SMS陳杰豪在SAP d-com發表的主旨演講

Dr Philipp Herzig, Chief Technology Officer

Mr Simon Davies, Regional President of SAP APAC

Mr Manik Saha, Managing Director for SAP Labs East Asia

Ms Eileen Chua, Managing Director for SAP Singapore

Ladies and gentlemen.

Good morning. It is a great pleasure for me to be here today at SAP d-com.

I am very happy to be able to come by and see the very promising work by very talented individuals and I enjoyed the showcase of agentic solutions earlier.

What stood out to me was not just the technology, but taking the technology and applying it to make a meaningful difference to business and the society. Today, I saw how we are taking AI from “interesting” to “useful”, and from “possible” to “deployable”.

What also stood out to me was the teams’ efforts to build solutions designed to work in real operations, under real constraints.

In many ways, this represents the inflexion point the world is facing.

To really harness AI, we must move beyond experiments to operations, beyond ideas to implementation.

We must be able to transform the core systems and business processes that help organisations run smoothly – in Finance, Compliance, Supply Chain Management, Human Resources, and more – with AI in mind, and think about how to design systems and processes to make full use of AI’s capabilities while still maintaining the human’s ability to judge.

We believe that this is important to Singapore's future economic growth. Singapore may not have the resources, land, power, people, or market size to have the most frontier AI models, but we believe that we can play a useful role in this world of AI, specifically in areas where our sectors are strong.

We have a good ecosystem of capabilities in finance, banking, insurance, power plant, logistics, connectivity, advanced manufacturing, but you still want to look at the world through AI’s lenses. It is not just about applying AI or technology superficially to existing processes or systems – that will never work. You have to fundamentally transform them.

So whether it's call back systems, telecommunications, workflows in procurement, f inance, or supply chain, how are you working with the business owners and industry to fundamentally redesign the workflows and systems in the age of AI? And that, I think, is something that Singapore believes that we can play a part in, and we look forward to working with like-minded partners to pathfind and think about what the future of different sectors looks like. We may not have all the answers, but I think that we can chart out the contours of future here in Singapore.

This is also why much of the recent Budget 2026, Prime Minister Lawrence Wong highlighted the importance that Singapore will place on AI as a strategic competitor. In fact, he set up and will personally chair the National AI Council, an inter-ministry workgroup.

We are doing this off good foundations.

Over the past few years, more than 60 companies have set up their AI Centres of Excellence here, to translate AI’s potential into practical applications.

SAP has been an important partner in this effort.

SAP’s Digital Innovation Accelerator Lab, or “DIAL 3.0” has collaborated with over 70 Singapore enterprises to develop proof‑of‑concept solutions.

12 of which have moved into full-scale implementation, driving greater efficiency, cost savings, and improved customer experiences on the ground.

The task before us is now to scale this impact more widely across the economy.

Our National AI Impact Programme (NAIIP), announced at MDDI’s Committee of Supply debate, sets out our ambition.

To have 10,000 enterprises integrate AI meaningfully in their workflows; and

To uplift 100,000 workers to be AI-ready.

But for enterprises and workers to adopt AI meaningfully, they need trusted platforms and solutions that support real business processes and workflows.

This is where widely used enterprise platforms like SAP play an important role.

By embedding AI capabilities, including agentic AI, into its core product offerings for areas like Finance, Supply Chain Management and HR, SAP can help:

Diffuse AI into the daily workflows of workers and enterprises, helping them deploy AI more systematically; and

Through this, catalyse transformation across our enterprise ecosystem.

All of this is impossible to achieve if we don’t have the right talent.

So allow me to briefly touch on two trends we’ve been seeing, and how we are working with our Institutes of Higher Learning and industry to respond.

The first, is how AI tools are reshaping the nature of technical work.

If AI tools like Claude or Cursor can now generate entire code blocks, is there a need for engineers?

In truth, writing code is only part of the value that engineers bring. I always say, when an engineer knows how to write code, you are a decent engineer. If an engineer knows how to solve the problem, you are a good engineer. If an engineer knows whether it is the right problem to solve, you are an excellent engineer.

What matters more is whether that code helps to solve a real business problem, and whether it is scalable, cost-efficient, and secure. Fundamentally, it is about whether you are helping your clients solve the most important problem for their business, and that requires more than just AI knowledge. It is wisdom, experience, and understanding the domain, client, and issues.

I believe that engineers continue to have a strong role to play at the macro level, by driving specifications, system design, data integrity, governance, and anticipating risks before they happen.

This brings me to the second trend, which is the increasing need for what we call “bilingual” AI talent – professionals who are fluent in both AI and a business domain.

In order for our engineers to work at this macro level, we need talent who can:

Understand the technicalities behind business functions that their work support – workflows, outcomes, and priorities;

Know what “good enough” looks like in the real world because we cannot always aim for ideal and the best engineering solution must have the wisdom to judge cost benefits and adapt as we go along; and

Can accurately judge to tell when AI truly adds value, and equally important, when it might not be the right tool for the job, and not blindly use AI because at the end of the day, it is about the value it brings and applying it at an appropriate juncture to solve the problem.

Take Jasmine Quek from the team, for example.

Jasmine is a Machine Learning engineer by training.

But over the past one and a half years, Jasmine has had to build up a deep understanding of finance workflows, treasury policies, and account-balancing requirements.

This is because Jasmine and her team have been designing and building a “Cash Management Agent” – an AI agent that performs daily cash‑positioning work.

I’m glad to hear they were very successful, building a solution that helped reduce the average time to monitor bank statements and cash positions per bank group from seven mins to two mins.

These trends are why IMDA’s TechSkills Accelerator, or TeSA, is focusing its efforts on equipping the workforce with the capabilities to integrate AI more deeply across domains and workflows.

Beyond supporting tech workers to move beyond writing code and towards orchestrating end-to-end systems powered by AI agents, the TeSA initiative will be enhanced to develop more AI bilingual workers.

Non-tech workers can also develop practical AI capabilities so that they can leverage AI to transform domain-specific workflows and boost productivity.

But this is not something that the Government can do alone. After all, the best training is really one which is received on the job.

I am therefore very heartened that SAP has been a steadfast talent development partner to the Singapore ecosystem.

Over the past two years, SAP has hired many promising AI graduates from our universities, as Scientists, Engineers, and Data Specialists. I have met a few of them just now. In doing so, SAP has also given them the chance to work on real, meaningful projects, with mentors who guide them closely and patiently.

SAP is also actively helping its engineers build their capabilities to take on higher order work, through internal learning programmes that pair deep technical training on AI fundamentals with real on-the-job experience alongside domain experts.

So, I am happy to announce that SAP will partner IMDA to hire and train 50 AI Scientists and Machine Learning Engineers over three years. This is SAP's first project supported under TeSA’s Company-Led Training programme. Participants will pick up critical AI and data skills through structured training and working on AI projects in SAP Labs.

It is partnerships that give me the confidence that Singapore will develop the community of AI builders, engineers and companies who can harness both AI and domain knowledge to deliver real outcomes to our economy, and support its ongoing transformation.

On that note, thank you once again to SAP and all of you here today.

I look forward to what we will build together in the months ahead.

And wish you all a fruitful day at d-com.