Frontier Enterprise

Richard Chung, Executive Advisor, Toyota Boshoku. Image courtesy of Toyota Boshoku. Automotive interior components supplier and filter manufacturer Toyota Boshoku is using AI across its design and manufacturing operations. But while the technology is reshaping product development and manufacturing, it has also highlighted the limits of automation in areas where human knowledge and judgement remain difficult to replace.

Ask most executives where their next major cyber incident will come from, and they will point outward, toward ransomware syndicates, nation-state operatives, or sophisticated phishing campaigns engineered to exploit a single distracted employee. The threat, in the corporate imagination, always arrives from beyond the walls. The data tells a different story.

Ninja Van Malaysia, which serves nearly 25 million recipients nationwide, has modernized its fulfilment operations with Zebra’s warehousing solutions. As the company scaled to serve both e-commerce and B2B clients, the complexity of its fulfilment operations grew. This created a need for agile, automated solutions to keep pace with high order volumes, diverse client requirements, and time-critical dispatch schedules without peak-period lag.

Photo courtesy of National Supercomputing Center in Shenzhen LineShine, a previously unlisted system installed in China, debuts at No. 1 in the 67th edition of the TOP500 list of the world’s most powerful supercomputers. The company displaced El Capitan as the world’s most powerful supercomputer as measured by the High Performance Linpack (HPL) benchmark.

The Singapore Red Cross (SRC) took steps to strengthen disaster response and public health preparedness in collaboration with Dataiku, provided SRC’s Humanitarian Innovation and Technology (HIT) team with free access to its platform and pro bono support from Dataiku’s data scientists and engineers. This is intended to help the non-profit organisation unlock the value of its data and scale its impact through specific AI use cases.

Last month’s Asia Tech x Singapore did more than bring technology leaders into one room. It captured where the wider Asia-Pacific (APAC) region’s AI debate is heading. The takeaway was hard to miss: APAC has moved past the question of whether enterprises should use AI. The harder question is how they deploy it securely, reliably, and at scale. That conversation matters, yet it still leaves one cost under-discussed. Across APAC, boards are scrutinising AI spend with growing intensity.

Reducing operational bottlenecks can accelerate hiring without expanding HR teams. Image generated by DALL·E. Acquiring talent used to be a recurring challenge for global professional services firm Jones Lang LaSalle (JLL), largely because of its 15 fragmented HR systems supporting its 115,000 employees across more than 80 countries. This meant different payroll vendors in each country, separate recruiting tools, and disconnected time-tracking systems.

Enterprises across Asia-Pacific are moving quickly on generative AI, and that momentum is genuinely exciting. But in many cases, the distance between a strong pilot and reliable operational deployment is proving harder to close than anticipated. More often than not, the challenge is not the model. It is the data behind it. AI systems perform to the quality of the data used to train, validate, and refine them.

Kobu Kotaraju, Global Procurement Lead at CBRE, and Natalie Que, CIO and CDO for Southeast Asia and North Asia at Kenvue, join Arun Saksena, Global Lead, Strategy, Customer and Channel Programs, AWS Marketplace at Amazon Web Services, during a discussion on the evolving role of procurement in technology decisions at an AWS forum in Singapore, moderated by Rahul Joshi, Head of Content at Jicara Media.

Building the right architecture and governance is essential for scaling AI beyond pilot projects. As organisations move beyond experimenting with AI, many are discovering that successful deployment depends less on model performance alone than on the underlying architecture that supports it. Scaling AI requires robust governance, well-defined workflows, secure infrastructure, and integration across enterprise systems.