AI CONOPS: Operational Concept for AI-Enabled Systems

Generic, brand-neutral engineering capability; deep, sourced playbooks live in references/ and knowledge/. [DOC]

Generic, brand-neutral engineering capability; sourced playbooks in references//knowledge/. [DOC]

TL;DR

CONOPS for AI systems defines what the system does, for whom, and under what conditions — before architecture begins. Aligned with IEEE 1362-2022, this skill produces the operational concept document that drives all downstream architectural decisions: stakeholder identification, interaction autonomy levels, business value assessment, measurable success metrics, and operational modes with their state transitions [EXPLICIT]

When to Use

• Defining the operational concept for a new AI-enabled system before architecture begins
• Mapping stakeholders and their roles in an AI system (architects, data scientists, operators, consumers)
• Selecting the appropriate AI-human interaction level (Manual → Decision Support → Shared Control → Supervised Autonomy → Full Autonomy)
• Assessing business value of AI use cases (Quick Wins vs Strategic Investments)
• Defining measurable success metrics across three pillars (Technical, Business, UX/Ethics)
• Planning operational modes and state transitions for AI systems
• Communicating AI system vision to executive stakeholders and engineering teams

When NOT to Use

• Internal code structure and module boundaries → ai-software-architecture
• Pipeline design and CI/CD for AI → ai-pipeline-architecture
• Design pattern selection and system tactics → ai-design-patterns
• Testing strategy for AI systems → ai-testing-strategy
• GenAI/LLM-specific architecture → genai-architecture
• Traditional software CONOPS (non-AI) → general stakeholder-mapping skill

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Sub-capabilities (resource map)

Deep, evidence-tagged playbooks — open the one the task needs (ICM Layer 3, on-demand). [INFERENCE]

Procedure

1. Resolve the sub-capability; open the matching references/ playbook. [EXPLICIT]
2. Apply its decision tables; pick the strategy explicitly. [EXPLICIT]
3. Validate against the Quality Criteria and tag every claim. [EXPLICIT]

Quality Criteria

• Sub-capability resolved to one playbook. [INFERENCE]
• Claims evidence-tagged. [EXPLICIT]

Contract

• Aceptación: capability resolved to its reference playbook, applied, validated, evidence-tagged. [EXPLICIT]
• Límites: · Focuses on operational concept, not internal architecture (see ai-software-architecture) · Does not design data pipelines (see ai-pipeline-architecture) · Does not se. [EXPLICIT]
• Casos borde: AI System Replacing Human Process: Interaction level selection is politically sensitive. Stakeholders affected by automation may resist. CONOPS must address change management a. [EXPLICIT]
• Supuestos: · Business stakeholders are available to articulate problem statements and success criteria · At least one AI-aware architect (Vision Holder, Technology Expert, or AI Engineer) par. [SUPUESTO]
• Trade-off: Decision Enables Constrains When to Use --- --- --- --- Level 2 (Decision Support) Human accountability, high trust, regulatory safe Low throughput, human bot. [EXPLICIT]

Packet

Capas del packet, cargables bajo demanda (disciplina ICM: una capa por vez, nunca todas juntas): references/ guías de profundidad (cargar UNA por etapa) · knowledge/ cuerpo de conocimiento · prompts/ prompts listos · examples/ salida de ejemplo · agents/ subagentes del packet · assets/ recursos estáticos.