Industrial AI+XR Digital Twins in India and Asia: How Energy Companies Can Move From Pilots to Production

Artificial intelligence is moving from experimentation into India's and Asia's energy sector. Oil and gas companies are applying AI to predictive maintenance, inspection, process optimisation, asset management and workforce operations. Utilities are exploring AI for grid and asset intelligence. New energy projects are adding digital technologies from the design stage.

Yet a successful AI pilot is not the same as production-scale industrial AI.

A pilot can operate with limited data, a controlled environment and a small technical team. A live refinery, offshore platform, petrochemical complex or power plant cannot. Production AI must work with legacy OT systems, engineering data, cybersecurity controls, safety procedures and skilled workers. It must also deliver measurable operational value. This is where the combination of industrial AI, XR and digital twins becomes important.

For energy companies in India and Asia, the next challenge is not proving that AI works. It is connecting AI to the physical asset, the engineering model and the people responsible for operating it.

Why energy companies are moving from AI pilots to production

The regional AI conversation is changing.

IBM's research on India's AI economy highlights strong AI experimentation while pointing to the need to translate adoption into broader economic value. Bain's energy research similarly identifies a shift toward focused, end-to-end AI applications rather than disconnected experiments. For energy leadership teams, the question is therefore changing. It is no longer:

Where can we test AI?

It is:

Which operational problems can AI solve reliably, repeatedly and at scale?

That distinction matters across oil and gas like refineries, upstream assets, petrochemical plants, LNG facilities, power stations and other process-intensive energy infrastructure.

What prevents industrial AI from reaching production?

1. Industrial data is fragmented

Energy assets generate data across multiple systems. This can include:

• CAD and BIM models
• SCADA and DCS systems
• IoT sensors
• EAM and CMMS platforms
• inspection records
• maintenance history
• engineering documents
• photographs
• operator knowledge

The problem is not simply data volume. It is context. A vibration anomaly has limited operational value without knowing which equipment generated it, where that equipment is located, what its design specification requires and which maintenance procedure applies. Industrial AI therefore needs contextualised data, not just more data.

2. Legacy OT cannot simply be replaced

Many energy facilities across Asia combine decades-old control infrastructure with newer digital platforms. Replacing the OT environment is rarely a realistic starting point. Production AI must instead coexist with existing systems while meeting requirements for:

• OT/IT integration
• cybersecurity
• network segmentation
• edge computing
• latency
• system availability
• data sovereignty
• secure or air-gapped environments

This makes industrial AI architecture fundamentally different from deploying a conventional enterprise AI application. The AI model is only one component of the solution.

3. AI lacks physical context

An AI system can identify an abnormal condition. But an engineer still needs to know:

• Where is the asset?
• What connects to it?
• Can a technician physically access it?
• What does the latest engineering model show?
• Which procedure applies?

This is where a digital twin becomes more than a visualisation tool. A connected industrial digital twin can associate engineering geometry, asset information and operational data with the physical environment. The objective is simple:

AI insight → asset context → human decision → field action

4. Workforce readiness becomes part of AI deployment

Industrial AI changes how people work. Operators need to understand AI recommendations. Technicians need access to relevant information. Maintenance teams need to know how new workflows affect existing procedures. Training therefore cannot remain separate from digital transformation.

• VR can allow workers to rehearse equipment procedures, maintenance tasks and emergency scenarios without exposing them to live-plant risks.
• AR can bring contextual instructions into field environments.
• MR can align digital engineering information with physical equipment.

This creates a human layer around industrial AI.

Suggested reads: AR vs VR vs MR vs XR

5. Technical accuracy is not the same as business value

An AI model can have excellent accuracy and still fail as an industrial investment. Energy leadership teams need operational metrics. For example:

• Unplanned downtime avoided
• Inspection cycle time reduced
• Mean time to repair improved
• Maintenance productivity increased
• Rework avoided
• Training time reduced
• Time to competency improved
• Safety readiness strengthened

The business case should be defined before the pilot begins.

What India's and Asia's energy leaders can learn from the market

The opportunity is not to reproduce generic AI use cases. It is to understand how AI must operate inside large, distributed energy businesses.

ONGC: connecting intelligence with field execution

ONGC operates complex upstream assets across onshore and offshore environments. Its operating environment illustrates an important challenge for energy companies: detecting an equipment issue is only the beginning.

The operational value appears when intelligence can be connected to the asset, engineering information and the procedure required for field intervention. A connected workflow could look like:

AI anomaly → asset location → digital twin → maintenance procedure → technician action

Industrial XR & AI can strengthen this final step by presenting relevant information through immersive training or field interfaces. The goal is not to replace engineering judgement. It is to reduce the distance between machine-generated insight and physical execution.

Reliance Industries: extending engineering data beyond design

Large refining and petrochemical projects by RIL generate extensive engineering information. The strategic opportunity is to reuse that information after design. A connected 3D asset can support:

• Design reviews
• Construction validation
• Operator training
• Inspection
• Maintenance
• Digital work instructions
• Remote collaboration

This creates a stronger digital thread between engineering and operations. For companies managing complex brownfield and greenfield assets, the question is not simply whether they have 3D models. It is whether those models can become operational assets. VizExperts' VR design review approach demonstrates one application of engineering models before physical execution.

PETRONAS: distributed expertise across Asian energy assets

PETRONAS provides a useful Asian perspective. Its operations span upstream, downstream and LNG-related activities across geographically distributed assets. For such organisations, expertise cannot always be physically available where it is needed. Digital twins, AI and XR can create a connected operating model.

• An engineer can review an asset remotely.
• A technician can access contextual information in the field.
• A trainee can rehearse a procedure before entering a live environment.

The value comes from connecting these activities rather than treating each technology as a separate project.

