Remake Library

The assets are the lenses Remake reasons with, applied within the ADAPT motion’s stages — each declared as a Model, a Diagnostic, a Methodology, or a Principle. Every asset has its own page: what it is, where it was introduced, and the writing that builds on it.

• AI Amnesty Methodology

  The time-boxed programme that brings shadow AI under governance: a declaration window, typically 30 to 45 days, in which employees disclose all AI tool usage without fear of punishment, followed by the post-amnesty roadmap of rapid triage, governance guardrails, pilot launch, and ongoing operations. Its declarations populate the AI/ML register, and its findings are read with the AI Amnesty Questionnaire. For a Board, the no-reprisal commitment must be real and followed by visible action, otherwise disclosed usage simply retreats back into the shadows.

  See also:AI Amnesty QuestionnaireShadow AIShadow AI DiscoveryRisk-based triageAI/ML registerMinimum Lovable GovernanceGuardrails

• AI Amnesty Questionnaire Diagnostic

  The structured instrument for running an AI amnesty: ten sections capturing which tools employees actually use, the use cases they serve, the data they touch, the value already created, and the risks encountered, turning unknown unknowns into a governable inventory.

  Introduced in: Shadow AI and the Case for an AI Amnesty

  See also:AI AmnestyAI CoE SimulatorAI Initiative RubricShadow AI

• AI Business Case Methodology

  The integrated decision framework that crystallises across an ADAPT engagement rather than at a single stage: strategic alignment established at Align, cost and readiness evidenced at Diagnose, value shaped at Advise, and execution designed at Plan. The Investment Case is its directional form; the Transformation Plan carries its validated form.

  See also:ADAPTWell-AdvisedBusiness Case TrapAlignment

• AI CoE Simulator Diagnostic

  An interactive assessment tool operationalising the AI Stages of Adoption, objectively placing each business function within adoption stages using specific criteria rather than subjective self-assessment, revealing an organisation’s multi-speed AI reality.

  Introduced in: AI Centre of Excellence: Mapping Your Multi-Speed AI Reality

  See also:AI Stages of AdoptionAI Initiative RubricMulti-Speed Adoption

• AI Initiative Rubric Diagnostic

  A pilot evaluation tool that scores candidate initiatives across the five Well-Advised value priorities and Five Pillars capability building, recommending whether to prioritise, defer, pipeline, or develop them further.

  Introduced in: AI Centre of Excellence: Your First 90 Days With Well-Advised Value Focus

  See also:AI CoE SimulatorWell-AdvisedFive Pillars of AI CapabilityProcess Audit

• AI Sovereignty Trilemma Model

  The proposition that organisations and jurisdictions can optimise their AI posture for trust, speed or control, but not all three simultaneously, forcing deliberate strategic positioning rather than attempting to serve every market at once.

  Introduced in: The AI Sovereignty Trilemma

  See also:Frontier model

• AI Stages of Adoption AISA Model

  A framework describing five stages of the AI journey, Experimenting, Adopting, Optimising, Transforming and Scaling, plotted on an investment-value graph, recognising that different functions progress simultaneously at different paces.

  Introduced in: Understanding the AI Stages of Adoption

  See also:Five Pillars of AI CapabilityComplete AI Adoption FrameworkMulti-Speed Adoption

• CoE 90-Day Sprint Methodology

  The time-boxed execution method that launches an AI Centre of Excellence’s first pilot portfolio: initiatives scored and selected with the AI Initiative Rubric, run as a ninety-day sprint portfolio that balances quick wins against strategic bets, with governance mechanisms evolving alongside the work rather than ahead of it. Its purpose is to prove the CoE’s value inside a quarter.

  See also:AI centre of excellenceAI Initiative RubricGraduation criteriaHub-and-spoke model

• Complete AI Adoption Framework Model Retired

  An integration of three mechanisms, the Five Pillars (what capabilities), the AI Stages of Adoption (where functions stand), and Well-Advised (why to invest), providing guiding policy for systematic AI governance.

  Superseded by: Remake

  Introduced in: The Complete AI Adoption Framework

  See also:AI Stages of AdoptionFive Pillars of AI CapabilityWell-Advised

• Eight Disciplines of AI Model

  The taxonomy of eight AI disciplines: Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, Robotic Process Automation, Cognitive Computing, and Generative AI. The model names which discipline a piece of work actually calls for, so strategy stops treating AI as a single undifferentiated capability.

