By DPEX Editorial Team
At the recent 11th Learning & Development Asia 2026 Malaysia conference, the focus was placed on how learning and development can become a business advantage as rapid advances in technology, rising cyber risk, and shifting workforce expectations are reshaping how people work and learn. Held from 23 to 24 September 2026 at Sofitel Kuala Lumpur, the event brought together more than 120 HR and L&D delegates across more than 16 sessions.
The discussions were pertinent as Malaysia's National AI Action Plan 2026 to 2030 positions AI as a core driver of the country's next phase of development, with the ambition of becoming an inclusive and sustainable AI nation by 2030. The plan recognises that Malaysia is in the process of moving to large-scale, multi-sector AI deployment, with remaining gaps in advanced talent depth, private investment mobilisation, and research-to-commercialisation pathways.
That gap between national AI ambition and enterprise execution formed the backdrop to a panel discussion titled "From vacancy to velocity: Keeping pace with mid-management scarcity, critical skills and digitalisation," where Alvin Toh, Co-Founder and Chief AI Officer of Straits Interactive, took to the stage and addressed the nation's root challenge: many organisations are adopting AI as a reaction to a skills shortage, when the deeper issue is a judgement gap.
Malaysia's AI Strategy Needs Organisational Capability
AI Malaysia Berhad, the national agency under the Digital Ministry, has been mandated to lead, coordinate, and accelerate the national AI ecosystem towards the AI Nation 2030 vision. At policy level, Malaysia's direction is clear: AI must support productivity, competitiveness, responsible governance, and public trust.
Inside organisations, however, that ambition still has to pass through people, workflows, and management structures. National strategies can provide direction and infrastructure, but AI adoption succeeds or fails when teams decide what to automate, what to augment, what to govern, and who remains accountable for the final decision.
Alvin framed this as "a judgement deficit dressed up as a skills shortage." Organisations are often chasing AI tools and pushing their employees to the latest software-as-a-service (SaaS) models while lacking people who can challenge machine output, apply domain context, and decide whether it should be used. Alvin notes these professionals as "AI bilingualist", business professionals who speak both the language of their domain and the language of Gen AI capability.
Mid-management Is the Translation Layer
The panel focused on mid-management scarcity because this is where AI capability often becomes operational. Senior staff may hold tacit knowledge of the business, while younger staff may be more familiar with new tools. The middle layer is expected to translate between experience, technology, and accountability.
When that layer thins out, AI pilots may struggle to scale. A team may build a useful automation, but without clear ownership, documentation, governance, or a business process around it, the pilot remains dependent on the person who built it. If that person leaves, the capability may leave with them.
Alvin also linked the gap to shadow AI. When formal pathways move too slowly, employees may route around them by using free consumer tools or informal workflows outside governance. He described this as the "freemium trap": activity that can look like enthusiasm, but may actually introduce unmanaged risk.
Capability Is Not Simply Tool Familiarity
A key distinction is the difference between competency and capability. Competency may be one analyst who can prompt well, while capability requires skills, knowledge, tools, processes, and a transformation mindset to work together in a repeatable way.
This matters because AI adoption in Malaysian organisations will not be driven only by coders. Alvin pointed to the practical reality that the larger value base often sits among domain experts in human resources, legal, finance, operations, customer service, and other business functions. They understand the decisions, risks, exceptions, and customer realities that AI systems must support.
For learning and development teams, AI training should therefore move beyond tool use. It should help employees understand which decisions matter, where guardrails are needed, and how machine output should be challenged before it becomes action.
Job Architecture Must Follow the Workflow
Alvin's advice was to start from the process, not the organisation chart. Companies should map workflows that carry revenue or risk, identify where AI can augment work, and define where a human must still decide. The role should then be designed around the decision it improves, not around the attraction of a new title.
This also changes how organisations should think about emerging roles such as AI trainers, AI supervisors, and data and AI governance officers. These responsibilities may already exist informally, but if they are not named, recognised, and governed, they may sit with whoever is most curious rather than whoever is most accountable.
Malaysia's AI Opportunity Requires Human Accountability
Malaysia's AI agenda is moving into an execution phase. For organisations, Alvin's remarks point to a practical order of operations: train people, map processes, then bring in the tools. Moving quickly without ownership often becomes visible only after a pilot fails to scale or an unmanaged tool becomes operational risk.
Malaysia's AI market will not be defined only by how quickly companies adopt new systems. It will be defined by whether organisations can build people who direct the work, question the output, govern the risk, and take accountability for the decision. As Gen AI develops across the Malaysian landscape, the next skill gap will fall to business professionals who can judge AI outputs through domain expertise, governance awareness, and operational accountability.
For Malaysian organisations preparing their workforce for AI adoption, explore our wide variety of Gen AI courses to help HR, L&D, marketing, sales, compliance, and legal teams apply AI with domain judgement, governance awareness, and practical safeguards.
Sources: Human Resource Online, Ministry of Digital Malaysia, National AI Office Malaysia
