Embracing responsible AI to transform infrastructure development in ASEAN

Using AI to unlock sustainable growth

Mott MacDonald’s David Boyland, Maria Mingallon and Derek Murray explore how responsible AI can revolutionise infrastructure development across the ASEAN region, ensuring projects are not only efficient but also environmentally and socially responsible.

As ASEAN continues to grow and urbanise, the region faces critical challenges: the digital divide and an increasing infrastructure deficit. These issues threaten to hinder progress and widen inequalities. However, AI and digital technologies, when implemented responsibly, can hold the key to unlocking a brighter future. In fact, AI could uplift ASEAN GDP by 10-18%, translating to up to USD 1 trillion by 2030.¹

A tale of two realities

In ASEAN, the digital landscape is marked by stark contrasts. Countries like Brunei Darussalam, Malaysia and Singapore boast high internet penetration rates of over 97%, while Cambodia, Indonesia and Lao PDR lag significantly behind with rates as low as 56.7%.²

The divide is exacerbated by the regional needs for digital infrastructure, such as data centres.³ Despite these disparities, the demand for AI-ready data centres is surging, driven by rapid digital transformation and increasing adoption of artificial intelligence.

The surge in AI-ready data centres

Significant investments are being made in AI infrastructure, especially in data centres, to meet the increasing demand for advanced computing power.

Malaysia is leading with an investment of USD 15 billion, followed by Singapore with USD 9 billion and Vietnam with USD 6 billion.³ This expansion aligns with the region’s broader strategy to enhance AI capabilities and meet increasing computational demands.

However, ASEAN remains underpenetrated in data centre supply compared to more developed markets like China, South Korea, and Japan. Training a single generative AI model can use as much energy as 130 US homes in one year.⁴ 

In ASEAN, where access to renewable energy is variable, balancing high energy demands with sustainability targets is a significant challenge. The demand for data centre computing power exacerbates the need for investment in reliable end-to-end infrastructure.

Infrastructure deficit – the road ahead

The Asian Development Bank (ADB) highlights significant infrastructure needs in ASEAN, particularly in transportation and energy. It estimates that ASEAN requires a USD 2.8 trillion investment between 2016 and 2030,⁵ which is about 5.7% of GDP, in sectors such as transport, power, telecommunications, water supply and sanitation.

Currently, ASEAN’s infrastructure investment is around 2.6% of GDP, leaving a 3.1% gap needed to sustain economic growth and address climate change mitigation and adaptation. In the energy sector, the ASEAN Interconnection Masterplan Study (AIMS) III estimates that USD 770 billion is needed to develop the ASEAN Power Grid (APG) and meet renewable energy targets for 2025. These targets include 44.5 GW of solar and 14.3 GW of wind capacity.⁶ 

Despite having abundant natural resources for renewable energy, ASEAN faces challenges in infrastructure development and grid integration. Air transport infrastructure also faces challenges, with significant growth in international and domestic air passengers necessitating improvements to support increasing traffic.

AI’s role in engineering: innovating for efficiency

Optimising capital expenditure

Reducing capital expenditure (CapEx) is vital for ASEAN infrastructure projects. A McKinsey & Company report indicates that using digital technologies can cut project costs by up to 45%.⁷ This significant reduction is achieved through improved project planning, better resource allocation and enhanced collaboration among stakeholders.

One of the key areas where technology plays a vital role is in optimising design processes. A notable example of this is our use of AI to automate geotechnical data processing for design purposes.

At a major ASEAN airport hub, we developed in-house solutions and partnered with a tech provider to automate data processing. This AI-driven solution not only reduced about 25% of the design costs but also enhanced the efficiency of the design process by digitising borehole logs, performing soil classification and integrating geospatial data analysis. This approach enabled optimisation of the design process, ensuring better resource allocation and improved project outcomes.

Accelerating project timelines

Speeding up project timelines is essential for reducing overall costs and improving delivery efficiency. The Master Plan on ASEAN Connectivity 2025 (MPAC 2025) highlights the importance of coordinating resources across the full lifecycle of infrastructure projects.

Integrating digital technologies, such as AI-driven project management tools, allows infrastructure projects to be completed more quickly and efficiently, reducing delays and cost overruns.⁸ This approach ensures that projects are not only faster but also more resilient and sustainable.

