APAC industry leaders share their thoughts on AI

APAC leaders say AI value depends on redesigned work, trusted deployment, governed data and accountable automation.

AI value in APAC organizations is moving beyond experimentation and model deployment, according to industry leaders reflecting on developments in the first half of 2026. Their responses point to a common test: whether enterprises can embed AI into work and operational systems while retaining human judgment, security and accountability.

How should organizations redesign work around AI?

Jessica Zhang, Senior Vice President, ADP APAC, said the value of AI depends on how effectively it enables employees to focus on strategic and high-value work. She described a workforce shift in which routine tasks are streamlined while people retain responsibility for critical thinking, creativity, problem-solving, collaboration and judgment.

Zhang said employers need to support that transition through practical AI training, clear communication about changing roles and a culture of continuous learning. Richard Spence, VP and GM APAC at Cognition, similarly said software engineers are spending less time writing routine code and more time directing, reviewing and validating work completed by AI teammates.

For enterprise leaders, the implication is that AI value in APAC organizations will depend partly on job and workflow redesign rather than adoption alone.

The true value of artificial intelligence lies not in the technology itself, but in its ability to empower people to focus on more meaningful, strategic and high

— Jessica Zhang, Senior Vice President, ADP APAC

What changes when AI enters critical and industrial systems?

Emily Tan, CEO of Thales Solutions Asia Pte Ltd and Country Director of Thales Singapore, said the debate is shifting from innovation to trusted deployment as AI moves into aviation, defense, public safety and critical infrastructure.

She said such systems require resilience, validation, redundancy, traceability and human oversight. Alex Teo, Vice President and Managing Director, Southeast Asia, Siemens Digital Industries Software, made a similar case for manufacturing, saying industrial AI must be embedded across engineering, production and operational workflows with transparent outputs that engineers can validate.

The shared concern is that critical environments cannot treat AI as an isolated software layer. Systems must connect data, communications, cybersecurity and human expertise while preserving operational control.

The real test is whether AI can operate safely, securely, and reliably when decisions carry real

— Emily Tan, CEO, Thales Solutions Asia Pte Ltd and Country Director, Thales Singapore

Why are governance and control becoming central to AI strategy?

Dr. Vishnu Nanduri, AI Innovation Leader, ASEAN & Korea, Kyndryl, said enterprise AI is entering a phase in which control matters as much as adoption. He cited Kyndryl’s 2026 People Readiness Report, which found that 57% of organizations had embedded AI in core processes or deployed it broadly, while only 32% had achieved at least one of their top two AI goals.

Nanduri also said 81% of organizations expect AI agents to make impactful decisions within the next year, but only 25% completely trust systems operating without human oversight. He argued that enterprises need defined roles, permissions, escalation paths and auditability as agents begin operating across functions such as IT, finance, operations, supply chains and customer service.

Yashaswi Mudumbai, Senior Director of Solution Engineering, APAC, JFrog, identified a related gap between AI policy and enforcement. JFrog’s 2026 Software Supply Chain Security State of the Union report found that 83% of APAC organizations had checks for AI inputs and outputs, while only 55% used automated Shadow AI detection.

For the past two years, enterprises have treated AI as a race to adopt. The next phase will be a race to control.

— Dr Vishnu Nanduri, AI Innovation Leader, ASEAN & Korea, Kyndryl

What data foundations are needed for AI to deliver workflow outcomes?

Geoff Thomas, Senior Vice President and General Manager for APJ at Infor, said organizations are seeing greater value when AI is embedded in operational workflows rather than used as a standalone productivity tool. He said industry processes, data and business rules provide the context needed for AI to move from recommendations toward trusted execution.

Greg Taylor, SVP, APAC, Confluent, said agentic AI systems—which can make decisions, trigger actions and interact with internal systems—require continuously updated, real-time context. He cited Confluent’s Data Streaming Report, which found that 75% of Singapore IT leaders were deploying or piloting agentic AI, while 78% said insufficient real-time data infrastructure was slowing AI growth.

The responses position data architecture as a business-control issue. Without connected and governed data, AI systems may produce insights without the context required to act reliably across workflows.

AI will not create business value because organisations deploy more models

— Greg Taylor, SVP, APAC, Confluent

How should enterprises build trust with employees, customers and regulators?

Juliana Lim, Country Manager, Red Hat Singapore, said AI workloads increasingly span on-premises environments, multiple clouds and the edge, while data sovereignty requirements raise the importance of governance, compliance and control. She said organizations need visibility into how AI systems are developed, deployed and managed regardless of where workloads run.

Shahid Nizami, VP APAC & GCC, Braze, focused on the customer dimension of trust. Braze’s 2026 Customer Engagement Review found that 93% of marketers said AI helped them understand customers better, compared with 53% of customers who felt brands accurately predicted their wants and needs.

Govind Choudhary, General Manager, Southeast Asia & India, Digital Realty, said infrastructure readiness is also shaping how quickly enterprises can deploy AI in production. He emphasized the need to govern, secure and retain the intelligence and institutional knowledge generated through AI while keeping those assets within organizational control.

Across the interviews, the message is consistent: AI value in APAC organizations will be judged by whether systems remain explainable, secure, resilient and aligned with business and customer expectations.

AI has become remarkably good at generating content. It's still much harder to generate trust.

— Shahid Nizami, VP APAC & GCC, Braze