This article first appeared in Digital Edge, The Edge Malaysia Weekly on June 8, 2026 - June 14, 2026

In an industrial environment, reliance on reactive maintenance strategies comes at a significant cost as unplanned downtime can reduce asset productivity by 5% to 20%, while the average manufacturer experiences about 800 hours of equipment downtime annually.

These disruptions are estimated to cost as much as US$50 billion a year, according to a 2025 report titled “Expand digital transformation with the breadth and integration of Aveva’s digital twin solution”.

Businesses often face delays, inefficiencies and higher operational risks because data is incomplete, inconsistent or difficult to share between teams. These problems are compounded by poor information flow across the design, construction and operations stages, and become more challenging as companies expand across multiple sites.

“This is where companies need a solution that can unify data, harmonise processes and provide real-time, actionable insights. Digital twins, for instance, help reduce rework, improve collaboration and preserve lifecycle continuity, allowing organisations to deploy assets faster in response to today’s growing demands,” says Caspar Herzberg, CEO of industrial technology company Aveva.

A digital twin is a digital representation of a physical system, plant or piece of equipment that uses data, analytics and insights from multiple sources. By connecting real-time operational data, engineering models, documents and historical information on a single platform, it provides a comprehensive view of how an asset performs and interacts with its environment.

“Through a digital twin, users can visualise performance, monitor assets, optimise processes and make informed decisions faster. Digital twins are increasingly becoming a strategic priority for industrial organisations,” Herzberg said in his speech at the Aveva World 2026 conference in Milan, Italy, held from May 19 to 21.

According to the 2025 report, the global digital twin market is projected to reach US$149.81 billion by 2030. The report also says 35% of G2000 companies will employ supply-chain orchestration tools with digital twin capabilities by 2027.

Herzberg points out that the question is no longer whether organisations should adopt digital twins but how they can implement these at scale to generate long-term business value.

Canada-based nuclear facility Bruce Power, for one, saved 1,000 employee hours through faster data retrieval, achieved 15% fewer repeat walkdowns and field checks, and delivered 50% savings in cost and scheduling with Aveva’s industrial intelligence platform CONNECT. The platform provides dashboards and embedded artificial intelligence-driven analytics for faster decision-making from digital twins, as well as supports what-if scenario planning to enhance safety, optimise performance and manage risks.

“Some industries that will benefit from digital twins are the oil and gas and energy sectors due to the risks involved and the size of the assets. In energy-intensive industries, organisations can typically achieve savings of between 15% and 20%,” says Sébastien Ory, vice-president of Europe, the Middle East and Africa at Aveva.

“By creating a virtual representation of physical assets and processes, companies can reduce project timelines by as much as six to 12 months. The technology can also lower capital expenditure by around 10% of a project’s total value, generating significant savings and business value across a wide range of industries,” he said during a Q&A session at Aveva World 2026.

Headquartered in Cambridge, the UK, Aveva Group is a global provider of industrial software solutions. Founded in 1967 as the Computer-Aided Design Centre by the UK’s Ministry of Technology and the University of Cambridge, the company transitioned into a private entity in 1983 before being rebranded as Aveva in 2001.

Other than improving operational efficiency, digital twins also play a critical role in preparing organisations for industrial AI. It brings data from disparate systems into a single environment to establish the foundation needed for AI applications to learn and deliver meaningful business outcomes.

For industrial organisations, the pressure to adopt AI is intensifying. However, many continue to grapple with fragmented data, ageing infrastructure and complex regulatory environments.

“Data is the lifeblood of AI. In industrial spaces, the first data that you think of is time-series numeric data, sensor data, temperatures and pressures, and flow rates and all the types of things that can be recorded every minute, every second, every interval. Then they form patterns, and that’s what AI loves. AI is a pattern machine. That’s really what it is for, machine learning,” says Jim Chappell, global vice-president and head of AI at Aveva, tells Digital Edge on the sidelines of Aveva World 2026.

But many industrial organisations still operate with data trapped in silos across multiple systems, plants, departments and vendors. Information may be incomplete, inconsistent or locked within legacy infrastructure, making it difficult to establish a reliable foundation for AI.

As such, Chappell says successful industrial AI adoption is less about deploying more AI tools and more about building the digital foundations that enable these to work effectively.

“Don’t do it all at once. Take it step by step and make sure you have the right foundation elements before you do it. If a business is interested in AI and you’re interested in making a smart factory or smart plant, make sure you have the data, put the right processes in place and have a data collection mechanism with secure and easy access,” he says.

Moreover, the pace of AI adoption depends heavily on an organisation’s ability to invest in the necessary technology, people and processes, says Aveva Southeast Asia market leader Thomas Phang.

“For larger enterprises, it is easier for them to consolidate and get in the right technology, put in the right people in the right place, and this is where they are able to get all this information together. The more challenging ones are the mid-sized and smaller enterprises out there that need a lot of nudging and a lot of help, so that they can also get on the bandwagon,” he adds.

Phang says governments can play a role in helping small and medium enterprises (SMEs) adopt AI by supporting investments in digital infrastructure, workforce development and technology implementation.

According to the Malaysia Digital Economy Corporation in its Belanjawan 2026: Digital Economy Snapshot report, more than 60% of Malaysian businesses remained at a basic level of digitalisation in 2025, highlighting a substantial readiness gap, particularly among SMEs and rural entrepreneurs.

These findings indicate the need for sustained interventions that address both capability and accessibility challenges in digital transformation. To bridge the gap, the government has expanded allocations for digital centres and introduced programmes such as Maju Usahawan Madani 2.0, alongside targeted tax incentives for SMEs investing in AI and cybersecurity training.

“Companies do not adopt technology for the sake of technology. They need to be convinced that the technology will help improve productivity and positively impact their bottom line,” says Phang.

“Unlike the big enterprises, there are a couple of things that a typical company needs to consider. First of all, they need to have a strong data architecture. They also need to adopt digital workflows that align with their business processes. The third is to have a certain level of standardisation.”

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