Self-Run Toolkit

Diagnose what is blocking production, select the right use case, and form the team that will deliver it.

• Program Activation Workshop(PDF)

  Structured workshop plan for Week 0. Aligns leadership, sets boundaries, and launches the 90-day sprint with clear ownership.

• Readiness Assessment(PDF)

  Evaluates your organization across ownership, governance, data access, infrastructure, and operational support to determine if production is realistically achievable.

• AI Initiative Portfolio Diagnostic(PDF)

  Maps every active AI initiative (POCs, pilots, demos) and applies selection criteria to eliminate waste and identify the lighthouse candidate.

• Production Readiness Scorecard & Lighthouse Selection(PDF)

  Scores candidate use cases on production viability and selects the one that will become the first system delivered to production.

• Tiger Team Charter(PDF)

  Defines the execution team: named owners, decision authority, escalation paths, and accountability structure. No committees, no shared ownership.

Define the production system in enough detail that engineers, governance, and leadership are aligned on what is being built.

• Escape Canvas(PDF + Excalidraw)

  Single-page system definition capturing problem, inputs, outputs, dependencies, risks, and success criteria. The reference document everyone builds against.

• Data Contract & Dataset Definition Pack(PDF)

  Defines the data requirements, contracts, ownership, quality expectations, and dataset specifications the system needs to function in production.

• System Definition & Architecture Snapshot(PDF)

  Documents the technical architecture, integration points, infrastructure dependencies, and deployment model for the production system.

Translate the plan into structured weekly delivery with tracked progress and clear deliverables.

• 90-Day Execution Planner(PDF)

  Week-by-week execution plan with milestones, checkpoint reviews, and escalation triggers. The operational backbone of the program.

• Delivery Backlog & Work Model(PDF)

  Structures all work into a managed backlog with prioritization rules, work-in-progress limits, and delivery cadence.

• Execution Progress Dashboard(Excel)

  Live tracking of milestones, blockers, and checkpoint status. Replaces status meetings with structured progress visibility.

Establish enforceable governance that enables production delivery without creating bureaucratic overhead.

• Governance Frameworks(PDF)

  Approval gates, risk classification, compliance checkpoints, and responsible AI requirements, designed to enable, not block.

• Go / No-Go Decision Framework(PDF)

  Structured decision criteria for production launch. Removes subjective readiness conversations with scored, evidence-based evaluation.

• Production Contract(PDF)

  Enforceable agreement between the delivery team and the organization defining what 'production' means and what must be true before launch.

• Production Definition of Done(PDF)

  Explicit checklist of what must be complete, technically, operationally, and from a governance perspective, before a system is considered production-ready.

• Production Governance Pipeline Blueprint(PDF)

  The mandatory path every system must pass through. Defines gates, checks, and enforcement points with no manual bypass.

Prepare the organization to run, communicate about, and support the production system after launch.

• Stakeholder Communication Templates(PDF)

  Pre-built communication templates for leadership updates, go-live announcements, and stakeholder alignment at each phase of the program.

Create audit-ready evidence trails and exception handling so the organization can prove compliance and handle edge cases.

• Exception & Waiver Mechanism(PDF)

  Structured process for handling governance exceptions with documented rationale, approval chains, and expiration tracking.

• Lightweight Evidence Model(PDF)

  Defines what evidence must exist for each production system (ownership proof, review records, test results, approval logs) without creating a documentation burden.

Code-based enforcement layer with YAML schemas, Python validators, CI/CD pipeline checks, runbook templates, and automated tests. This is what makes governance enforceable, not aspirational.