As we all know, the Asia-Pacific (APAC) region has been at the forefront of digital transformation over the past decade. Cloud adoption, mobile-first economies, and platform-led business models have enabled enterprises to scale rapidly and innovate continuously. Hyperscalers such as AWS have played a foundational role in this journey, providing scalable infrastructure, accelerating application modernization, and democratizing access to advanced technologies.
At the AWS Summit Singapore 2026, Priscilla Chong, managing director at AWS Singapore, reflected on this journey, highlighting that since launching its first APAC region in Singapore in 2010, AWS has invested over SGD 11.5 billion in digital infrastructure, with an additional SGD 12 billion planned through 2028. These investments are estimated to contribute close to SGD 23.7 billion to the country’s GDP while enabling job creation and workforce development.
This perspective was reinforced by Desmond Tan, senior minister of state in the Prime Minister’s Office and deputy secretary general of the National Trades Union Congress (NTUC), who emphasized that Singapore’s progress across waves of technological disruption, from computerization to cloud, has always been anchored in three pillars: vision, execution, and values. The country’s National AI Strategy and the tripartite collaboration model among government, enterprises, and the workforce exemplify how ecosystems can align to drive inclusive digital transformation.
However, the conversation has shifted for both industry leaders and policymakers, and the next phase of transformation is no longer about access to AI; it is about execution at scale.
For CIOs, this represents a fundamental inflection point.
Across APAC, enterprises have been investing heavily in data platforms, analytics, and AI initiatives. Over the past few years, generative AI (Gen AI) has further accelerated interest, with organizations experimenting across customer experience, productivity, and automation use cases.
In essence, APAC enterprises entered 2026 with:
As Jaime Valles, vice president and managing director APJC, AWS, emphasized during the summit, the industry is moving toward a phase where technology access is no longer the primary barrier to innovation; instead, the focus is shifting to how quickly organizations can integrate, operationalize, and scale AI across their environments.
This shift is being driven by several structural changes:
According to Avasant’s Applied AI Services 2024–2025 Market Insights™ report, globally over the past two years, Gen AI adoption has scaled rapidly as enterprises realize ROI. In contrast, agentic AI remains in the pilot/POC phase due to a lack of governance frameworks and the need for autonomous decision-making.
This raises a critical question:
How can enterprises transition from fragmented AI initiatives to scalable, production-grade systems that deliver sustained business value?
The answer lies not in isolated tools but in orchestrating the enterprise for AI execution across infrastructure, data, applications, and workflows.
These four layers are deeply interconnected and together define how effectively an organization can scale AI.
This layered compute portfolio lets enterprises match AI workloads to the right hardware, from cost-optimized inference to high-performance GPU training. But infrastructure alone doesn’t create value; it requires a strong data foundation.
Customer example: Grab, a Singapore-based ride-hailing, food delivery, and digital payments application
Ken Lek, Grab’s managing director and head of strategic finance and investor relations, illustrated how data fragmentation can become a strategic bottleneck. While it was working to get listed on NASDAQ in 2021, the company faced extreme uncertainty of declining demand, significant losses, and the need for real-time scenario planning. However, decision-making was constrained by disconnected systems and manual reconciliation, with planning effectively built on “a spreadsheet with 47 tabs.”
By rebuilding its data architecture on AWS through GrabHouse, Grab established a centralized, governed data lake using Amazon S3 and Apache Iceberg. The impact was transformative:
More importantly, this foundation enabled the next phase of AI-driven decision-making through platforms such as BriX, its enterprise AI platform, and FinSight, an AI agent for finance, allowing teams to query data in natural language, run scenario analyses, and automate decision workflows within a governed environment.
But even with strong data foundations, value is realized only when intelligence is embedded in applications.
Customer example: Certis, a Singapore-based security and integrated services organization
Certis’ President and Group CEO Ng Tian Beng demonstrated how agentic AI extends beyond digital workflows into physical operations. Facing workforce constraints, the company re-architected its operations around AI and robotics.
Its Mozart platform, built on AWS, integrates data from sensors, cameras, robots, and operational systems into a unified control layer. Powered by Amazon Bedrock for reasoning and Amazon SageMaker for model development, Mozart coordinates AI agents across use cases such as incident detection, automated reporting, and workforce optimization.
These agents are tightly integrated with robotics, including humanoid concierge units and autonomous quadrupeds, enabling end-to-end execution in complex environments such as airports and public infrastructure. For instance, robots can autonomously inspect unattended objects, analyze contextual data from multiple systems, and trigger appropriate responses, reducing reliance on manual intervention while improving response times.
