Abstract
Software delivery in large organisations is shaped by two different needs. Management must maintain control over scope, cost, commitments, risk and compliance, while delivery teams must respond when technical findings, user feedback or external dependencies change the working assumptions. This paper develops a conceptual framework that connects traditional project governance, agile execution and artificial intelligence (AI)-supported decision-making within one operating model. Five applications are considered: effort and capacity estimation, backlog prioritisation, delivery-risk sensing, project knowledge capture and software quality analytics. The framework also treats bias in historical records, data quality, explainability and accountability as project-management concerns. Four propositions link the framework with schedule adherence, scope control, rework, throughput, release quality and stakeholder satisfaction. A staged route for empirical validation and a practical readiness assessment are proposed. The study is conceptual and draws on practitioner-informed observations. It argues that AI creates value only when its outputs are connected to an existing decision process at the point where action is still possible; responsibility for interpreting, accepting or rejecting the recommendation remains with the accountable project participants.