Author name: Editorial Team

How AI-driven, human-centred manufacturing will shape SEA region in 2026 Marcelo Tarkieltaub, Regional Director, Southeast Asia, Rockwell Automation Southeast Asia’s manufacturing sector had a challenging start to 2025, but a positive end. As the uncertainty settles, 2026 presents an opportunity to move forward with intelligence, foresight and the ability to scale digital capabilities across diverse markets. As global supply chains continue to rebalance and the region strengthens its position in electronics, automotive, food production and advanced materials, manufacturers are asking a new question: How do we build operations that can anticipate, not just react? Four forces will shape this next chapter: predictive capability, edge intelligence, workforce evolution and embedded sustainability. Underpinning these forces is the accelerating use of AI across Southeast Asia’s manufacturing sector, not as a standalone lever but an enabler for smarter decisions, faster responses and more adaptable operations.  Together, these shifts reflect a broader regional movement toward adaptive, insight-driven manufacturing. While countries such as Singapore and Malaysia offer early examples, the implications span the entire Southeast Asian landscape. Predictive manufacturing becomes a regional differentiator Predictive manufacturing is fast becoming the region’s competitive advantage. Manufacturers across ASEAN are accelerating their use of digital twins, simulation environments and AI-enabled forecasting to anticipate issues before they occur. Several factors are driving this shift: Rockwell Automation’s 10th Annual State of Smart Manufacturing Report highlights that 94% of APAC manufacturers are investing in or planning adoption of AI/ML tools. Predictive technologies are at the center of these investments, reflecting a global shift from manual troubleshooting to data-driven optimization. Research reinforces this momentum, wherein predictive maintenance practices in 2025 shows consistent reductions in downtime, energy use and maintenance costs across digitally advanced plants.  Early movers across Southeast Asia are already applying predictive insights to optimize equipment lifecycles, balance energy loads and simulate production decisions before making physical adjustments. This capability allows manufacturers to navigate uncertainty with greater confidence, an advantage that will grow more critical as supply chains adjust across the region. Edge intelligence accelerates smart manufacturing at scale While cloud platforms remain essential for enterprise-wide governance and analytics, the next wave of industrial gains in Southeast Asia will be unlocked at the edge. Edge intelligence enables data to be processed directly on the production floor, supporting faster, more accurate decisions. IDC’s Asia/Pacific Future of Operations Survey estimates that 40% of operational data in the region will be generated and processed at the edge by 2027. This reflects the realities of Southeast Asian manufacturing: distributed production networks, varying connectivity environments, and a mix of advanced and legacy equipment. Edge analytics is particularly effective for: Manufacturers are increasingly using a blended approach, where big picture analysis happens in the cloud while fast, real-time decisions happen at the factory. This hybrid architecture is becoming a norm in markets such as Vietnam, Thailand, Malaysia and Indonesia to modernize at their own pace while maintaining unified oversight. For enterprise IT leaders, this signals a strategic shift: edge is no longer a hardware consideration but a foundational element of digital resilience. People will define the success of digital factories Despite rapid advances in automation and AI, human capability remains central to manufacturing competitiveness. A recurring challenge across ASEAN is workforce readiness: senior technicians are retiring, younger talent is gravitating toward digital-first industries, and new technologies require new skills. But across the region, manufacturers are reframing workforce transformation from a labor shortage problem into a skills-acceleration opportunity. Three developments are gaining traction: The latest Rockwell State of Smart Manufacturing Report indicates that 42% of Asia Pacific manufacturers are using digital tools to redesign roles and create more engaging work environments. This underscores the region’s broader pivot toward workforce augmentation, not replacement. Sustainability becomes an operational lever, not a reporting task Southeast Asian manufacturers are increasingly integrating sustainability into production, moving beyond compliance into real-time operational optimization. AI-enabled energy orchestration, automated load balancing and waste-reduction analytics are allowing factories to reduce consumption while maintaining throughput. As customers tighten expectations around transparency, sustainability performance is increasingly intertwined with competitiveness. The manufacturers that treat sustainability as a measurable, data-driven performance metric will lead the next phase of regional growth. A predictive, connected and people-centred future Southeast Asia’s manufacturing sector will continue to diversify in 2026, but one theme will cut across all markets: intelligence will define advantage. Predictive tools will shape strategic decisions, edge intelligence will enable responsiveness at scale, workforce transformation will become a shared priority, and sustainability will integrate into daily operations. For manufacturing leaders across the region, the goal is not to adopt every emerging technology, but to build digital foundations that connect systems, empower people and deliver insights that scale across varied environments. Those who do will navigate the region’s rapid transformation and shape its next era.