PTT: technology adoption must include workforce capability

Thailand's PTT operates across a broad energy and industrial ecosystem. Its example reflects another regional reality. Digital transformation does not end when software is deployed. People must be able to use it safely and consistently. Immersive training can help workers practise equipment procedures, maintenance sequences and emergency scenarios before performing them on operating assets.

This makes workforce readiness part of the digital transformation strategy.

How industrial digital twins strengthen AI

A digital twin should not be treated as a 3D replica alone. Its value increases when it connects the physical asset with engineering, operational and procedural information. For industrial AI, three layers are particularly important.

• Spatial context: AI outputs can be associated with the actual location and configuration of an asset.
• Engineering context: Operational information can be connected with CAD, BIM and engineering specifications.
• Operational context: Asset information can be presented alongside inspection, maintenance and procedural data.

Together, these layers create a more useful operating chain:

Data → AI → digital twin → human decision → operational action

That is the architecture energy companies should focus on.

Where XR fits into the industrial AI stack

AI generates intelligence. Industrial XR helps people understand and act on that intelligence. VR can support operator training, safety simulations, maintenance rehearsal and immersive design reviews. AR can support inspection, maintenance and field work instructions. MR can align digital engineering information with physical equipment. Smart glasses can support hands-free instructions and remote assistance.

VizExperts' AI-powered AR work instructions for industrial plants illustrate how contextual information can be delivered closer to where field work happens. The larger opportunity is to reuse the same digital asset across multiple workflows instead of creating isolated applications.

A practical roadmap for energy companies

Energy companies do not need to digitise every asset before seeing value. A staged approach is more practical.

Step 1: Choose one high-value operational problem

Start with a measurable business problem. Potential starting points include:

• Predictive maintenance
• Visual inspection
• Asset reliability
• Operator training
• Engineering review
• Maintenance guidance
• Construction quality

Step 2: Assess data readiness

Map the required data sources. Identify:

• Data ownership
• Data quality
• Access requirements
• Integration dependencies
• Security constraints

Step 3: Add asset and engineering context

Connect AI outputs to the relevant equipment, engineering model and operating procedure. This is where an industrial digital twin can become a foundation layer.

Step 4: Put intelligence where work happens

Do not leave AI recommendations inside an analytics dashboard. Deliver information through the workflow. That may mean a:

• Control room
• Engineering workstation
• Mobile device
• VR environment
• AR headset

Step 5: Measure operational impact

Step 6: Scale the architecture, not isolated pilots

A successful pilot should become a reusable capability. The goal is not hundreds of disconnected AI applications. The goal is an industrial architecture that can support multiple assets, teams and use cases.

Why this matters beyond LNG at Gastech 2026

Gastech 2026 is an important backdrop for this conversation, but the opportunity extends well beyond LNG. The energy industry is simultaneously dealing with:

• Artificial intelligence
• Digitalisation
• Decarbonisation
• Hydrogen
• Energy infrastructure
• Operational efficiency
• Workforce transformation
• Asset reliability

These themes are connected.

• A new energy facility still requires engineering.
• It still needs trained operators.
• It still requires inspection and maintenance.
• It still needs safe field execution.

That is why industrial AI should not be treated as an isolated software initiative.

For India and Asia, the challenge is even broader. New infrastructure is being developed alongside large installed asset bases. Companies therefore need digital technologies that can work across both greenfield projects and brownfield operations. Industrial AI, digital twins and XR provide a way to connect those environments.

Five questions energy boards should ask before funding another AI pilot

Before approving another industrial AI programme, leadership teams should ask:

1. What operational problem are we solving?

A technology demonstration is not a business case.

2. Is the required industrial data available and trustworthy?

Sophisticated AI cannot compensate for poor data foundations.

3. Where will the AI output be used?

An insight has value only when it reaches the right person at the right time.

4. Can the solution operate within our existing IT and OT environment?

Production deployment requires security, integration and availability.

5. Can the architecture scale across assets?

A pilot that cannot be reused creates another technology silo.

From AI pilots to connected industrial intelligence

India and Asia's energy sector is entering a more demanding phase of AI adoption. The first phase was experimentation. The next phase is integration. AI needs to connect with engineering data, operational systems, digital twins and human workflows.

For an upstream operator, that could mean connecting an equipment anomaly to the technician responsible for intervention. For a refinery, it could mean using the same engineering data for design review, training, inspection and maintenance.

For a new energy project, it could mean establishing a digital foundation before commissioning. For the workforce, it could mean practising critical procedures before entering a live environment. The technology stack may include industrial AI, digital twins, computer vision, IoT, VR, AR and MR. But the objective remains straightforward: Turn industrial data into better decisions, and better decisions into measurable operational outcomes.

That is how energy companies in India and Asia can move from AI pilots to production.

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Frequently asked questions

What is industrial AI?

Industrial AI applies artificial intelligence to industrial data, equipment, engineering systems and operational workflows. Common applications include predictive maintenance, computer vision, asset management, process optimisation and workforce training.

Why do industrial AI pilots fail to reach production?

Common barriers include fragmented data, legacy IT and OT systems, cybersecurity requirements, limited industrial context, workflow integration and unclear business value.

How can digital twins support industrial AI?

Digital twins provide spatial and asset context for AI outputs. They can connect engineering models, equipment information and operational data so teams can interpret and act on AI-generated insights.

What role does XR play in industrial AI?

VR, AR and MR provide immersive or hands-free interfaces for AI and digital-twin information. Applications include training, design reviews, inspection, maintenance and field work instructions.

How should an energy company start an industrial AI programme?

Start with one high-value operational problem. Assess data readiness. Connect AI to asset context. Integrate the output into an existing workflow. Measure business impact. Then scale the architecture across additional assets and use cases.