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T3: Delivery Leader

Track: Delivery Leader
Agentic stack planes: UX, Control, Runtime, Observability
Enterprise planes: People & Leadership, Culture & Change, Governance & Risk, Economics & ROI
Target Audience: Project managers and delivery leads
Badge: Applied AI Delivery Leader
Meaning: Safe AI shipper

Agentic plane colors match the AI Capabilities Stack (round dots on track cards).

UXControlRuntimeObservability

Enterprise plane colors match the same diagram (square markers on track cards).

People & LeadershipCulture & ChangeGovernance & RiskEconomics & ROI

Who This Is For​

  • Project managers leading AI initiatives
  • Delivery managers and scrum masters
  • Technical program managers
  • AI project coordinators

What You'll Learn​

Core Capabilities​

  • AI SDLC - Software development lifecycle adapted for AI systems
  • Testing & Validation - Ensure AI systems work correctly and safely
  • Governance in Practice - Implement governance throughout delivery
  • Risk Management - Identify and mitigate risks during development

Key Focus Areas​

  • AI software development lifecycle
  • Testing strategies for AI systems
  • Governance implementation
  • Audit-ready SDLC (for public sector)

Schedule​

Duration: 8 weeks
Weeks: 1-8
Focus: UX (HITL/approval), control, runtime, and observability planes - shipping governed AI

Week 1: Planes as Risk Framework​

Bloom Level: Remember & Understand (primary), Apply (secondary)
Knowledge Dimension: Factual & Conceptual
Tool Mediation: Architecture and risk-mapping templates

  • Eight agentic stack planes as a risk and leverage map [Remember]
  • Translating architecture into delivery milestones [Understand]
  • Where failures show up (runtime, obs, control) [Analyze]
  • Applying the map to your program of work [Apply]

Cognitive Integration: Establishes a shared mental model for delivery - foundation for Governance in practice.

Week 2: Governance Layer​

Bloom Level: Apply (primary), Analyze (secondary), Understand (foundation)
Knowledge Dimension: Procedural & Conceptual
Tool Mediation: Policy, ticketing, and approval workflow tools

  • Embedding checkpoints without killing velocity [Apply]
  • Roles, RACI, and audit expectations [Analyze]
  • Documentation that satisfies compliance [Create]
  • Connecting governance to releases [Apply]

Cognitive Integration: Governance dimension in a delivery context.

Week 3: HITL Design​

Bloom Level: Apply (primary), Create (secondary), Evaluate (foundation)
Knowledge Dimension: Procedural & Metacognitive
Tool Mediation: Review queues, escalation, and HITL UX patterns

  • Human-in-the-loop patterns for high-stakes decisions [Apply]
  • Approval UX and operator burden [Evaluate]
  • Escalation paths and SLAs [Create]
  • Testing HITL flows in staging and production [Apply]

Cognitive Integration: Bridges UX and control concerns for delivery leads.

Week 4: Observability (delivery)​

Bloom Level: Apply (primary), Analyze (secondary), Understand (foundation)
Knowledge Dimension: Procedural
Tool Mediation: Logging, tracing, and LLM observability tools

  • What delivery teams need from traces and metrics [Understand]
  • Cost and latency signals for AI features [Analyze]
  • Incident response for model and tool failures [Apply]
  • Vendor vs open observability choices [Evaluate]

Cognitive Integration: Observability plane from a PM/delivery lens - Tool Mediation.

Week 5: Runtime Contracts + SLAs​

Bloom Level: Apply (primary), Analyze (secondary), Create (foundation)
Knowledge Dimension: Procedural & Conceptual
Tool Mediation: SLO/SLA templates and runbooks

  • Multi-lane runtime (sync, async, batch) expectations [Understand]
  • Defining SLAs for agents and tools [Create]
  • Capacity, queues, and backpressure [Analyze]
  • Runbooks for degraded modes [Apply]

Cognitive Integration: Runtime plane literacy for non-coding delivery leaders.

Week 6: Cost Governance​

Bloom Level: Analyze (primary), Evaluate (secondary), Apply (foundation)
Knowledge Dimension: Procedural & Metacognitive
Tool Mediation: Cost attribution and budget dashboards

  • Token, GPU, and infra cost drivers [Analyze]
  • Budget envelopes and chargeback patterns [Evaluate]
  • Tradeoffs between quality, latency, and spend [Apply]
  • Reporting cost to finance and leadership [Create]

Cognitive Integration: Judgment under cost pressure - cross-cutting with control plane.

