AI Automation
A structured way to select, design, and operate AI use cases—so outcomes are measurable, secure, and sustainable.
Why it matters
AI can accelerate service delivery, reduce costs, and free teams for higher-value work. Programs that succeed treat AI as operational capability: clear use-case selection, data controls, human oversight, and continuous evaluation. In Saudi Arabia, solutions that handle government or personal data should align to SDAIA/NDMO standards for data management and protection. Global guidance provides a common approach to AI risk, oversight, and lifecycle controls.
How the AI automation program is built
What you get
Prioritized AI use-case pipeline with quick value cases and feasibility notes.
Design packs per use case: pattern choice (RAG/doc-AI/ML/agent), data flows, guardrails, and HITL points.
Control checklist aligned to SDAIA/NDMO (classification, access, logging, retention).
Evaluation harness and KPIs (task accuracy, latency, cost, policy violations, override rate).
Monitoring runbook: drift detection, incident handling, rollback/versioning.
Lightweight AI CoE governance: intake, review, and change workflows with documentation templates.