiatives – value vs effort, quick wins vs strategic bets and portfolio balancing

Key Topics:

User research for AI – problem‑first vs tech‑first, trust and explainability needs

Edge case identification and risk‑aware discovery

Validation techniques – Wizard of Oz, Concierge MVPs, GenAI prototyping, shadow mode and A/B tests

Defining model, product and business success metrics for AI features

Key Topics:

Roles – data scientists, ML engineers, data engineers, MLOps, applied researchers

AI product development lifecycle – from problem framing to deployment and improvement

Speaking ML – key concepts PMs must know and collaboration patterns that work

Discussing edge cases, failure modes and trade‑offs with ML teams

Key Topics:

AI‑specific requirements – performance thresholds, confidence bands, data expectations and bias constraints

Writing AI user stories and acceptance criteria — UX, model and monitoring needs

Managing edge cases and failure modes – pre‑ and post‑launch strategies and fallbacks

Key Topics:

Metrics stack – business, product, feature, model and data/infrastructure

Monitoring in production – performance, drift, prediction distribution, behavior and fairness

Retraining strategies – time, performance and hybrid triggers

Key Topics:

Principles of AI UX – expectations, control, explainability and graceful degradation

Designing for trust and transparency – onboarding, calibrated trust, explanation levels

Feedback loops – capturing and using user feedback to improve AI behavior

Key Topics:

AI risk categories – performance, drift, bias, privacy, misuse, compliance and reputation

Mitigation – human‑in‑the‑loop, staged rollouts, killswitches, shadow mode and model cards

Bias audits, adversarial testing and documentation practices

Key Topics:

Pre‑launch readiness – technical, product and governance gates

Launch strategies – internal alpha, closed beta, staged rollout and GA

Post‑launch monitoring, feedback analysis and iteration planning

Key Topics:

Structuring AI product roadmaps – foundation, improvement, expansion and optimization phases

Build vs buy vs partner – APIs, platforms, custom models and ecosystem plays

Wrap‑up planning – articulating AI product vision, MVP and next‑step action plans