Course Objective & Scope
When every competitor can call the same foundation model APIs, wrapping a thin UI around a prompt creates no lasting product advantage—and variable inference costs can quickly erode traditional software gross margins. This programme gives product managers and leaders a practical framework for building durable AI product strategy.
Participants analyse data flywheels, workflow depth, unit economics and pricing models (seat-based, usage-based and outcome-based), producing an AI product strategy brief, feature brief and metric plan for their own product.
Who Should Attend?
- Product managers, group product managers, CPOs and business unit leaders
- Digital product strategy, pricing and business development teams
- Venture and innovation leads managing internal or commercial AI products
- No coding required; general product management familiarity is sufficient
Key Takeaways
- Distinguish cosmetic AI features from defensible products that solve core user problems
- Build competitive moats through proprietary context, feedback loops and workflow integration
- Make rigorous build, buy, fine-tune or partner decisions across the AI stack
- Model token and inference unit economics and select the right pricing structure
- Write an AI product strategy and feature brief that aligns executives and engineering
Curriculum
Value Proposition and Defensibility (Moats) in AI Products
- Escaping the 'thin wrapper' trap through deep workflow ownership
- Proprietary context, system-of-record gravity and user feedback flywheels
Stack Decisions: Build vs Buy vs Fine-Tune
- When to buy SaaS, call frontier APIs or host open-weight models
- Managing model provider lock-in and switching costs
AI Unit Economics and Pricing Models
- Marginal inference costs, power-user margin compression and cost routing
- Seat-based, credit/usage-based and outcome-based pricing architectures
Enterprise Adoption Barriers, Risk and Compliance
- Addressing buyer requirements on data residency, IP, KVKK and security
- Matching promised autonomy to actual model reliability
Workshop: AI Product Strategy and Feature Brief
- Opportunity sizing and positioning for your organisation's product
- Drafting an actionable AI feature brief and metric plan
Other Courses in This Category
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Product discovery for probabilistic AI products: identifying AI-suited problems, rapid feasibility spikes on real data, Wizard-of-Oz and prompt prototyping, Minimum Viable Quality (MVQ) and eval-driven acceptance criteria.
- Live Course
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AI product roadmap and portfolio management in a fast-moving model landscape: balancing quick wins with strategic bets, avoiding features that next-generation base models will commoditise, and stage-gated delivery.
- Live Course
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Designing high-impact AI features inside digital products: trigger models, context assembly, input/output contracts, failure/fallback/escalation flows and writing an engineering-ready AI feature brief.
- Live Course
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Generative AI user experience (GenAI UX) beyond the blank chat box: inline copilots, collaborative canvases, guided input controls, streaming states and graceful error recovery patterns.
- Live Course
- 5 modules
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Calibrating user trust and preserving human control in AI products: confidence cues, editable drafts, undo and version history, privacy boundaries and escalation UX.
- Live Course
- 5 modules
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Explainability design in LLM, RAG and decision-support products: inline citations, document/data provenance, reasoning summaries, decision factor breakdowns and auditable interfaces.
- Live Course
- 5 modules
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