Director – Project Controls Systems, Data Governance, Analytics, Reporting & AI Enablement

The Director of Project Controls Systems, Data Governance, Analytics, Reporting & AI Enablement is accountable for the governance, administration, and business enablement of the company’s global Project Controls systems landscape, with primary responsibility for Primavera Cloud and Oracle Unifier. This role also leads the responsible adoption of AI including Large Language Models (LLMs) and Agentic AI as applied to cost, schedule and project performance to enhance reporting, controls, data quality, and decision support across the capital delivery portfolio.

This position serves as the critical bridge between business requirements, system configuration, and advanced analytics—ensuring Enterprise‑grade systems and AI capabilities are implemented rapidly, governed rigorously, and produce high‑quality, trusted outputs for executive decision‑making. While not a technical engineering role, this position owns the business use cases, governance, controls, and value realization of AI applied to project controls data.

Key Responsibilities Systems Ownership & Governance

• Own the end‑to‑end governance of Project Controls systems, including Primavera Cloud and Oracle Unifier.
• Configure systems to enforce global standards for schedule, cost, change, forecasting, reporting, and controls.
• Act as the business system owner, accountable for system configuration, usage standards, and lifecycle management.
• Establish decision rights, approval hierarchies, and change‑control processes for both system and AI‑related enhancements.
• Protect system integrity by prioritizing requirements and explicitly declining low‑value or high‑risk requests.

Business Requirements, MVP Delivery & AI Integration

• Lead global requirements gathering, analysis, and prioritization for Project Controls systems and AI‑enabled capabilities.
• Drive a minimum viable product (MVP) approach for both system functionality and AI use cases.
• Translate business intent into clear, testable functional requirements for technical delivery teams.
• Ensure AI capabilities (e.g., LLM‑driven insights, automated narrative reporting, anomaly detection, controls validation) are aligned to business needs and governance standards.
• Operate effectively in a fast‑paced implementation environment requiring extended work hours during initial system and rollout phases.

AI, LLM & Agentic AI Enablement (Business‑Owned, non‑developer work)

• Define and govern AI use cases across Project Controls, including automated schedule and cost variance narratives.
• Data quality validation and exception identification.
• Controls assurance and compliance monitoring.
• Executive reporting and decision support.
• Provide business leadership for the application of Large Language Models (LLMs) to project controls data, ensuring accuracy, explainability, and auditability.
• Oversee the controlled deployment of Agentic AI solutions that execute predefined workflows (e.g., issue triage, reporting preparation, risk flagging) under strict governance.
• Partner with AI, data, and security teams to ensure AI solutions meet enterprise standards for data privacy, security, and responsible use.
• Ensure AI outputs are trusted, traceable, and decision‑ready, with human oversight embedded in all critical workflows.

Data Quality, Reporting & Controls Accountability

• Own the quality, integrity, and consistency of schedule, cost, and forecast data across systems and AI‑enabled outputs.
• Ensure CAPEX/OPEX cost reporting is accurate, auditable, and aligned to executive expectations.
• Oversee global reporting for the data‑center fleet, specific to data‑center fit‑out programs.
• Establish validation rules, data controls, and exception management processes across both traditional reporting and AI‑generated insights.

Team Leadership & Organizational Build

• Build and lead a global organization of business analysts and system administrators from the ground up.
• Scale and manage teams of up to 30+ professionals across regions.
• Develop capability in AI‑assisted analysis, requirements definition, and controls governance within the team.
• Set performance expectations, career paths, and delivery standards aligned to enterprise objectives.

Stakeholder Management

• Serve as a trusted partner to senior leadership by delivering clarity, discipline, and reliable insight.
• Manage competing global priorities while maintaining governance and data integrity.
• Communicate complex system and AI concepts clearly to non‑technical stakeholders.
• Act as a strong gatekeeper against over‑customization, unmanaged AI experimentation, or misaligned expectations.

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