Why AI matters at this scale

McDermott International is a premier, fully integrated provider of engineering, procurement, construction, and installation (EPCI) services for the energy industry. With a century of operation and over 10,000 employees, the company designs and builds complex offshore oil and gas platforms, subsea systems, and related infrastructure. Its projects are characterized by massive capital expenditure, intricate global supply chains, multi-year timelines, and operations in some of the world's most challenging environments.

For an enterprise of McDermott's size and sector, AI is not a speculative trend but a strategic imperative for survival and growth. The capital project nature of the business means that even small percentage improvements in project efficiency, cost prediction, or asset uptime translate to tens or hundreds of millions of dollars in value. At this scale, the volume of data generated from engineering models, IoT sensors on equipment, and project management systems is vast but often underutilized. AI provides the tools to synthesize this data, uncover hidden patterns, and drive unprecedented levels of operational predictability and optimization.

Concrete AI Opportunities with ROI Framing

First, AI-driven project scheduling and risk simulation offers a high-impact opportunity. By applying machine learning to decades of historical project data, weather patterns, and vendor performance, McDermott can move from static Gantt charts to dynamic, probabilistic schedules. This can identify likely delay cascades before they happen, allowing for proactive mitigation. The ROI is direct: reducing average project overruns by even 5-10% protects margin and enhances bid competitiveness.

Second, predictive maintenance via digital twins creates immense value. Creating AI-powered digital models of critical platform components (e.g., compressors, power systems) that ingest real-time sensor data allows for forecasting failures weeks in advance. For a remote offshore platform, an unplanned shutdown can cost over $1 million per day. Preventing a single such event through predictive insights can justify the entire AI initiative, with recurring benefits across the asset lifecycle.

Third, automating design and compliance checks accelerates engineering throughput. Natural Language Processing (NLP) models can be trained to review thousands of pages of technical specifications, drawings, and regulatory documents (e.g., API, ISO standards) to ensure compliance. This reduces manual review time by engineers, cuts down on costly rework due to non-compliance, and speeds up the approval process for clients and regulators, improving cash flow.

Deployment Risks Specific to Large Enterprises

Deploying AI in a 10,000+ person organization like McDermott comes with distinct challenges. Organizational inertia and siloed data are primary risks. Data is often trapped within specific project teams or legacy software systems, making it difficult to create the unified data lake required for effective AI. Overcoming this requires strong, top-down mandate and investment in a centralized data governance and platform team.

Integration with entrenched processes is another hurdle. AI recommendations must be woven into existing, often manual, workflows of engineers, project managers, and field technicians. Change management and user-centric design of AI tools are critical; otherwise, even the most powerful models will be ignored. Finally, the high-stakes, low-margin environment tolerates little room for error. AI pilots must be carefully scoped to domains with clear metrics and contained risk, avoiding "big bang" deployments that could disrupt live, billion-dollar projects. A phased, proof-of-concept approach, starting with a single asset class or project type, is essential for building trust and demonstrating tangible value.