Singapore Signals Shift from AI Pilots to Operational Deployment Across the Economy
Singapore is sharpening its focus on moving artificial intelligence (AI) from experimentation into everyday operations, as part of a broader strategy to strengthen productivity and long-term economic growth. Speaking at SAP d-com, Senior Minister of State for Digital Development and Information Tan Kiat How highlighted the need for AI to be embedded into core business systems and workflows, supported by trusted platforms, sector expertise and a skilled workforce. The approach reflects Singapore’s wider ambition to translate AI potential into practical, scalable outcomes across the economy.
In his keynote address, published by the Ministry of Digital Development and Information, Tan underscored the importance of applying AI in real operational environments rather than limiting its use to proof‑of‑concepts. He said what stood out at the event was not just the technology itself, but how teams were designing AI solutions to work under real-world constraints and deliver tangible value to businesses and society.
From experimentation to operational AI
Tan described the current moment as an inflexion point, where organisations must move “beyond experiments to operations, beyond ideas to implementation”. This, he noted, requires rethinking core systems and processes in areas such as finance, compliance, supply chain management and human resources, so that they are designed with AI in mind while retaining appropriate human judgement.
Rather than layering AI superficially onto existing processes, Tan stressed the need for fundamental redesign. This perspective aligns with Singapore’s broader digital policy direction, which emphasises resilience and long-term capability building, as outlined in Singapore’s approach to building a resilient digital future.
AI as a strategic national priority
The speech also placed AI within Singapore’s national strategy. Tan referenced Budget 2026, where Prime Minister Lawrence Wong highlighted AI as a strategic priority and announced the formation of a National AI Council, which he will personally chair. The council will function as an inter-ministry platform to coordinate AI policy and implementation.
These efforts build on existing foundations. Over the past few years, more than 60 companies have established AI Centres of Excellence in Singapore to translate AI capabilities into practical applications. Tan pointed to SAP as a key partner in this ecosystem, noting that its Digital Innovation Accelerator Lab has worked with over 70 local enterprises on proof‑of‑concept projects, with 12 progressing to full-scale implementation.
Scaling adoption through enterprises and platforms
The next challenge, Tan said, is scaling this impact across the wider economy. Singapore’s National AI Impact Programme aims to see 10,000 enterprises integrate AI meaningfully into their workflows and to prepare 100,000 workers to be AI-ready. Achieving this will depend on the availability of reliable enterprise platforms that support real business processes.
Widely used enterprise systems, including those in finance, supply chain management and human resources, were highlighted as important channels for diffusing AI into daily work. By embedding AI, including agentic AI, into core systems, such platforms can help normalise AI use and support systematic transformation across organisations.
Developing “bilingual” AI talent
A significant portion of the address focused on talent. Tan observed that while AI tools are changing how technical work is performed, the role of engineers remains critical. Beyond writing code, engineers are increasingly expected to define system design, ensure data integrity, manage governance and anticipate risks.
He also emphasised the growing need for what he termed “bilingual” AI professionals—individuals who combine technical AI expertise with deep understanding of specific business domains. Such talent is essential for judging when AI adds value, balancing ideal solutions against real-world constraints, and ensuring that AI is applied appropriately.
This focus on skills development is reflected in national workforce initiatives, including IMDA’s TechSkills Accelerator (TeSA). The programme is being enhanced to support both tech and non-tech workers in integrating AI into domain-specific workflows, complementing broader efforts discussed in Singapore’s upskilling strategy for a sustainable, tech-savvy workforce.
Industry partnerships to build capability
Tan stressed that government action alone is insufficient, noting that effective skills development often happens on the job. He welcomed SAP’s role as a talent development partner, highlighting its hiring of AI graduates and its internal programmes that combine technical training with real project experience.
He announced that SAP will partner with IMDA to hire and train 50 AI Scientists and Machine Learning Engineers over three years under TeSA’s Company-Led Training programme. Participants will receive structured training while working on AI projects within SAP Labs, contributing to Singapore’s pool of applied AI expertise.
Concluding his address, Tan said such partnerships give confidence that Singapore can develop a strong community of AI builders who combine technical capability with domain knowledge to deliver real economic outcomes. The emphasis, he noted, is not on AI for its own sake, but on its responsible and effective use to support the country’s ongoing transformation.
