By Celine Chew, Head of Learning & Development at Straits Interactive
With Generative AI now firmly in the forefront of technological innovation, it is no wonder that the World Economic Forum reported that AI and big data are the fastest-growing skills by 2030. As of this month, Singapore has a staggering total of 1,600 AI-related courses on the SkillsFuture portal, the nation’s platform for lifelong learning, career planning, and skills development. What this demonstrates is clear – strong demand for Generative AI upskilling in the workspace remains, with no signs of abating.
This upskilling race is backed by views from APAC SMEs that adopting Generative AI would bring about cost savings, which in turn means that employees will have to possess Generative AI skills to ensure career survival and growth. In fact, a PwC study showed that AI-skilled workers can earn up to 56% more.
The focus today on AI Bilingualism – applying domain knowledge to more efficient AI-aided work processes – means Generative AI is no longer the coder’s playground. Anyone and everyone can skill up to AI literacy. Non-tech professionals are now expected to use Generative AI and integrate it into their day-to-day work. AI bilingualism is ultimately about translating domain knowledge into AI-driven processes, as well as translating AI outputs into meaningful business decisions.
Beyond individual productivity gains, organisations are increasingly adopting an “AI Factory” model — where AI development, deployment, and monitoring are systematised into repeatable workflows. Increasingly, the AI Factory has become an operational capability embedded across business functions, supported by structured pipelines, shared tools, and governance frameworks.
However, with the deluge of course seekers and course providers heeding the government’s call to upskill, how does one actually pick the right course that can help them effectively future-proof their career?
Basic and work-relevant Generative AI skills differ greatly from each other. Free, popular AI tools can only go so far; structured, hands-on practice and a firm grasp of ethics and security remain highly relevant in professional settings.
The temptation to collect certifications can be very real. However, does having more certifications really demonstrate better skills that translate into real-world usage? Additionally, how should one select a course so that it is more than a ‘vanity’ certification that sells ‘immediate expertise’?
Here are some course components that put you in good standing:
1. Reputable Training Providers
Seek out reputable institutions such as local universities that provide credible, structured training and accreditation that is recognised in the industry. Reputable training providers go beyond superficial learning and deliver a strong foundation that remains relevant even as AI tools change.
2. Structured, Hands-on Learning
Generative AI skills, like any other skill, require consistent hands-on application. Learning to build AI apps and trying your hand at Generative AI tools drives deeper learning and builds confidence. Moving from theory to practice steers the learner away from passive learning and towards active deployment.
3. Respect for Domain Expertise
The concept of the AI Bilingualist means that domain expertise is as important as, if not more important than, Generative AI skills. The age of the prompt engineer as a silo is over. Today, being able to augment work processes with Generative AI is key – and that means transformation is led by human judgement and industry know-how. Courses that teach Generative AI targeted at specific verticals not only bring greater industry value but also greater retention as a result of their professional relevance.
4. Ethical and Responsible AI Mindset
Beyond technical skills, professionals using Generative AI must develop a strong foundation in responsible and ethical AI practices. As AI becomes embedded in everyday work processes, AI-assisted decisions can have real consequences for organisations and society.
Training programmes that emphasise accountability, transparency, and fairness equip learners to critically evaluate AI outputs, recognise bias, and maintain human oversight. This ensures that professionals are better positioned to deploy Generative AI in ways that are both innovative and trustworthy.
5. Focus on AI Governance
Gartner calls AI governance a “critical necessity”, and Indeed reports that demand for AI governance skills is up 150%. As agentic AI becomes more commonplace, the need to govern against data leaks, AI bias and hallucination risks increases. With more countries implementing Generative AI-specific laws, professionals who possess AI governance knowledge will be essential in bridging the gap between innovation, data protection obligations, and regulatory expectations.
As the market matures, the distinction between short-form upskilling and formal professional qualifications also grows. Short courses will continue to provide tactical skills. Structured qualifications — such as advanced certificates, diplomas, or stackable credentials — offer stronger signalling value in the job market.
These programmes demonstrate not just familiarity with tools but also a sustained commitment to mastering AI concepts, governance principles, and real-world implementation. In an increasingly competitive landscape, this depth can become a key differentiator.
Staying relevant through further education
Today, learning Generative AI skills has never been easier. On the flip side, selecting the right course has never been more challenging.
When we first rolled out our first Advanced Certificate in Generative AI, Ethics and Data Protection in 2024 with SMU Academy, we sought to highlight the ethical risks and responsible use of AI deployment. To bridge the gap between compliance, creation and agentic workflow, we developed the Advanced Certificate in Generative Artificial Intelligence Apps Design and Prompt Engineeringto equip professionals with the practical skills to design and build high-impact apps for different verticals.
Today, the completion of those two Advanced Certificates culminates in the industry’s first no-code Industry Graduate Diploma (IGD) in Generative AI Large Language Models and AI Governance from SMU. The structured curriculum brings a deep dive into the technical foundations of LLMs, practical prompt engineering, AI-integrated app design, and AI governance frameworks, making it ideal for professionals involved in digital transformation.
By bridging the gap between the ethical foresight to navigate the complexities of AI and practical implementation, this programme brings an all-rounded perspective of Generative AI, while also cultivating professionals who are as technically capable as they are strategically grounded – the benchmark of an AI-ready workforce.
Enrolment for the IGD in Generative AI, Large Language Models and AI Governance can be found on SMU’s website. Applications for the May 2026 intake close on 31 March 2026.
Sources: World Economic Forum, Ministry of Education Singapore, Deloitte, PwC, PYMNTS, Gartner, Indeed, Infocomm Media Development Authority Singapore