Accelerating biomedical insights: How AI can speed up literature analysis in genomics The ability to synthesise and curate vast amounts of scientific literature is becoming a critical differentiator for biomedical organisations seeking to stay ahead. For Aaqib Alavy, a UCL Masters candidate specialising in artificial intelligence, this challenge is at the heart of work he currently does. Aaqib, who works on synthesised genetic variant information, is focused on streamlining the process of literature analysis and data curation in genomics. “I’m doing a lot of work within that space, looking at how AI and machine learning can aid and support the medical field,” he explained. “Right now, I’ve been working on a project within genomics, but more specifically, literature analysis and data curation within that field.” Crucial streamlining The stakes are high. As Aaqib noted, “With the flow of genomics, a lot of it is advanced by studies and scientific literature comes out every day; there are multiple articles produced regularly. “ “There’s a lot of research being done, but not enough resources to digest that research. Trying to speed up that process is part of what I’m looking into and working on.” In the field of biomedicine and healthcare, genomics companies aim to provide medical professionals with updates on genetic variants – mutations in DNA that can be linked to certain diseases. “You can think of it like an update in the form of synthesised information that is a very easy and digestible review for medical professionals, to look at and be able to understand,” Aaqib said. Profound implications Streamlining workflows and synthesising scientific literature has profound implications for the biomedical research community, for example challenging or consolidating consensus around a particular scientific finding that can impact patient care from risk assessment to preventative strategies. Aaqib explained, “If a study comes out presenting evidence that an existing benign variant now shows signs of actually being pathogenic, this is now an important area to potentially direct more resources and focus towards further consolidating that finding; however with the sheer volume and rate of publication, findings like these can often go undetected for extended periods of time.” This is why accelerating the process of curating and reviewing new findings can help the biomedical community more rapidly detect relevant findings and align their efforts with the latest evidence. Challenges The technical challenge, however, is formidable. “One of the biggest bottlenecks is the computational cost, and when it comes to optimising the resources needed whilst maintaining results that are both accurate and robust,” Aaqib shared.  If a study comes out presenting evidence that an existing benign variant now shows signs of actually being pathogenic, this is now an important area to potentially direct more resources and focus towards further consolidating that finding; however with the sheer volume and rate of publication, findings like these can often go undetected for extended periods of time.Aaqib Alavy “Right now, I’ve been working on a locally developed implementation where you can use consumer grade GPUs, as well as using models and technology that are both a lot more accessible, and more traceable and transparent when it comes to verifying results and how they were achieved.” He described how he uses LLMs or large language models, to analyse structured and formatted data like complex tables, where semantic context is limited. A heuristic-based relation extraction approach identifies co-occurrences of genomic entities within text and assesses their potential relationships using scoring models. These associations are then validated or expanded by cross-referencing known databases. APIs (Application Programming Interfaces) play a crucial role in Aaqib’s work by providing powerful tools for data extraction and analysis. A specific example he highlighted is Llama’s extraction tool, Llama Extract. However, Aaqib  noted significant challenges with API-based LLM solutions. “Because it’s an LLM, it’s not as deterministic and transparent. There’s also the issue of hallucinations, where LLMs will make up information for the sake of answering a prompt or completing a task.” Explainability and robustness Another critical issue is explainability – an extremely important requirement in the medical field. “Because it’s the medical field, genomics, that is probably one of the most crucial and vital components – being able to understand why these insights are the insights that they are, is extremely important for both medical professionals and the larger biomedicine community,” Aaqib emphasised. “If you want to make conclusive decisions, you need to understand why a certain tool, model, or technology has yielded the insights that it has.” Despite these challenges, Aaqib sees broad potential for the technology. “There’s a lot of benefit for other industries, because it’s essentially information synthesis. Being able to have that for any industry, any application, is extremely useful.”  For example, a significant technical hurdle exists in extracting and processing PDF documents, a problem that stems from the inconsistent nature of PDF documents: “PDFs are extremely varied across the board in terms of structure, layout, and semantics,” he explained. And as the volume of biomedical literature continues to grow, the need for advanced tools to curate and synthesise information will only intensify. “The advancement of information synthesis, literature analysis within the field of genomics and the wider medical field can really, really aid the whole world of biomedicine as a whole,” Aaqib emphasised. “Because the main bottleneck is moving along findings down the pipeline… being able to produce and create tools that can aid in that review and streamline the process is extremely useful.” Ultimately, the ability to effectively parse documents in challenging formats could have broader implications beyond biomedicine, as Aaqib had also suggested that solving the PDF extraction challenge could potentially benefit multiple industries, ultimately enhancing knowledge sharing within these sectors.