Enhancing operational efficiency and asset management

A common issue with renewable energy is the inconsistency of variable renewable energy (VRE), which demands improvements in grid flexibility to accommodate more VRE inputs.² Artificial intelligence can manage this inconsistency by providing advanced digitalisation, demand response management and storage solutions.

AI-driven technologies optimise grid flexibility for integrating more VRE inputs and enhance predictive analytics for better forecasting and maintenance, ensuring a reliable and efficient energy infrastructure. Mott MacDonald leverages AI and machine learning to more precisely forecast electricity demand enabling grids to operate with a greater degree of reliability confidence.

Typically, AI improves forecasting accuracy by 25%, which feeds through to a reduction in ancillary services cost.⁹ Savings come principally from fuel efficiency and reduced need for reserve generating capacity, and reduced grid disruption and restoration costs.

We have partnered with Above Surveying to use AI technology to deliver enhanced quality and efficiency of solar plant design, construction and operation by providing accurate data and digital models. For asset management, AI-driven predictive analytics help forecast when maintenance is needed, which means less downtime and longer-lasting assets. 

Climate-resilient smart cities

AI also plays a pivotal role in the ability for smart cities to tackle climate change. In Singapore, AI-driven initiatives are helping to optimise energy use, reduce emissions and predict environmental impacts with unprecedented accuracy.¹⁰ AI-enabled strategies are being used in Jakarta to enhance climate change adaptation and address the city’s vulnerability to flooding and sea-level rise.¹¹

In Bangkok, a smart flood management system that integrates advanced technologies such as remote sensing, hydraulic modelling and machine learning to provide accurate flood predictions has been designed and deployed by our team.

A key component of the system is the use of AI for accelerated modelling. Unlike traditional hydraulic models, which can take up to 30 hours to predict flooding for multiple rain events, the AI-driven surrogate model can perform the same task in just 30 seconds.¹²

These initiatives illustrate AI’s involvement in enhancing urban resilience, quality of life and sustainable development in ASEAN cities amid the challenges posed by climate change.

Paving the way for a bright future

ASEAN faces a significant challenge with inadequate infrastructure, but the integration of AI and digital technologies promises to revolutionise development in the region. The success of this transformation depends on empowering individuals with the necessary skills and knowledge to harness these advancements. With 88% of knowledge workers in Southeast Asia already using generative AI, there is a strong foundation to build upon.¹³

However, bridging the gap between employee readiness and leadership hesitancy is crucial for progress. Establishing trust in AI requires robust policies that ensure responsible and ethical deployment. At Mott MacDonald, we have been leveraging AI to improve costs, time, safety, and carbon efficiency. Our commitment to responsible AI is underpinned by rigorous protocols and frameworks for information management, ensuring data security and fostering long-term success.

Leaders in ASEAN must embrace responsible AI, driving efficiency and innovation in the infrastructure sector. By fostering a culture of continuous learning and adaptation, the region can unlock the full potential of these technologies and pave the way for a brighter, more sustainable future.

Author's note: The first draft of this article was created using Copilot for Microsoft 365, which assisted in finding use cases, statistics, and evidence on the implementation of AI technologies in ASEAN.

1. The Quest Toward Developing an AI Governance in ASEAN, ASEAN for Business Bulletin, 2024
2. ASEAN Key Figures 2024, The ASEAN Secretariat, 2024
3. Data Centres in SEA: Mapping key sustainability trends and milestones - Southeast Asia Infrastructure
4. How much electricity does AI consume? The Verge, Feb 16 2024
5. Meeting Asia’s Infrastructure Needs, Asian Development Bank, 2017
6. ASEAN Interconnection Masterplan Study (AIMS) III, ASEAN Centre for Energy, 2024
7. Navigating the digital future: The disruption of capital projects, McKinsey, 2017
8. Master Plan on ASEAN Connectivity 2025, Association of Southeast Asian Nations, 2025
9. Grid balancing using machine learning, Mott MacDonald, 2022
10. The role of Green AI in Singapore’s digital sustainability journey, Infocomm Media Development Authority, 2025
11. Smart Cities with AI for Flooding Management A Case Study of Jakarta, Katy Salamati, SAS, 2024
12. Bangkok Smart Flood Management, Mott MacDonald
13. Microsoft and LinkedIn release the 2024 Work Trend Index on the State of AI at Work in Asia Pacific, Microsoft, 2024