The conversations
shaping Malaysia’s
industrial future

What this means for Malaysia, semiconductors, manufacturing, supply chains, trade policy and US-China technology competition

Your factory is overflowing with data. What if you could use it to design and develop the most efficient production workflow for your products as you scale from pilot line to sustainable production and eventually to Gigafactory output? As Malaysia enters a new industrial growth cycle, driven by advanced manufacturing, regional supply chain shifts and rising expectations for digital readiness, manufacturers are looking for practical ways to scale with confidence. This presentation explores how first principles design and data driven decision making, as applied in some of the world’s fastest scaling production environments, can help Malaysian manufacturers build systems that grow intelligently and predictably. Drawing on experience developing rapid pilot and production lines across all scales, from startups through to Tesla’s Gigafactories, we’ll discuss how early and ongoing operational data can shape product architecture, simplify system design, optimise end to end material flow, and reduce supply chain complexity before major investments are made. Participants will gain insights for designing factories from the atoms up, so that they are flexible at low volume, efficient at high volume, and ready to compete across Southeast Asia and the global market.

As large corporates race to deploy AI across critical operations, three questions now dominate the boardroom: Is it safe? Is it sovereign? Can we trust what it tells us? Getting this wrong risks regulatory exposure, data leakage, and AI systems that confidently deliver the wrong answer at the worst possible moment. This session unpacks how leading enterprises are answering all three — moving beyond proof-of-concept to production-grade AI that satisfies security, compliance, and the C-suite simultaneously. We'll examine how organisations are choosing between private cloud, on-premises, and sovereign cloud deployments based on data sensitivity and residency requirements; embedding access controls, encryption, and human-in-the-loop approval for high-stakes decisions; and using retrieval-augmented generation and curated enterprise knowledge to keep outputs accurate, current, and free of hallucination. Attendees will walk away with a practical framework for AI governance — spanning use-case prioritisation, cross-functional accountability, and continuous monitoring for bias and performance drift — that turns AI from a compliance headache into a measurable driver of business value

3 Key Learnings
Sovereignty starts with control: Keep sensitive corporate data, models, and AI operations within trusted infrastructure and clear governance boundaries.
Accuracy requires enterprise knowledge: Connect AI to verified internal data, policies, and documents through RAG, validation, and human review.
Safety must be designed from day one: Implement strong access controls, encryption, audit trails, risk monitoring, and clear accountability before scaling AI across the organisation.

Industrial Artificial Intelligence Maturity Readiness Index (AIMRI) is co-developed by INCIT and Detecon International (with scientific advisors from leading institutions) to unlock the power of AI in manufacturing. It is the world's first AI-centric industrial readiness framework to give manufacturer a clear, evidence-based picture of their AI readiness across 3 building blocks (AI Purpose, Technology & Data Foundation and Intelligent Enterprise)— so manufacturers can prioritise AI investment, align stakeholders, and build the path from buying AI to advancing and becoming AI future-ready.

Key Learnings:

1.) Introduction to the AIMRI Framework
2.) Understanding the impact to bottom line, connecting AI maturity to financial outcomes.
3.) Understanding and ranking AI business objectives, based on what matters most. "

How do you invest confidently in AI and digital transformation when the economic environment is uncertain?

Physical AI brings adaptive machine intelligence into physical hardware, whereas traditional industrial robots rely on rigid, step-by-step programming. When it works, Physical AI is vastly superior, enabling machines to navigate unstructured environments, handle varied tasks, and learn from unexpected obstacles in real time. However, this technology comes with steep trade-offs. Physical AI requires exponentially more powerful compute stacks, low-latency processing, and high energy budgets to operate effectively. So, is Physical AI a reality, hype, or strategic marketing branding? This talk will share key insights into its current state and offer a brief glimpse into its future.

Key Learnings:

• The key differences between Physical AI and traditional robotics
• The hidden trade-offs of deploying Physical AI
• Case studies of Physical AI applications

This keynote presents a practical view of Agentic AI in smart manufacturing through real-world examples and implementation patterns. It shows how manufacturers can apply Agentic AI to augment workflows, improve execution, and support better operational outcomes. Attendees will leave with a clearer understanding of how to use Agentic AI responsibly, build trust progressively, and turn technical potential into measurable business value.