UTM roundtable asked whether Malaysia should go AI-first—or build from local strengths

Universiti Teknologi Malaysia convened academics, industry participants and policy-linked organisations in May 2024 to debate a question that still matters: should Malaysia try to move AI-first, or should it adopt the technology more selectively around the capabilities it already has? The roundtable was held in conjunction with the launch of UTM's Faculty of Artificial Intelligence and included discussion of the Malaysia Artificial Intelligence Consortium, or MAIC.[1]

The official report did not present a finished national plan. It recorded a working discussion about scarce resources, human capacity, partnerships, small-business economics, ethics and public understanding. Professor Olaf J. Groth, invited by UTM for the session, argued that trust is essential to adoption, while participants considered how Malaysia could learn quickly without pretending to be a new global semiconductor or computing giant overnight.[1]

That makes the event useful as a decision framework rather than as a programme announcement. It highlights why national AI progress cannot be measured only by the number of strategies, faculties or consortiums launched. Adoption becomes meaningful when organisations can identify a problem, access capable people and infrastructure, manage the risks and show that the benefit exceeds the cost.[1]

UTM's account said the group examined AI-first and AI-second approaches at a country level. It also acknowledged that Malaysia had limitations in resources and human capacity, making it unrealistic to assume the country could simply recreate a company such as NVIDIA. The proposed response was not withdrawal from AI, but faster learning through partnerships and stronger use of existing local capabilities.[1]

A strategy built around local strengths still requires choices. Universities can develop talent and applied research; government can shape procurement, policy and public programmes; industry can identify real workflows and fund deployment. Partnerships are useful when they transfer capability and create repeatable local work, not when they only produce a ceremonial launch or a broad memorandum without an owner, budget and measurable outcome.[1]

The roundtable referred to a MyDIGITAL-commissioned estimate of RM113 billion in potential economic impact if Malaysian businesses adopt AI. At the same time, participants noted that adoption had not fully accelerated. The official report connected that gap to the structure of the local economy: many businesses are small or medium-sized, remain cost-conscious and are reluctant to invest when the return is uncertain.[1]

For an SME, the practical threshold is rarely whether an AI model looks impressive. It is whether a specific use case saves enough time, reduces errors, improves conversion or creates revenue after integration, training and oversight costs. Smaller experiments with a clear baseline can answer that question more honestly than a large transformation slogan. A national adoption programme should therefore make evidence, implementation help and reusable examples easier to reach.[1]

Groth emphasised ethics as a foundation for trust between organisations and their customers or stakeholders. The discussion also considered public fear and misunderstanding, calling for awareness and education supported by government policy. UTM's report said the recommendations ultimately clustered around two themes: capacity-building in the Malaysian context and trust-building through public engagement.[1]

Trust is not created by reassurance alone. It requires clear responsibility for decisions, sensible data handling, disclosure when AI materially shapes an outcome, escalation to people when systems are uncertain and evidence that a deployment works as claimed. Public literacy can make those expectations easier to discuss, while universities and consortiums can translate them into training, technical evaluation and sector-specific practice.[1]