AI Automation Maturity Model
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An AI Automation Maturity Model is a capability maturity model that is an automation classification framework that can assess AI system automation levels (from human-only operation to fully autonomous operation).
- AKA: Automation Level Framework, AI Autonomy Maturity Model, Human-AI Automation Continuum, Automation Capability Model.
- Context:
- It can typically define AI Automation Maturity Levels through human involvement degrees and system autonomy measures.
- It can typically establish AI Automation Transition Points through capability thresholds and responsibility boundarys.
- It can typically measure AI Automation Completeness through task coverage metrics and intervention frequency indicators.
- It can typically assess AI Automation Reliability through failure rate statistics and performance consistency scores.
- It can typically guide AI Automation Strategy through implementation pathways and evolution roadmaps.
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- It can often include AI Automation Intermediate States through partial automation configurations and hybrid operation modes.
- It can often reveal AI Automation Capability Gaps through technology limitations and implementation constraints.
- It can often inform AI Automation Risk Assessments through failure consequence analysis and safety requirement identification.
- It can often support AI Automation Decision-Making through cost-benefit evaluation and readiness assessment.
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- It can range from being a Simple AI Automation Maturity Model to being a Complex AI Automation Maturity Model, depending on its granularity level.
- It can range from being a Domain-Specific AI Automation Maturity Model to being a Universal AI Automation Maturity Model, depending on its application scope.
- It can range from being a Static AI Automation Maturity Model to being a Dynamic AI Automation Maturity Model, depending on its evolution capability.
- It can range from being a Discrete-Level AI Automation Maturity Model to being a Continuous-Scale AI Automation Maturity Model, depending on its level definition approach.
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- It can be applied to Aviation Automation Systems for flight automation classification.
- It can be utilized in Autonomous Vehicle Development for driving automation level definition.
- It can be referenced in Medical AI System Design for diagnostic automation planning.
- It can be employed in Manufacturing Automation for production automation assessment.
- It can be integrated into AI Governance Frameworks for automation policy development.
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- Example(s):
- SAE J3016 Automation Levels (Level 0-5) for autonomous vehicle classification.
- Parasuraman-Sheridan-Wickens Automation Levels for human-automation interaction classification.
- Endsley-Kaber Automation Taxonomy for cognitive function automation levels.
- Healthcare AI Automation Maturity Levels, such as:
- Aviation Automation Maturity Levels, such as:
- AGI Level Framework for artificial general intelligence capability classification.
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- Counter-Example(s):
- Binary Automation Classifications, which lack intermediate level recognition.
- Technology Readiness Levels, which measure development maturity rather than automation degree.
- Software Process Maturity Models, which assess process maturity rather than automation level.
- See: Capability Maturity Model, Organizational Capability Maturity Model, Human-AI Collaboration Model, Autonomous System Classification, Automation Level, Maturity Assessment Framework, AGI Level.