AIRL™AI Readiness Level. Framework for measuring organizational capability to deploy AI at economic scale across technical, operational, and human dimensions.QCDSQuality, Cost, Delivery, Safety. Core manufacturing value metrics. If your AI doesn't improve at least one, it's a hobby project. Each AIRL advancement requires measurable QCDS impact.TRLTechnology Readiness Level. NASA-developed framework (1970s) measuring technology maturity from concept (Level 1) to operational deployment (Level 9). Widely adopted across aerospace, defense, and advanced manufacturing.MCRLManufacturing Capability Readiness Level. Framework measuring manufacturing process maturity and production capacity. Aligned with TRL to ensure technology and manufacturing capability develop in parallel.PAS 1040:2019Publicly Available Specification by British Standards Institution. Multi-domain readiness framework integrating TRL, MCRL, MRL (Materials), and MTRL (Methods Technology) for complex systems.ISO 56000International Organization for Standardization series on innovation management systems. Provides vocabulary and framework for systematic innovation across organizations.OEEOverall Equipment Effectiveness. Manufacturing KPI measuring availability × performance × quality. Standard metric for production efficiency.SLAService Level Agreement. Contractual performance targets (uptime, latency, accuracy) that deployed systems must meet. Typical at AIRL 8+.ROIReturn on Investment. Financial metric measuring gain/loss relative to investment cost. Expressed as ratio (e.g., 3.0x = 300% return) or payback period.FTEFull-Time Equivalent. Standard unit for measuring workforce capacity. Used to quantify efficiency gains from automation.PoCProof of Concept. Early-stage validation demonstrating technical feasibility without operational integration. Typically AIRL 2-3.RAGRetrieval Augmented Generation. Combining models with real-time data retrieval. Emerges AIRL 4 (prototype), production-ready AIRL 8+ (stable, <2s latency).Agentic AISystems that autonomously decide and act. Progression: AIRL 4-5 (human-supervised) → AIRL 6-7 (semi-autonomous) → AIRL 8-9 (autonomous with feedback).MLOpsMachine Learning Operations. Operational discipline: versioning, monitoring, retraining, rollback. Essential for AIRL 7+.Platform AgnosticNot locked to one cloud provider. Achieved through abstraction layers and containerization. AIRL 8-9 territory.