Construction firms across Asia-Pacific are adopting more digital tools, but the useful question is no longer whether the sector will digitize. It is where technology can remove friction from work that still depends heavily on handoffs between offices, job sites, suppliers and subcontractors.
The 2026 State of Digital Adoption in the Construction Industry, produced by Deloitte Access Economics for Autodesk, surveyed 954 construction and engineering businesses across Australia, Hong Kong, India, Japan, Singapore and Vietnam. It found that 56 percent were using data analytics tools, 50 percent used cloud-based construction management software and 47 percent used mobile field applications.
Those figures show that digital adoption is no longer confined to a small group of large contractors. Yet adoption remains uneven, particularly when software has to work across the messy conditions of a live project.
Where digital tools show up on site
Estimating is one example. Digital systems can reuse pricing, material and project data from previous work instead of forcing an estimator to rebuild every proposal from scratch. The gain comes from reducing repetitive work and making past project information easier to retrieve.
Field coordination is another. Singapore’s Building and Construction Authority has made Integrated Digital Delivery a major part of its built-environment transformation agenda. Shared digital systems can bring progress tracking, inspections, safety information and material records into a common workflow so supervisors have a more current view of what is happening on site.
Procurement can benefit from the same visibility. A 2026 study of construction SMEs reported that firms using combinations of mobile applications, RFID tracking and cloud-based procurement tools reduced order-processing times by roughly 30 to 40 percent and material waste by 15 to 25 percent. The study also found that phased adoption and targeted training were important to making the transition manageable for smaller companies.
The underlying benefit is straightforward. When teams can see what has been ordered, delivered and used, they rely less on fragmented records and informal updates.
Automation is reaching physical workflows
Digital adoption is also moving beyond administrative software. Singapore’s Building and Construction Authority now supports robotics, automation and digital solutions through the Built Environment Productivity Solutions Grant. Its current tranche runs from April 2026 to March 2031 and offers qualifying SMEs support for adopting approved productivity solutions.
AI is beginning to enter more of these workflows as well. BCA has identified AI use cases across the built environment, including design, project management and operational processes. In practice, the value will depend on whether AI has access to reliable project data and fits into the way site teams already make decisions.
TNGlobal has also reported on the region’s wider construction-technology adoption, with Vietnam and Singapore among the stronger adopters in the 2026 Autodesk and Deloitte research.
Skills and process remain the harder barriers
Buying software does not automatically change the way a project operates. The Deloitte Access Economics research found that only 16 percent of surveyed businesses had advanced digital capability. Less than half said their on-site teams had access to real-time project data.
Skills remain a recurring constraint. A 2024 study of 248 construction professionals in Vietnam identified resistance to established ways of working among the strongest barriers to digitalization, along with technology costs, lack of market information and the absence of standardized practices.
These barriers are familiar to smaller contractors. A company may have spreadsheets for estimating, a messaging application for field coordination, separate accounting software and paper records for other processes. Introducing one more platform can create another silo unless somebody is responsible for how information moves between systems.
Data quality also becomes more visible once firms start automating. Poor naming conventions, inconsistent site records and incomplete supplier data that were tolerable in a manual process can produce unreliable outputs when fed into analytics or AI tools.
Start with one process that creates measurable friction
For smaller firms, a phased approach is usually easier to absorb than a full technology overhaul. The first target might be field reporting, procurement or document control, depending on where delays and rework are most visible.
The point is to fix a specific process and measure what changes. That could mean shorter ordering cycles, fewer missing records, lower material waste or faster progress reporting. Once one workflow is stable, the same data can support more advanced analytics and automation.
Government co-funding can reduce the initial cost, but the harder work still sits inside the company. Someone has to own the rollout, train the people using it and decide which records become the reliable source of truth.
For construction businesses across Asia-Pacific, digital adoption will increasingly be part of normal project delivery. The firms that get the most from it will be those that connect technology spending to concrete operating problems, clean up the data underneath those processes and expand only after the first change is working.

Lenea Morrison is the CEO of Certified Material Testing Products (Certified MTP), a women-owned provider of materials testing equipment and laboratory solutions for the construction and engineering industries. She focuses on quality, innovation and customer-driven solutions for laboratories, contractors, engineers and quality professionals.
Editor’s note: This contributed article has been lightly edited for clarity and TNGlobal house style. The substance of the author’s contribution has been preserved.
Share your perspective: TNGlobal welcomes contributed insights and expert commentary from across Asia’s technology and innovation ecosystem. Submit a contribution for editorial consideration, or explore more conversations in our TNGlobal INSIDER and TNGlobal Q&A and Interviews archive.
Feature image: Ben Allan on Unsplash
The bottleneck in enterprise AI has moved from writing code to verifying it

