The biggest LNG boom in history will push organisations to their limits

In LNG, time really is money. A modern liquefaction facility can easily cost $30-40 billion and take three to five years to build.  Once operational, every week of delay can cost tens of millions of dollars.

Across the LNG value chain, engineering, procurement, and construction (EPC) firms, operators, and technology partners, are under immense pressure to bring these projects online on schedule, within budget, and to a standard that guarantees reliable performance and satisfies investors for decades to come.

The overarching opportunity lies in closing the execution and operational gap. As the industry evolves, moving away from legacy systems toward unified digital technology offers a powerful pathway to success. By embracing integrated data and continuous optimisation from the start, LNG stakeholders can maximise their returns and confidently lead the energy transition.

The data foundation question

Organisations across the LNG value chain are rich in data: sensor information, engineering data, maintenance records, and operational logs. But a lot of this data is inconsistently governed and can’t be accessed in a way that produces useful operational insight at speed.

Deloitte’s 2026 Oil and Gas Outlook identifies this as a defining challenge: firms face “shifting policies, rising costs, and new opportunities in LNG and digital transformation, requiring agility.”

A unified data environment tells businesses where the data lives, who has access, and how it’s been transformed. Without that, engineers and data scientists can’t work efficiently — AI models are only as good as the data underneath them.

Thai chemical giant SCG Chemicals represents a brilliant case in point. Its leadership set a “zero unplanned downtime” target — a vision that would have seemed untenable without the right technology.

By implementing prescriptive AI on top of a robust data platform, they increased plant reliability from 98% to 100% and achieved a 9x return on investment within six months of deployment. Their teams can now predict equipment health, monitor performance in real time, and drive continuous optimisation — all from a single platform.

In another example, Brazil’s AP Consultoria e Projetos adopted unified engineering platforms to tackle execution bottlenecks. 

By moving engineering workflows to the cloud, AP Consultoria created a single, shared data environment where civil, mechanical, piping, and instrumentation teams could work in parallel. Pipe support design was automated using AI models trained on historical engineering expertise, cutting analysis time by 90% and stress analysis review time by 60%.

Radical collaboration as competitive model 

Deloitte’s 2026 outlook notes that this year will likely see AI technologies “move from pilots to enterprise-wide deployment” as companies build on early successes. The firms driving that transition share a common trait — they aren’t locked into single-vendor ecosystems.

Radical collaboration means building open, agnostic platforms that bring ecosystems together rather than locking customers in. It means integrating operational data with enterprise data — ERP, engineering systems, geospatial data, weather — to build AI models that reflect the full complexity of the real world.

This entails connecting operational technology with IT across a single governed environment. Customers want flexibility across equipment, platforms, and ecosystems with data that stays secure, governed, and AI-ready.

The benefits are already being demonstrated across Asia Pacific. PETRONAS identified that fragmented data across multiple assets and locations was creating “high-value leakage” by slowing responses to equipment issues and increasing waste. By improving data integration and visibility, the company enabled earlier detection of potential failures, faster intervention, and more efficient asset utilisation, resulting in an estimated $30 million in savings through reduced unplanned downtime, lower waste, and improved environmental performance.

As the industry approaches a skilled labour cliff, AI enables operators to capture institutional knowledge, augment workforce capabilities, and maximise productivity without depending on expertise that is increasingly difficult to find and retain.