AI in Offshore EPC: Contracts, Coverage, and Compliance Demand a Single Source of Truth

Field Development: AI's Growing Role in EPC/EPCM-And the Risks You Must Manage

AI adoption in offshore construction and operations is climbing, bringing fresh contractual, insurance, and regulatory exposures. Generative AI can add value across EPC/EPCM workflows, but only if teams enforce a single source of truth (SSOT), clear oversight, and measurable accountability.

A 2025 industry survey of operators, service companies, and EPCs shows where effort is going: 43% predictive maintenance, 31% seismic data interpretation, 17% drilling optimization, and 10% reservoir management. The biggest barriers: integrating with existing systems, workforce skills, data security, and high costs.

How AI Is Enhancing Safety

• Computer vision for equipment monitoring
• Real-time data analytics
• Automated inspection systems
• Predictive risk assessment

EPC/EPCM: Where AI Fits in the Scope of Work

AI is being considered for supply chain, materials procurement, project design/management, QA/QC, and detecting faulty installs. Early deployments often trigger schedule and budget friction due to first-use learning, data readiness, SSOT setup, and HSE alignment.

Given 2024 offshore EPC awards of about $52B and a 2025 outlook near $54B, contract structures must now reflect AI's presence-especially where contractors carry schedule and cost exposure (EPC) versus advisory-heavy models (EPCM).

Practical SOW Definition for AI

• List AI use cases by phase: engineering, procurement, construction, installation, commissioning, operations, predictive maintenance, and decommissioning.
• For each use case, define inputs (datasets, sensors), outputs (reports, alerts, decisions), performance targets, and acceptance tests.
• Specify human-in-the-loop checkpoints and escalation paths.
• State cybersecurity, privacy, and model security controls aligned to a recognized standard (see NIST AI RMF).

The Non-Negotiable: Single Source of Truth (SSOT)

SSOT must be spelled out in the contract. It is the authoritative data backbone for design specs, construction tolerances, regulatory requirements, equipment ratings, and operating envelopes.

• Define the SSOT owner, update cadence, version control, and audit trail.
• Document model training sources, fine-tuning procedures, and access controls.
• Require traceability: every AI recommendation links back to SSOT inputs and model version.
• Set a defect-analysis protocol: if output is wrong, determine whether the cause was SSOT, training, integration, or model behavior.

Oversight and QA for AI Outputs

• Appoint named roles to review prompts, outputs, and decisions (engineering, QA/QC, HSE, operations).
• Implement an exception register for hallucinations, anomalies, and model drift; require time-bound notification to counterparties.
• Set thresholds where AI recommendations cannot proceed without human approval.
• Align AI workflows with existing MOC, permit-to-work, and stop-work authority.

Contractual Risk Allocation-Updated for AI

Knock-for-knock remains useful, but AI introduces new fault lines: wrong recommendations, data bias, stale models, and integration failures. Contracts need explicit treatment for these scenarios.

• Attribute error sources: SSOT errors, model training defects, inference issues, integration/configuration mistakes, and misuse.
• Assign responsibility, indemnities, and caps for each source. Back-to-back these terms with subs and AI vendors.
• Mandate minimum QA for data providors; negligent data supply should carry consequences.
• Require cooperation obligations and access for forensic analysis after an incident.

Clause Starters You Can Use

• SSOT governance: ownership, change control, versioning, data lineage, audit rights.
• AI performance: measurable KPIs, test environments, shadow-mode periods, acceptance criteria.
• Error handling: notification windows, rollback plans, safe states, interim manual controls.
• Liability: distinct limits for AI output errors vs. traditional workmanship; carve-outs for gross negligence and intentional acts.
• IP/data: who owns prompts, outputs, model improvements; restrictions on training with project data.

Insurance: What the Market Is Offering and What to Ask For

Specialized policies are emerging. At Lloyd's of London, products backed by firms such as Armilla and Chaucer signal coverage for hallucinations, model drift, mechanical failures, and related liabilities. Other market entrants (e.g., Testudo) are building AI underwriting data and methods, supported by the Lloyd's Lab accelerator (Lloyd's Lab).

For risk managers and brokers, align AI exposures across property, E&O/PI, GL, cyber, and business interruption.

• Confirm treatment of AI-specific perils: hallucination, drift, data corruption, control-system misoperation, and integration faults.
• Check triggers: does coverage respond to financial loss from wrong recommendations without bodily injury or property damage?
• Scrutinize exclusions tied to experimental tech, unapproved use cases, or unvetted training data.
• Require cooperation clauses for incident response and model forensics; ensure vendors comply.
• Stress-test limits against worst credible scenarios (schedule slippage, remediation, rework, downtime).

Regulatory Watch: Funding, Export, and Ethics Expectations

New federal measures provide incentives for US-based AI development and data center infrastructure while restricting involvement by prohibited foreign entities where federal funds are used. Executive orders target faster data center permitting, export of US AI technology, and unbiased AI practices within federal agencies.

Action for EPC/EPCM parties: track funding eligibility, confirm foreign ownership restrictions in your stack, and align export controls with vendors and data flows. Codify this in supplier onboarding and contract schedules.

Implementation Playbook (First 90 Days)

• Form an AI governance squad: legal, risk/insurance, IT/OT security, engineering, operations, HSE, procurement.
• Select 2-3 high-value, low-criticality use cases; run in shadow mode with clear acceptance gates.
• Stand up SSOT and a model registry; enforce change control and audit trails.
• Pilot incident logging for hallucinations/drift; define rollback and manual overrides.
• Amend master service agreements: SSOT duties, oversight, error attribution, and insurance wording.
• Brief your broker; map AI exposures across policies; request endorsements for AI-specific perils.
• Upskill key roles (engineering, operations, PMO, risk). If you need structured paths by job, see these curated AI learning tracks.

HSE Integration: No Conflicts, No Surprises

• Make it explicit: AI cannot override HSE rules or regulatory requirements.
• Require human sign-off for any recommendation affecting critical safety functions.
• Log all AI-influenced decisions affecting permits, isolations, or barrier health.

Vendor Management: Back-to-Back and Verifiable

• Flow down clauses to AI vendors and subs: SSOT compliance, security standards, incident cooperation, and audit rights.
• Set model update policies and deprecation timelines; prohibit unapproved retraining with project data.
• Define IP boundaries for prompts, outputs, and model improvements to avoid ambiguity later.

Signals from the Field

Major EPCs are using AI to analyze project data, anticipate schedule risk, and improve forecasting. Surveys show strong investment intent from leading construction companies, with vendors offering cloud tools for unsolved engineering problems and EPC use cases.

The takeaway: AI can be additive, but only under disciplined data governance, explicit oversight, and upgraded contracts and insurance. Put the SSOT at the center, make accountability testable, and keep people in control where it matters most.