AI-Assisted Cost Control and Governance Frameworks for Infrastructure Procurement

Ye Lin Khant

MPRA Paper from University Library of Munich, Germany

Abstract: Major international infrastructure programmes consistently exceed their approved cost baselines, not because data is unavailable, but because governance frameworks fail to act on it in time. This paper argues that Earned Value Management (EVM), augmented by artificial intelligence and embedded within a redesigned governance framework, would systematically close the gap between cost signal detection and decision-maker escalation. Drawing on two detailed case studies, HS2 High Speed Rail in the United Kingdom and the EU High-Speed Rail Network as audited by the European Court of Auditors, this paper applies a consistent thematic coding framework (T1–T5) to map cost tracking mechanisms, early warning failure points, governance failure types, AI intervention potential, and regulatory environment across two contrasting institutional contexts. The analysis finds that HS2 suffered a documented twelve-month lag between independent assurance reporting that the project was undeliverable and the sponsor formally accepting unaffordability, a governance failure of Type C (decision authority) compounded by Type D (incentive misalignment). The EU case reveals a structurally different but equally systemic failure: the absence of enforcement powers over cross-border project completion, universal cost overruns averaging 78% at line level, and cost-benefit analyses that functioned as administrative formalities rather than decision tools. [5-6] The paper proposes an AI-EVM governance framework comprising seven integrated components a Baseline Realism Validator, Cost Performance Index Monitor, Contractor Estimate Anomaly Detector, Assurance Report NLP Engine, Cross-Border Coordination Tracker, CBA Quality Screening Module, and Schedule Realism Modeller each mapped to a specific governance failure type with defined escalation triggers. The paper concludes with a four-phase implementation roadmap for national governments and multilateral development banks, and identifies the revisions required to FIDIC, NEC4, World Bank Procurement Regulations, and the EU TEN-T Regulation to make these frameworks AI-ready.

Keywords: earned value management; infrastructure cost overrun; project governance; artificial intelligence; cost control; escalation; HS2; high-speed rail; public procurement (search for similar items in EconPapers)
JEL-codes: H54 H57 H83 L74 O22 O33 (search for similar items in EconPapers)
Date: 2026-06-24
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