RROLEAAGENCYIIMPACTSSYSTEMSEEXPLORATIONRobustnessBuildCapabilityScalePilotsPrepareDataGovernAIDriveAdoptionDriveROIRole 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
    Robustness — AI transformation lever in the Future Positive Atlas

    GOVERN AI / GV-10

    Robustness

    0.36Adaptability average

    Robustness is the technical resilience and consistent performance of an AI system under both expected and adverse conditions. It centres on a system's ability to defend against adversarial inputs and hold reliability when faced with unexpected data distributions at scale, demonstrated through adversarial probing and validation across diverse inputs. The EU AI Act holds this to a measurable standard, requiring defined levels of accuracy and cybersecurity maintained across the whole lifecycle rather than verified only at deployment, so systems stay dependable against external threats and internal failures.

    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