  See also:AI Stages of AdoptionCognitive computingComputer visionDeep LearningGenerative AIMachine learningNatural Language ProcessingReinforcement LearningRobotic Process Automation

• Five Pillars of AI Capability Diagnostic

  The five capability domains of an AI capability model that cut across every level of maturity: Governance and Accountability, Technical Infrastructure, Operational Excellence, Value Realisation and Lifecycle Management, and People, Culture and Adoption. Read together, they tell a Board not whether it has AI but whether it can run it.

  Introduced in: The Five Pillars of AI Capability

  See also:AI Stages of AdoptionComplete AI Adoption Framework

• Maximum Fidelity Model

  The evidence discipline of grading every reading a Board relies on by one of four indicator types: lagging indicators of past outcomes, leading indicators of early signals, predictive indicators of future value, and reasoned indicators that prove what must hold true. It matters because a decision is only as strong as the fidelity of the evidence beneath it, and the type tells a director exactly what quality of evidence they are looking at.

  Introduced in: Maximum Fidelity: How Four Indicator Types Strengthen Board Decisions

  See also:Automated reasoningFormal verificationLagging indicatorLeading indicatorPredictive indicatorReasoned Indicator

• Minimum Lovable Governance MLG Principle

  Governance embedded in how work happens: proportionate to risk, continuous rather than episodic, and used because it works. The operating principle through which duties like the DUAA safeguards are actually delivered.

  Introduced in: Minimum Lovable Governance

  See also:Shadow AISix Board ConcernsData (Use and Access) Act 2025AI Amnesty

• Process Audit Diagnostic

  The per-process evaluation applied one process at a time: what the work is for, how it is done today and whether it is still needed, and why it needs the technology at all, returning a verdict on the Stop, Keep, Remake scale with every reading graded by indicator type. For a Board, it means each verdict in a transformation plan traces to evidence rather than to opinion.

  See also:Stop Keep Change ModelMaximum FidelityWell-AdvisedSix Board Concerns

• Redeployment Dividend Model

  The strategic value released when the hours AI frees are redirected to differentiated work rather than eliminated; the headline line of any remaking’s business case. It is realised only by deliberate redeployment: token retraining programmes forfeit it, merely delaying displacement and undermining Board confidence.

  Introduced in: The Redeployment Dividend: Why AI Will Unleash Your People, Not Replace Them

  See also:Reinvention DividendSelective AtrophyUndifferentiated work

• Six Board Concerns Model

  An interconnected lens of six concerns, Strategic Alignment, Ethical and Legal Responsibility, Financial and Operational Impact, Risk Management, Stakeholder Confidence and Safeguarding Innovation, that must be orchestrated together so AI discussion does not collapse into risk management alone.

  Introduced in: Board priorities for AI governance

  See also:Minimum Lovable GovernanceAlignment

• Stop Keep Change Model Model

  The verdict set of the Remake framework: for the work under examination the framework returns one of three verdicts, Stop, Keep, or Change, and all three are governed. A Stop verdict requires a decommission plan and verification that the stopping actually happened, a Keep verdict requires documentation sufficient to break key-person dependency, and a Change verdict proceeds through the full ADAPT motion. A remaking comprises all three, and an all-Keep outcome is a legitimate result.

  Introduced in: Not Everything Needs AI: The Questions That Come Before the Decision

  See also:Process AuditADAPT

• Verification Premium Model

  The differential value commanded by workers who can exercise genuine judgement and verify AI outputs, as distinct from those who merely process what AI produces; it takes on a new dimension when the most capable workers also own the most capable tools.

  Introduced in: The Verification Premium: What Classical Training Reveals About AI Coding Costs

  See also:vibe codingVerification capability

• Well-Advised Principle

  The framework of five strategic priorities, Innovation, Customer Value, Operational Excellence, Responsible Transformation, and Revenue, used to ensure AI investments create balanced value rather than narrow cost reduction.

  Introduced in: Measuring AI ROI with Well-Advised

  See also:Well-Advised AssessmentComplete AI Adoption FrameworkAI Stages of Adoption

• Well-Advised Assessment Diagnostic

  The measurement instrument of the Well-Advised principle: an assessment of the value an AI investment is actually realising across the five strategic priorities, returning a balanced-value reading rather than a single ROI number.

  Introduced in: Measuring AI ROI with Well-Advised

  See also:Well-AdvisedAI Initiative RubricMaximum Fidelity