CEAI Global Explores AIDT Lab Establishment for SSP to Accelerate Applied AI Capability

KUALA LUMPUR, MALAYSIA — A proposed AI & Design Thinking Lab for SSP would provide a setting for multidisciplinary teams to turn operational challenges into testable solutions rather than isolated technology demonstrations. The proposed lab model would combine executive learning, Design Thinking, use-case prioritisation and hands-on demonstrations. Its objective is to help participants identify where AI can improve operational performance while addressing data readiness, governance, workforce capabilities and implementation risk. A phased operating model could begin with readiness assessments and workshops, followed by selected sandbox pilots and a pathway for commercialisation of validated solutions. Any final scope, governance structure and launch schedule would be subject to SSP approval and definitive arrangements.

Purpose of the proposed lab

An AIDT Lab for SSP could create a structured environment in which executives, subject-matter experts and technical teams jointly examine real operational problems. Its value would come from combining AI awareness with design thinking, process analysis and the practical evaluation of solutions.

Governance and delivery model

A credible lab would require defined sponsorship, an operating charter, appropriate access controls and a portfolio of prioritised use cases. A small number of pilots could be assessed using baseline and post-implementation measures, with any wider roll-out conditional on demonstrated value.

A lab built around real problems

The proposed SSP lab would combine AI readiness, design thinking and structured problem discovery. Rather than measuring success by training attendance alone, its work could begin with operational pain points selected by SSP leadership and tested with staff who understand existing processes.

A practical operating model

A credible model would include an executive sponsor, a cross-functional steering team, appropriate safeguards for organisational data and a small portfolio of priority use cases. Each pilot would have a baseline, accountable owner and post-test review. The lab could then support capability building and potential IP development without assuming that every experiment becomes a commercial product.

“An effective AI lab should not be measured by the number of demonstrations it hosts. Its value lies in building capability and producing solutions that address real problems with measurable results.”

Prof Dr M Nazri Muhd, Group Executive Chairman, Centre for AI Innovation (CEAI), Global, Chief AI Advisor, AI & Design Thinking (AIDT) Lab @ KSI Strategic Institute for Asia Pacific (KSI)

Next steps

The organisation will continue to assess relevant opportunities in line with stakeholder priorities and applicable approvals. Further details about any confirmed agreements, programme dates, products or implementation milestones will be communicated through official channels. This announcement does not constitute an offer of securities, a guarantee of financial performance or confirmation of a transaction not yet executed.

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