T2: AI Product
Track: AI Product
Agentic stack planes: UX, Control
Enterprise planes: People & Leadership, Culture & Change, Governance & Risk, Strategy & Portfolio, Economics & ROI
Target Audience: Product managers and product leaders
Badge: Applied AI Product
Meaning: AI value architect
Agentic plane colors match the AI Capabilities Stack (round dots on track cards).
Enterprise plane colors match the same diagram (square markers on track cards).
Who This Is For
- Product managers and product leaders
- Business strategists designing AI solutions
- Innovation leaders
- Product owners in AI initiatives
What You'll Learn
Core Capabilities
- AI Value Design - Architect AI solutions that deliver real business value
- Roadmap Strategy - Plan AI product roadmaps with clear milestones
- Use Case Evaluation - Identify and prioritize high-value AI opportunities
- Product-Market Fit - Ensure AI solutions solve real problems
Key Focus Areas
- Value proposition design
- AI product roadmaps and pricing
- Use case prioritization
- Public value design (for public sector)
Schedule
Duration: 4 weeks
Weeks: 1-4
Focus: UX and control planes - trust surfaces, narrative, and commercialization
Week 1: User-Centered Design
Bloom Level: Remember & Understand (primary), Apply (secondary)
Knowledge Dimension: Factual & Conceptual
Tool Mediation: Research, journey mapping, and prototyping tools
- User and operator journeys for agentic products [Understand]
- Borrowed surfaces vs dedicated hub vs hybrid maturity [Apply]
- Accessibility, consent, and clarity in AI UX [Apply]
- Mapping trust moments (approval, download, escalation) [Analyze]
Cognitive Integration: Anchors product work in UX plane concerns - where trust is won or lost.
Week 2: Agent Narrative & Trust
Bloom Level: Understand (primary), Create (secondary), Apply (foundation)
Knowledge Dimension: Conceptual & Procedural
Tool Mediation: Content design and policy-adjacent UX patterns
- Explaining agent behavior without overpromising [Create]
- Disclosure, escalation, and failure narratives [Apply]
- Alignment with governance and brand [Understand]
- Reducing automation surprise and harm [Evaluate]
Cognitive Integration: Develops Judgment on what users should see, approve, and understand.
Week 3: DX Patterns
Bloom Level: Apply (primary), Analyze (secondary), Create (foundation)
Knowledge Dimension: Procedural & Conceptual
Tool Mediation: API design, SDK docs, and developer onboarding tools
- Developer experience as a product discipline [Apply]
- Patterns for tools, APIs, and agent extensions [Analyze]
- Feedback loops for internal and partner developers [Create]
- Balancing flexibility with guardrails [Evaluate]
Cognitive Integration: Connects product strategy to Tool & AI Mediation and adoption.
Week 4: Roadmap + Pricing
Bloom Level: Create (primary), Evaluate (secondary), Apply (foundation)
Knowledge Dimension: Procedural & Metacognitive
Tool Mediation: Roadmap, pricing, and progressive delivery tools
- Roadmap sequencing with governance and dependencies [Create]
- Packaging, metering, and pricing models for agentic products [Create]
- Progressive delivery and feature flags in regulated contexts [Apply]
- Capstone: Agentic Product Roadmap + Pricing Canvas [Create]
Cognitive Integration: Produces the credentialed artifact; demonstrates Transfer and Rigor.
Assessment: Agentic Product Roadmap + Pricing Canvas
Track Learning Outcomes
Upon completing this track, you will be able to:
- Design AI value propositions - Create compelling value propositions that connect AI capabilities to business outcomes
- Evaluate AI use cases - Assess feasibility, value, and risk of potential AI applications
- Prioritize AI initiatives - Use frameworks to rank and sequence AI product opportunities
- Design AI product roadmaps - Create strategic roadmaps with clear milestones, dependencies, and success metrics
- Define AI product requirements - Translate business needs into technical requirements and success criteria
- Assess market fit - Validate that AI solutions address real problems and deliver measurable value
- Design for public value - Create AI products that serve public good and build citizen trust (for public sector)
- Measure AI product success - Define and track metrics that demonstrate AI product value and impact
Transfer & Judgment Capabilities
This track explicitly develops two critical capabilities:
Transfer Capability
Apply learning to novel situations and contexts
- Adapt value proposition frameworks to different market contexts
- Transfer product strategy principles across industries and domains
- Generalize use case evaluation methods to diverse scenarios
- Apply roadmap frameworks to various product types and lifecycles
How it's developed: Through case studies, market analysis exercises, and applying frameworks to different product contexts.
Judgment Capability
Make sound decisions under uncertainty
- Exercise restraint in prioritizing AI product opportunities
- Identify and defer unsuitable AI use cases for products
- Make principled tradeoffs between product features and AI capabilities
- Balance product-market fit with AI feasibility and risk
How it's developed: Through prioritization exercises, "when NOT to build AI products" scenarios, and tradeoff analysis frameworks.
Track Structure
This track contains multiple levels, each with specific learning outcomes. Progress through levels to build your capability in AI product leadership.
Level 1: Foundation (Weeks 1-4)
Bloom Focus: Remember & Understand (primary), Apply (secondary)
Knowledge Dimensions: Factual & Conceptual
Capability Target: Master core concepts and frameworks, understand relationships and principles
- Value and roadmap design [Apply]
- AI product strategy [Understand]
- Use case evaluation frameworks [Apply]
- Market analysis for AI products [Analyze]
Learning Outcomes:
- Recalls product strategy frameworks and concepts
- Explains relationships between value, market, and product strategy
- Applies evaluation frameworks to assess use cases
- Demonstrates foundational understanding of AI product strategy
Level 2: Application (Future)
Bloom Focus: Apply & Analyze (primary), Evaluate (secondary)
Knowledge Dimensions: Procedural & Conceptual
Capability Target: Apply knowledge systematically, analyze complex product scenarios
- Advanced product-market fit analysis
- Complex prioritization scenarios
- Multi-product portfolio strategy
- Advanced value measurement
Level 3: Mastery (Future)
Bloom Focus: Evaluate & Create (primary), Analyze (foundation)
Knowledge Dimensions: Metacognitive & Procedural
Capability Target: Make expert product judgments, create comprehensive strategies
- Expert-level product judgment
- Creating custom product frameworks
- Advanced market analysis
- Portfolio-level strategy and defense
Assessment
- Artifact: Agentic Product Roadmap + Pricing Canvas
- Focus: Transfer and rigor dimensions
- Outcome: Ability to design and prioritize AI value propositions
Public Sector Emphasis
For public sector participants, this track emphasizes:
- Public value creation
- Citizen trust
- Service delivery improvement
- Transparency in AI product design
Credential
Upon completion, earn the Applied AI Product badge - demonstrating your capability as an AI value architect.
Ready to get started? Get in touch to learn more or apply.
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