Section 08 · Section
Systems Engineering, Operations and Organizational Adoption
هندسة الأنظمة والعمليات والتبني المؤسسي
This chapter addresses how AI systems are built, operated and introduced into organizations: requirements, architectural patterns, operations, monitoring, change management and capacity building. It differs from Chapter 5, which explains the models themselves, and from Chapter 10, which presents sectors and their applications without re-explaining the engineering.
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§ 8.0
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الفروع| Code | Branch | Scope | Entries |
|---|---|---|---|
| 8.1 | Requirements Framing and Architectural Patterns | Turning a business need into actionable requirements: context analysis, setting goals, quality constraints and risks, and choosing a suitable architectural pattern and its trade-offs | 0 |
| 8.2 | MLOps and LLMOps | Operational practices for the model lifecycle: automated training and deployment pipelines, version and experiment management, model and feature registries, and operating large language models | 0 |
| 8.3 | RAG, Tool Use and Memory | The engineering of retrieval-augmented generation, tool use and memory in applications: chunking and indexing, retrieval design, tool integration, and short- and long-term memory management | 0 |
| 8.4 | Workflow Re-engineering for Agentic Systems | Changing how organizational work is designed to accommodate agents: dividing tasks between humans and agents, oversight and escalation points, agent permissions, and redrawing processes | 0 |
| 8.5 | AI-BOM and Technical Supply Chains | Inventorying system components and tracing their dependencies: AI bills of materials, the origin of models, data and libraries, dependency security, and technical supply documentation | 0 |
| 8.6 | Deployment, Monitoring and Drift Detection | Running the system after release: staged deployment and rollback strategies, performance and cost monitoring, detecting data and concept drift, and operational logs | 0 |
| 8.7 | Organizational Change Management | The human and organizational side of adoption: preparing leadership and culture, engaging stakeholders, addressing resistance, and internal governance that ensures responsible use | 0 |
| 8.8 | Reskilling and Operational Capacity Building | Developing the human capabilities needed for operations: reskilling pathways, team roles, organizational learning tools, and building competence in review and oversight | 0 |
| 8.9 | Measuring ROI and Everyday Adoption | Measuring the value AI delivers inside an organization: return and cost indicators, productivity measurement, daily usage rates, and reasons projects fail | 0 |