As AI writes code, Singapore firms hunt for ‘commercially ready’ tech talent

[SINGAPORE] Technical proficiency in programming and Web development – once the passport to a lucrative career in Singapore’s technology sector – may no longer be enough.

As generative artificial intelligence (GenAI) makes it easier to generate code and automate development tasks, employers are now recalibrating and raising the bar on what they look for in new hires.

“The ability to write code with an AI assistant is no longer a differentiator,” said Dr Vishnu Nanduri, Asean head of AI and innovation at information technology firm Kyndryl.

The differentiator has now shifted to “commercial readiness”, or the ability to translate technical output into business value.

Industry leaders told The Business Times that while coding standards are rising, the supply of specialised AI talent is still limited.

Technical skills including AI architecture, applied machine learning engineering and AI governance are still “scarce in the talent pool”, Dr Nanduri said.

The shortage is even more pronounced where AI must be combined with domain-specific understanding, said Guna Chellappan, general manager of Singapore at Red Hat, the open-source enterprise firm acquired by IBM.

Financial services, cybersecurity and enterprise infrastructure – sectors that are specialised and highly regulated – are among those feeling the pinch, he noted.

The lack of such AI capabilities reflects a wider trend of Singapore companies being unprepared to fully harness AI. Kyndryl’s 2025 Readiness Report found that only 26 per cent of leaders in Singapore feel their workforce have the technology skills to make the most of AI – a trend that even the tech firm itself experiences “acutely”, said Dr Nanduri.

Gap in education?

The skills deficit has turned the spotlight on Singapore’s institutes of higher learning.

While data science and computer science majors in local universities integrate machine learning and AI modules in their curriculum, some market observers believe that there is a disconnect between classroom learning and real-world applications.

Chellappan from Red Hat noted: “Graduates are leaving school with strong theoretical foundations, but the realities of deploying AI in business environments require a different set of skills.”

In reality, most companies work with unstructured data, legacy systems, complex security requirements and operational pressures.

As a result, he pointed out, while new graduates may know how to build a model, they may not know how to make it work within an organisation’s existing infrastructure.

While Dr Nanduri acknowledged that real-world data is unstructured and complex, he argued that the gap between early professional hires and Singapore university graduates is not as “pronounced” as some say.

He added that Singapore universities are “doing a phenomenal job” with machine learning and data science fundamentals.

What sets top candidates apart

Companies increasingly expect hires to think about AI beyond carrying out isolated technical tasks.

“We are hiring for people who understand data pipelines, data engineering, model evaluation, prompt engineering and risk controls,” said Dr Nanduri. “GenAI is only as strong as the data foundations and governance behind it.”

Those with hands-on experience with cloud platforms, financial operations or FinOps – a discipline that combines financial management with cloud engineering – as well as data governance and model deployment will likely stand out, he added.

Said Chellappan: “Commercially ready talent is therefore defined not just by technical knowledge, but by their ability to apply that knowledge in real-world settings.”

That includes adapting models for business needs, assessing feasibility and ensuring they operate securely and responsibly.

Shahid Nizami, vice-president for the Asia-Pacific and Gulf Cooperation Council at tech firm Braze, said those who stand out “tend to show strong real-world judgment, adaptability and an understanding of how their work advances broader business or customer outcomes”, going beyond technical requirements.

“This adaptability and willingness to learn continuously are what often set top performers apart in a fast-moving area like AI.”

SMEs looking at different skills

But when it comes to small and medium-sized enterprises (SMEs), Chua Pei Ying, head Asia-Pacific economist at LinkedIn, believes that a different set of skills is needed.

The way she sees it, AI capabilities can be classified into two distinct skill sets: AI engineering and AI literacy.

AI engineering involves skills required to build AI tools or integrate AI solutions into existing workflows, she said. It is especially relevant when it comes to training and developing machine learning and large language models.

“Even though it has grown very fast, (AI engineering) is still a really niche part of the labour market,” she said. In fact, she noted that less than 1 per cent of the global population have such skills.

Chua argued that most SMEs do not need “embedded” AI engineers as the heavy lifting of model development and technical integration can often be outsourced to third-party solutions.

Instead of competing for AI engineers, SMEs stand to gain more from developing AI literacy within their teams instead, she told BT.

AI literacy, as described by Chua, refers to the ability to use off-the-shelf AI tools such as OpenAI’s ChatGPT and Google’s Gemini to do everyday tasks such as writing e-mails, summarising notes and searching.

According to the LinkedIn Small Business Work Change Report released in December, AI adoption in SMEs is gaining traction, with four in 10 SME employees already using AI for everyday tasks. More than two in 10 use it for more advanced tasks such as complex strategy, data analysis and working with agents.

In fact, LinkedIn noted that there is year-on-year growth of 67 per cent in AI literacy skills among small businesses.

Chua likened the shift to the early days of digital literacy, where using Excel and PowerPoint eventually became a baseline expectation.

“I think we’re only just starting to see how AI tools are being used in the professional setting,” she said. “As they become more and more common, you will find that AI literacy is going to become increasingly important.”