Josephine Teo 大臣による ST Engineering InnoTech Conference 2025 での演説

Chairman, ST Engineering, Mr. Teo Ming Kian

President, Digital Systems, ST Engineering, Mr. Low Jin Phang (JP)

Colleagues and friends

Good morning and thank you for inviting me.

It will be no surprise to you that, like JP, my remarks today focus on AI.

In the last few years, AI developments have continued at a breathless pace. Increasingly, we see how it is reshaping the way we live, work, and interact.

Our first National AI Strategy was born in 2019. Barely four years later, we saw the need to refresh it. NAIS 2.0 was launched in 2023, and many of our plans are progressing well.

We are very encouraged by the pace of industry adoption, including by leading companies like ST Engineering.

At the ATxSummit in May, I announced the MERaLiON Consortium; you are one of A*STAR’s partners in the Consortium seeking to develop AI applications in specific domains, such as healthcare and aviation.

You have also entered into partnerships with companies at the frontiers of AI development such as Mistral AI and Enigma Health.

The Government is supporting enterprise adoption of AI in other ways.

At Budget 2025, we launched a $150 million Enterprise Compute Initiative (ECI). This has catalysed the cloud service providers to make available much more in terms of cloud credits, compute, AI tools, and consultancy services to support their clients in developing and scaling AI solutions.

We know that real transformation with AI is not just about making compute available. Most often, it starts with conviction and commitment at the top. This is why IMDA designed the “GenAI for Digital Leaders” programme together with tech giants like Microsoft and AWS. It is to help enterprise leaders understand how GenAI can lead to greater success. The programme will also support them in this journey.

With increased conviction and commitment, more than 40 leading companies in Singapore have set up AI Centres of Excellence. ST Engineering is one of them.

In almost all of the AI Centres of Excellence I’ve visited, the AI Practitioners – the data specialists in particular – tell me they absolutely need and value the inputs of their colleagues in other departments and functions.

In manufacturing, for example, the process engineers know the detailed workflows. The technicians know when and how maintenance must be carried out. Without them, the data scientist will be hard pressed to produce meaningful business improvements.

It is the same at Razer, a Singapore company that specialises in gaming products and services. A key process in game development is Quality Assurance, usually a time-consuming process where QA testers run the game multiple times to identify and fix bugs. Razer developed an AI tool to support QA testers in bug detection and automating bug reporting. One of the software engineers I spoke to shared that this tool can halve the usual time spent on QA, allowing him to focus on enhancing game design.

These examples show that increasingly, we need bilingual AI talents. They are people whose “mother tongues” are their domain or functional expertise. It is a language they have already mastered. With help, they can learn a new language – the language of their new AI teammate – and become fluent in it. This means acquiring AI-related skills that will allow them to work with AI practitioners or specialists to transform their work and improve outcomes.

I am glad that ST Engineering is also growing a pool of bilingual AI talents. You have many domain experts, for example those who build satellites and launch them into space for haze and weather monitoring. Your space engineers know how a satellite should be designed and tested to optimise its power efficiency, weight, and reliability, among other factors. This deep domain expertise is like their “mother tongue”, a language they have known a long time. While your space engineers are experts in their own field, they are also learning to be fluent in the language of their AI teammate. This new language is opening up another world of knowledge and opportunities for them. For example, they are using AI to optimise the structural layouts of satellites and efficiently test thousands of design permutations.

We believe these bilingual AI talents and their AI teammate are a formidable team. They will be pathfinders and pacesetters for meaningful AI adoption not just in ST Engineering but everywhere.

Since I shared this point of view, many people have told me it gives them a sense of hope, that they too can learn and be valued in the AI age, despite not being AI specialists.

At the same time, they tell me learning a new language is not easy. Where do we start?

In fact, many Singaporeans have started. According to OpenAI, usage of ChatGPT in Singapore is among the highest globally. One could say this represents early attempts at gaining fluency in the language of AI. We learn to speak a language by listening. Using AI tools like ChatGPT is like “listening to AI”, to get a sense of how it sounds.

Listening is good but not enough for fluency. We need opportunities to speak the language and learn from our mistakes.

Some Singaporeans appear to be doing just that. IMDA will soon be releasing the 2025 edition of the Singapore Digital Economy (SGDE) Report. It shows that three out of four workers surveyed are already using AI tools in their work regularly. These include tools like Cursor for software engineers, and customised AI tools built by Razer and ST Engineering for your needs. 85% of them say AI makes them more efficient and improves their work quality. But I’m sure this is still just the beginning.

The Government will help our businesses and people go beyond the “listening” and learn to “speak” the language of AI fluently. These are the building blocks for broadening and deepening AI adoption that will yield good results over the longer term.

First, for the broad base of enterprises, including SMEs, we will focus on ensuring that employees are equipped with the know-how to make full use of AI-enabled solutions. This includes IMDA working with tech vendors to bundle training as part of the AI solutions they offer.

Second, through our flagship TechSkills Accelerator (or TeSA) programmes driven by IMDA, we will also focus on developing AI-fluency amongst both our non-tech and tech professionals. We will partner professional bodies, such as in accounting and HR, to identify and build the skills and knowledge required by non-tech professionals to optimise core activities in their functions through AI and provide services that they previously were unable to do. At the same time, IMDA is working with Institutes of Higher Learning and other training partners to design pathways for tech professionals to acquire or deepen AI-related skills.

Businesses also have a part to play. Take Wang Zhihao, who joined ST Engineering as a fresh gradate in 2020. Through your R&D initiatives, Zhihao collaborated with the University of Cambridge, NTU, and SMU, and deepened his expertise in AI, cybersecurity, and the intersection between the two. With support from EDB’s Industrial Postgraduate Programme, ST Engineering is now sponsoring Zhihao’s PhD studies in AI Security.

I have focused my remarks today on why it makes sense to nurture bilingual AI talents and AI fluency in our workforce. It aligns with how JP suggested in his speech, for us to think of AI as part of our winning team. To work with this new teammate, we need to learn its language, to understand and communicate effectively with each other.

In fact, learning new “languages” is not new to Singaporeans.

From the 1970s, we’ve learnt the language of computers, the language of the internet, the language of mobile. Today, we are learning the language of AI.

To speak a language fluently, we need people to practise with. It is easier to do so if we are immersed in environments where many others speak the language. This is why I commend ST Engineering’s plans to equip 4,000 of your engineers and project managers with AI-related skills. You are systematically developing bilingual AI talent capable of embedding AI into their processes and solutions for customers. At the same time, you are nurturing a whole community of learners to practise and become fluent in the language of AI. Together with the 1,000 AI specialists, they will make a formidable team!

I hope ST Engineering will continue strengthening your AI talent pool and partner the government on this journey, such as through new collaborations under IMDA’s TeSA initiative and by contributing real-world problem statements so that our training programmes are relevant.

I encourage everyone to do the same and wish you all a fruitful conference ahead.