AI Control Towers: An Enterprise Operating Model for Autonomous Business Decision-Making for Saudi Vision 2030 Organizations

Enterprise artificial intelligence (AI) is moving from isolated predictive models and conversational assistants toward agentic systems that plan, use tools, collaborate, and act within delegated authority. This shift opens a governance gap, because organizations deploy AI agents faster than they establish oversight, decision authority, auditability, and value assurance. This study develops the Artificial Intelligence Control Tower (AICT), a governance-driven enterprise operating model for orchestrating and assuring autonomous business decision-making. Design Science Research is used to synthesize evidence from enterprise AI, multi-agent systems, decision intelligence, and responsible AI, together with the NIST AI Risk Management Framework, ISO/IEC 42001, ISO/IEC 23894, the OECD AI Principles, and the European Union Artificial Intelligence Act. The resulting artifact comprises six integrated layers: enterprise data and knowledge foundation, AI workforce and agent registry, orchestration and workflow control, decision assurance, governance and compliance, and executive value oversight. Its control logic is formalized through nine equations that express aggregated assurance confidence, decision risk, autonomy-tier assignment, policy admissibility, execution authority, the expected net value of delegated autonomy, governance coverage, and a weighted maturity index. The framework further specifies an eight-stage decision lifecycle, four risk-tiered autonomy levels, a five-level maturity model, and a balanced performance system covering operational, financial, quality, governance, risk, and workforce outcomes. Ex ante evaluation against eight design criteria and five enterprise scenarios (procurement, inventory, workforce planning, operational risk, and executive performance) indicates that AICT can improve decision velocity and traceability while limiting authority drift and unmanaged automation. The study contributes a unifying enterprise architecture, a formal decision-governance model, seven testable propositions, an implementation roadmap, and a measurement agenda, with relevance to Saudi Vision 2030 organizations that are scaling AI under national and international governance expectations.

Agentic Artificial Intelligence, AI Governance, Autonomous Decision-Making, Decision Assurance, Enterprise AI Operating Model

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