Research & playbooks
for shipping AI in Asia.
Frameworks we use in client engagements, plus original research on AI adoption across the markets we operate in. No hype, no rehashed Western reports.
Securing AI Agents in APAC Enterprise: A Practical Threat Model
AI agents that can take actions — send emails, update CRM records, query databases — create new attack surfaces that traditional application security does not cover. Here is how to build a threat model for production agent deployments.
Korea Enterprise AI in 2026: Regulation, Culture, and Deployment Realities
A practitioner snapshot of South Korea's AI landscape: the AI Basic Act implementation, chaebol and mid-market adoption patterns, Korean language deployment constraints, and what it actually takes to succeed in the Korean enterprise market.
Logistics and Supply Chain AI in APAC 2026: Trade Finance, Port Automation, and the China+1 Opportunity
APAC handles 60% of global container trade — making logistics the region's highest-volume AI adoption sector. A practitioner guide to APAC logistics AI: trade finance document intelligence (the Letter of Credit discrepancy problem), port automation at PSA/Kwai Tsing/Yangshan, demand forecasting for e-commerce supply chains, last-mile delivery AI across archipelago geographies, and supply chain visibility for the China+1 manufacturing era.
Real Estate and Proptech AI in APAC 2026: Lease Intelligence, Smart Buildings, and the Data Transparency Gap
Real estate is APAC's largest asset class — and one of the most data-constrained sectors for AI. A practitioner guide to property and proptech AI in Asia: lease document abstraction and portfolio management, construction document intelligence, smart building energy optimisation, AVM valuation models in transparent vs opaque markets, and REIT portfolio AI across Singapore's S-REIT, Hong Kong's H-REIT, and Japan's J-REIT sectors.
Data Readiness for AI: The APAC Enterprise Playbook
67% of APAC AI pilots that failed to reach production cited data quality or availability as a primary factor — yet only 23% of enterprises ran a formal data readiness assessment before starting. A practical framework covering the five dimensions of data readiness (quality, volume, labelling, governance, infrastructure), a 20-point pre-project checklist, and use-case-specific minimums for the most common APAC enterprise AI applications.
Technology Sector AI in APAC 2026: SaaS Companies, Infrastructure Providers, and the AI-Native Wave
The technology sector is simultaneously the largest producer and consumer of AI in APAC — building AI into products, using AI to accelerate development, and competing with AI-native challengers. A practitioner guide to how APAC technology companies are responding: AI-assisted software development, the AI-native startup threat to legacy SaaS, infrastructure AI for cloud providers, and the talent and model strategy decisions that separate AI leaders from laggards.
AI Change Management and Workforce Adoption: The APAC Playbook
Most enterprise AI deployments fail not because the technology doesn't work — but because the organisation doesn't adopt it. A practitioner playbook on AI change management in APAC: the cultural dynamics that derail Western frameworks (hierarchy, face culture, collective identity), five phases of adoption from awareness to expansion, the internal roles that sustain adoption, and the metrics dashboard that shows you whether you're stalling.
Education and EdTech AI in APAC 2026: Universities, Corporate Learning, and the Academic Integrity Challenge
APAC is home to 60% of the world's student population — and is the fastest-growing EdTech AI market globally. A practitioner guide to AI in APAC education: university AI policy responses (from Singapore's NUS to Japan's national framework), AI tutoring and personalised learning, corporate L&D AI, the academic integrity crisis, and the specific regulatory and cultural barriers that make APAC EdTech AI different from the US and European markets.
Why APAC AI Projects Fail in Year Two (And How to Not Be That Case Study)
Year one of an enterprise AI project is almost always a success. By year two, 40–60% have stalled, been deprioritised, or quietly discontinued. The failure is rarely technical. Here is what actually happens, and what to do differently.
The 2026 AI Adoption Playbook for Asian Enterprises
Where mid-market leaders in Hong Kong, Singapore, and Taiwan are putting AI budget — and where they are pulling back.
Webinar Recap: Agentic AI in Enterprise — What Actually Works in Production
Recap of AIMenta's April 2026 webinar on enterprise agentic AI deployment: where multi-agent systems succeed, where they fail, what orchestration patterns hold up at scale, and the hidden operational costs most pilots don't surface.
Agentic AI: Separating the Signal from the Noise in 2026
Every AI vendor is selling agentic AI. Every conference panel is discussing agentic AI. Most enterprise deployments of "agentic AI" are not agentic AI. A clear-eyed assessment of what the technology can actually do in enterprise contexts in 2026 — and where the real deployments are working.
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