- By Munish Grover, Business Head of Plant Engineering, Europe.

The industrial world has spent the last decade investing heavily in Digital Twins. Across chemicals, refining, energy, hydrogen, and process industries, organisations have tried to create virtual representations of physical assets to improve performance, reliability, and decision-making.

The concept is powerful: connect engineering data, operational information, and asset performance into one digital environment.

In my view, the next question is more important than the concept itself: are Digital Twins truly improving engineering and operations, or are many of them still functioning as advanced visualisation platforms?

As Artificial Intelligence matures, we are entering the next phase of Digital Twins. This phase has the potential to move beyond digital representation and create active engineering intelligence platforms that support better decisions across the plant lifecycle.

The Conventional Digital Twin

Traditional Digital Twins have primarily focused on integrating engineering design data, real-time operational information, asset documentation, visualisation tools, and monitoring systems. These capabilities have created value. Operators can monitor assets more effectively, maintenance teams can access information faster, and engineering data is more connected than it was a decade ago.

The market momentum reflects this confidence. Grand View Research estimates the global Digital Twin market at approx. USD 35.82 billion in 2025 and projects it to reach approx. USD 328.51 billion by 2033, driven by Industry 4.0 adoption, predictive maintenance, and real-time asset monitoring.

However, scale of adoption does not automatically mean full engineering transformation. In many plants, engineers still spend significant time searching for the latest drawings, validating information, comparing documents, identifying inconsistencies, and assessing the impact of design or operational changes.

This becomes especially visible in brownfield environments. A small modification to a pump, compressor, utility line, or process system can affect P&IDs, line lists, datasheets, control narratives, layouts, pipe supports, maintenance access, hazardous area classification, and management of change records. A Digital Twin may show the asset clearly, but the engineering interpretation still depends heavily on experienced people connecting all the related information.

For me, this is the limitation of many conventional Digital Twins. They help us understand what is happening, but they do not always help us decide what should happen next.

The Rise of AI-Enabled Digital Twins

The next evolution of Digital Twins is being shaped by Artificial Intelligence. AI-enabled Digital Twins can begin to understand engineering context, relationships, and intent, rather than only representing a plant visually.

Imagine a Digital Twin that can support engineers by:

• Understanding P&IDs, process descriptions, and engineering specifications
• Identifying inconsistencies across engineering deliverables
• Assessing the impact of process modifications before implementation
• Retrieving relevant vendor documentation and operating history
• Supporting engineering reviews through natural language interaction
• Giving teams access to lessons learned from previous projects

This changes the role of the Digital Twin. It moves from being a repository of plant information to becoming a working engineering assistant.

For example, if an engineer is reviewing a compressor modification, an AI-enabled Digital Twin could help identify connected lines, affected instruments, previous maintenance issues, related safety studies, vendor documentation, and possible documentation gaps. The engineer still makes the judgement. The system helps bring the right context together faster.

That distinction matters. In plant engineering, decisions require technical accountability, safety awareness, and practical experience. AI can help reduce the time spent searching, reconciling, and validating information, giving engineers more time to focus on judgement and problem-solving.

Beyond Data Integration: The Knowledge Challenge

Historically, Digital Twin initiatives have focused heavily on connecting systems and integrating data. That foundation remains essential. Plant information must be structured, tagged, linked, governed, and maintained properly.

However, the next frontier is knowledge integration.

Engineering organisations possess years of expertise embedded in design standards, lessons learned, vendor documentation, operating procedures, safety studies, engineering calculations, reliability records, and maintenance history. A large part of this knowledge is also carried by senior engineers who understand why certain decisions were made and what risks need attention during future modifications.

The challenge is that this knowledge is often fragmented. It may sit across document repositories, spreadsheets, project folders, emails, site notes, and individual experience. When a project team needs it, the information may exist, but it may not be easy to find or interpret.

