# Enterprise AI Deployment The practical discipline of rolling out AI tools, agents, and context management across an organization. Goes beyond [[AI Governance]] (policies) and [[Enterprise Context Management (ECM)]] (context strategy) to address the operational reality: infrastructure, access control, compliance, training, and change management. ## Key dimensions ### Infrastructure - Approved AI providers and enterprise agreements - Self-hosted model infrastructure for sensitive workloads ([[Running AI Models Locally]] at scale) - [[Model Context Protocol (MCP)]] server deployment for organization-wide tool access - Centralized vs decentralized skill and agent registries ### Access control and permissions - [[AI Agent Permissions]] at the enterprise level: who can deploy agents with what capabilities - Role-based AI access: different teams get different tools and permission levels - [[AI Usage Policy]] enforcement: technical controls, not just written rules - Audit trails: who used which AI tools, with what data, and what happened ### Data governance - [[AI Data Security]]: classification, encryption, access control - Data residency: where AI processing happens (on-prem, specific cloud regions) - Retention policies: how long AI conversations and agent memory are kept - [[AI Training Data Collection]]: enterprise-wide opt-out from provider training ### Compliance - [[EU AI Act]] risk classification for deployed AI systems - Sector-specific regulations (HIPAA, SOC2, GDPR, financial regulations) - [[AI Observability]]: monitoring deployed agents for compliance violations - Incident response: what happens when an AI system misbehaves ### Change management - Training programs: [[Levels of AI use]] progression for employees - Pilot programs: starting with low-risk use cases, expanding gradually - [[Roles and responsibilities in an AI team]]: who owns what - Measuring ROI and impact ### Skill and agent governance - [[AI Skill Distribution]] at enterprise scale: approved skill registries - [[AI Skill Supply Chain Security]]: vetting skills before org-wide deployment - [[AI Skill Versioning]]: coordinated updates across the organization - [[AI Agent Distribution]]: governed agent templates and configurations ## References - ## Related - [[Enterprise Context Management (ECM)]] - [[AI Governance]] - [[AI Context Governance]] - [[AI Usage Policy]] - [[AI Data Security]] - [[AI Agent Permissions]] - [[AI Safety]] - [[Responsible AI]] - [[AI Transformation Playbook]] - [[Roles and responsibilities in an AI team]] - [[AI Skill Distribution]] - [[AI Skill Supply Chain Security]] - [[Model Context Protocol (MCP)]]