RROLEAAGENCYIIMPACTSSYSTEMSEEXPLORATIONExplainabilityBuildCapabilityScalePilotsPrepareDataGovernAIDriveAdoptionDriveROIRole RedesignUpskillingWorkflowsRedesign & ReskillPeople & CultureDiverse TeamEmployee AIHuman-Machine IntelligenceCareer PathsDigital HRAI LiteracyCentralised AISkillsMomentumCulture & ChangeBusiness CaseRemoving BarriersAlways-On ChangeAI-Ready CulturePurposeTransformation LeadershipCommunicationsEarly AdoptersAI StrategyMotivationStrategic VisionUrgencyPersonalised ChangeExperimentationAutomationSponsorshipStatus ChecksChange TriangleAbilityMaking It StickDeploy–Reshape–InventResource AllocationChange MeasurementPilotsOKRs & KPIsBusiness ImpactPerformanceUse-CasesAI MaturityShort-Term WinsRoadmapAssetsSequenced InvestmentsCapability-BuildingProcess MonetisationAI GovernanceOversightRisk & ComplianceIncident ReportingProhibited AITrustRisk ClassificationConformity AssessmentExplainabilityAI EthicsAdvisory CommitteeEthics DesignGovernanceRobustnessMonitoringAccountability EmbeddedSafetySustainabilityResponsible AIData PrivacyFairnessTransparencyAI PoliciesObligationsData StrategyData AccessibilityGenAIData DocumentationData GovernanceData QualityData ManagementData SharingData OwnershipData IntegrityEmbedded DataData ArchitectureData AssetsData FitnessCross-Functional CollaborationScaling AIModular ArchitectureMLOpsHyperautomationChange ImpactAgile ChangeDocumentationAI InventoryIntegrationAI EngineeringAI EcosystemsManaged ServicesManual Process LogsAI Model LifecycleReliabilitySecurityOperating ModelAgile OpsDistributed TechnologyScalable EnterpriseAI MaturityAwarenessBCG AI at ScaleBoomi ProcessDeloitteEU AI ActGartnerGoogleIBMKotterKPMGMcKinseyMITProsci
    Explainability — AI transformation lever in the Future Positive Atlas

    GOVERN AI / GV-08

    Explainability

    0.48Adaptability average

    Explainability requires that an AI system's logic be understandable to humans, so the reasoning behind its outputs can be examined rather than taken on faith. Organisations must be able to identify the specific data used to train a model and detail the factors that shaped a particular decision. By opening the black box of machine learning, IBM's principle lets automated results be verified and gives users and stakeholders a genuine grasp of how and why a system reached its conclusion, the foundation on which trustworthy AI is built.

    Potential across the 5 Future Positive Principles

    Self-Directed
    Agency-Centered
    Impact-Led
    System-Focused
    Evolution-Driven
    Industry Standard baseline
    Future Positive potential

    Source Frameworks