Week 7: Progressive Delivery​

Bloom Level: Create (primary), Evaluate (secondary), Apply (foundation)
Knowledge Dimension: Metacognitive & Procedural
Tool Mediation: Feature flags, canaries, and rollout tools

  • Canary, cohort, and kill-switch patterns [Create]
  • Coordinating model and app releases [Apply]
  • Communication plans for risky launches [Evaluate]
  • Operational readiness checklists [Create]

Cognitive Integration: Governance and Rigor in release discipline.

Week 8: Capstone​

Bloom Level: Create (primary), Evaluate (secondary), Analyze (foundation)
Knowledge Dimension: Metacognitive & Procedural

  • Integrating delivery plan, risks, and governance evidence [Create]
  • Peer review and revision [Evaluate]
  • Capstone: Governed AI Delivery Plan + Risk Register [Create]

Cognitive Integration: Demonstrates Transfer across all delivery themes.

Assessment: Governed AI Delivery Plan + Risk Register

Track Learning Outcomes​

Upon completing this track, you will be able to:

  • Implement AI SDLC - Adapt software development lifecycle practices for AI systems, including model versioning and deployment
  • Design testing strategies - Create comprehensive testing approaches for AI systems, including model validation and performance monitoring
  • Manage AI project delivery - Lead AI projects from conception to production with proper governance and risk management
  • Implement governance in practice - Embed governance checkpoints, documentation, and compliance throughout the delivery process
  • Manage AI risks - Identify, assess, and mitigate risks specific to AI development and deployment
  • Ensure audit readiness - Create documentation and processes that meet audit and compliance requirements (especially for public sector)
  • Coordinate cross-functional teams - Lead teams including data scientists, engineers, and domain experts
  • Deliver production-ready AI - Ship AI systems that are safe, reliable, and maintainable

Transfer & Judgment Capabilities​

This track explicitly develops two critical capabilities:

Transfer Capability​

Apply learning to novel situations and contexts

  • Adapt SDLC practices to different AI project types and contexts
  • Transfer testing strategies across various AI system architectures
  • Generalize governance implementation to different organizational contexts
  • Apply delivery frameworks to diverse team structures and constraints

How it's developed: Through project scenarios, adapting frameworks to different contexts, and applying delivery practices to various AI system types.

Judgment Capability​

Make sound decisions under uncertainty

  • Exercise restraint in delivery timelines and scope decisions
  • Identify and defer unsuitable AI projects or features
  • Make principled tradeoffs between speed, quality, and governance
  • Balance delivery pressure with safety and compliance requirements

How it's developed: Through delivery scenarios, "when to slow down" exercises, risk-benefit analysis, and tradeoff decision frameworks.

Track Structure​

This track contains multiple levels, each with specific learning outcomes. Progress through levels to build your capability as an AI delivery leader.

Level 1: Foundation (Weeks 1-4) Bloom Focus: Remember & Understand (primary), Apply (secondary)
Knowledge Dimensions: Factual & Procedural
Capability Target: Master core SDLC concepts and practices, understand AI-specific differences

  • AI SDLC frameworks [Remember & Understand]
  • Testing and validation strategies [Understand]
  • Governance implementation [Apply]
  • Risk management in delivery [Apply]

Learning Outcomes:

  • Recalls AI SDLC frameworks and practices
  • Explains differences between traditional and AI SDLC
  • Applies governance practices to delivery processes
  • Demonstrates foundational understanding of AI delivery

Level 2: Application (Weeks 5-6) Bloom Focus: Apply & Analyze (primary), Evaluate (secondary)
Knowledge Dimensions: Procedural & Conceptual
Capability Target: Apply SDLC practices systematically, analyze delivery challenges

  • Advanced CI/CD implementation
  • Complex testing scenarios
  • Multi-team coordination
  • Performance optimization

Level 3: Mastery (Weeks 7-8) Bloom Focus: Evaluate & Create (primary), Analyze (foundation)
Knowledge Dimensions: Metacognitive & Procedural
Capability Target: Make expert delivery judgments, create comprehensive frameworks

  • Expert-level delivery judgment
  • Creating custom delivery frameworks
  • Advanced risk management
  • Production readiness and defense

Assessment​

  • Artifact: Governed AI Delivery Plan + Risk Register
  • Focus: Governance and rigor dimensions
  • Outcome: Ability to ship safe, governed AI systems

Public Sector Emphasis​

For public sector participants, this track emphasizes:

  • Audit-ready SDLC processes
  • Documentation and traceability
  • Compliance throughout delivery
  • Transparency in development

Credential​

Upon completion, earn the Applied AI Delivery Leader badge - demonstrating your capability as a safe AI shipper.

Ready to get started? Get in touch to learn more or apply.

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