This is where AI-enabled Digital Twins can create real value. They can help make engineering knowledge searchable, traceable, reusable, and available in real time to project teams, operators, and decision-makers.

A younger engineer should be able to understand not only what the latest drawing says, but also why a design decision was made. A project team should be able to identify whether a new datasheet conflicts with older vendor information. An operations team should be able to connect recurring asset issues with future engineering modifications.

The true value of Digital Twins 2.0 will come from making engineering knowledge accessible and actionable, not only from storing more information.

Transforming the Engineering Delivery Model

Much of the discussion around Digital Twins focuses on assets and operations. I believe an equally important transformation is taking place within engineering delivery itself.

For decades, engineering delivery models have relied on a combination of onshore and offshore execution. Local teams stay close to customers, stakeholders, and project sites, while global engineering centres provide scalability and technical execution capability. This model has worked well, especially for multi-discipline plant engineering projects.

The constraint has often been information flow. Site context may not always reach distributed teams clearly. Offshore teams may work with incomplete background information. Onshore teams may spend time clarifying inputs and managing review cycles. Different disciplines may work from different versions of documents.

AI-enabled Digital Twins can help reduce these barriers by creating a shared digital engineering environment. Teams across locations can work with the same engineering context, project knowledge, operational history, and design intent. This can improve coordination, reduce assumptions, and make distributed engineering delivery more consistent.

At TAAL Tech, our role is more practical. Digital Twins need a strong engineering foundation: accurate drawings, reliable documentation, structured data, disciplined change control, and multi-discipline engineering coordination.

Through our onshore, offshore, and hybrid delivery models, we support customers with scalable engineering capacity, design updates, documentation alignment, and plant engineering execution. Customer-facing teams can remain close to clients and site requirements in Europe, while engineering teams in India provide flexible technical support and delivery depth.

As AI-enabled Digital Twins mature, this kind of delivery model can help customers strengthen the engineering backbone needed for digital readiness. Cleaner data, better document alignment, and stronger engineering governance will be essential for organisations that want to move from digital representation to engineering intelligence.

Supporting the Energy Transition

The need for this transformation is becoming more urgent as industries move through the energy transition. Hydrogen, carbon capture, sustainable fuels, electrification, and energy infrastructure projects are demanding faster execution, shorter development cycles, and more efficient use of engineering talent.

The investment scale is significant. The IEA expects global energy investment to reach approx. USD 3.3 trillion in 2025, with around USD 2.2 trillion going into clean energy technologies such as renewables, grids, storage, low-emissions fuels, efficiency, and electrification.

For plant engineering teams, this investment translates into complex project work. Many energy-transition projects must be integrated into existing facilities where space constraints, shutdown windows, safety requirements, legacy documentation, and operating continuity all influence engineering decisions.

Digital Twins enhanced by AI can help by giving teams better visibility into existing assets before modifications are made. They can support impact assessment, highlight affected systems, retrieve relevant documentation, and help teams identify risks earlier in the project lifecycle.

This does not remove the need for experienced engineers. It helps engineering teams use their expertise more effectively, especially when projects are complex, timelines are tight, and skilled engineering resources are under pressure.

The Future Is Engineering Intelligence

There is often concern that Artificial Intelligence may replace engineering expertise. My perspective is different.

The future belongs to engineers empowered by AI. Engineering judgement, safety leadership, creativity, and accountability will remain firmly in human hands. AI can support engineers by reducing the time spent searching, reconciling, validating, and interpreting information. It can help teams focus more energy on solving problems, improving designs, and delivering value.

The first generation of Digital Twins focused on creating digital representations of physical assets. The next generation will focus on creating digital engineering intelligence.

Organisations that combine engineering expertise, AI capabilities, Digital Twins, and globally connected engineering teams will be better positioned to deliver projects faster, operate assets more effectively, and preserve critical engineering knowledge for the future.

For me, the question is not only whether Digital Twins are valuable. The more important question is whether they are ready to support the way engineering decisions are actually made.