Customer example: Castlery, a Singapore-based digital furniture lifestyle brand
Faced with an aggressive business mandate to grow 2–3x within three years without adding head count, the company’s existing setup of 70 engineers operating across traditionally siloed teams was structurally misaligned with the required pace of execution.
In response, Castlery partnered with AWS to adopt the AI-driven development life cycle, shifting from conventional development approaches to an AI-native, end-to-end model. By embedding AI across the entire life cycle, from requirements definition and design to coding, testing, and deployment, the company was able to compress its development cycle by 50% while effectively doubling its delivery capacity, all without increasing team size.
Yet even AI-native applications deliver limited value unless embedded into core business processes.
Customer examples:
As AI becomes embedded in workflows, trust becomes non-negotiable. Security, compliance, and governance must be built into every layer, from infrastructure to applications. AWS supports this through a comprehensive security framework, including 143 security standards and certifications, as well as data residency controls to meet sovereignty requirements. The AWS Nitro system provides hardware-based isolation with zero operator access, ensuring strong safeguards for sensitive workloads, particularly in regulated industries adopting agentic AI.
To further enable safe and responsible AI adoption, AWS has introduced capabilities such as automated reasoning to verify AI outputs with 99% accuracy and policy-based controls within AgentCore to validate actions before execution. These mechanisms ensure that AI systems operate within defined guardrails as they scale across enterprise workflows.
Beyond foundational security, AWS is also embedding AI into security operations, with agent-based capabilities for proactive threat detection and automated incident response. Organizations such as the Cyber Security Agency of Singapore are leveraging these services to enhance security assessments and operational resilience.
Increasingly, this conversation is extending beyond security to digital sovereignty, ensuring organizations retain verifiable control over where data resides, how it is processed, and who can access it. In 2022, AWS formalized its commitment through the AWS Digital Sovereignty Pledge, structured around four pillars: data residency (control over where data is stored and processed), operator access restriction (verifiable controls ensuring no unauthorized access — including by AWS personnel), resilience and survivability (continuity despite disruption), and independence and transparency (governance, auditability, and operational autonomy).
AWS’s sovereign-by-design architecture lets enterprises align sovereignty needs with workload requirements. The AWS Nitro System, which powers all AWS compute including Trainium and Inferentia, provides strong physical and logical security with no AWS access to customer workloads (independently validated by NCC Group). Services support encryption with external key management, while AWS Control Tower enforces data residency and governance across accounts.
For workloads with specific residency and isolation requirements, AWS provides a spectrum of infrastructure options, including:
As AI adoption scales, sovereignty requirements extend across the full AI stack, covering model inputs, outputs, training data, and inference pipelines. AWS enables AI sovereignty through choice of compute and deployment location, model control through Amazon Bedrock (where no customer inputs or outputs are used to train any models), identity governance for AI agents through Amazon Bedrock AgentCore Identity, and support for sovereign language model development, including SEA-LION, a family of open-source multilingual LLMs for Southeast Asia trained entirely on AWS.
Equally critical is aligning people with technology. The partnership between the NTUC and AWS reflects a broader shift in APAC from isolated AI adoption toward ecosystem-led workforce transformation.
Under the AI-Ready Enterprise initiative (2026–2029), NTUC and AWS aim to support at least 100 enterprises in AI-driven business transformation and to equip 10,000 workers with AI capabilities through structured learning pathways spanning foundational literacy to advanced technical deployment. Importantly, the initiative moves beyond training alone by integrating AI adoption, job redesign, and workforce upskilling through NTUC’s Company Training Committee (CTC) framework and grant ecosystem.
Today, more than 3,800 CTCs have been formed, benefiting enterprises across different sectors. The government has set aside SGD 300 million to support NTUC’s CTC grant.
APAC is entering a defining phase of its digital journey, one where competitive advantage will be determined not by access to AI, but by the ability to execute it at scale.
The enterprises that will lead are those that:
AWS provides a comprehensive stack to enable this transition. But technology alone is not the differentiator.
The real differentiator is execution. For CIOs, this requires a shift in mindset to orchestration, with the CIO as the conductor: from use cases to operating models, from tools to architecture, and from pilots to enterprise-wide transformation.
The next decade of enterprise value creation in APAC will belong to organizations that can translate AI ambition into disciplined, scalable execution, and in doing so, redefine how businesses operate in an AI-first world.
By Gaurav Dewan, Research Director, Avasant
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