Artificial Intelligence is rapidly reshaping how we work and live. As NVIDIA CEO Jensen Huang said, “AI will be the most transformative technology of the 21st century.” This shift is already underway, especially in Singapore’s dynamic AI landscape.
To better understand this transformation, we spoke with three key players:
- AI Singapore
- JJ Innovation
- Knovel Engineering
AI landscape and outlook
Over the past two to three years, AI has undergone a transformative shift, with generative AI and large language models (LLMs) emerging as the most significant developments noted by all interviewees. These technologies have expanded AI’s capabilities, reshaping how people and businesses work, create, and interact with technology.
A major highlight, shared by Hee Chuan, Founder & Chief Executive Officer of Knovel Engineering and Laurence Liew, Director of AI Innovation at AI Singapore, shared that tools like ChatGPT and Claude have made AI user-friendly, encouraging wider adoption. However, Chuan noted that adoption still lags in some sectors due to unclear ROI.
Liew also pointed out a global shift towards building local AI talent and readiness, inspired by Singapore’s AI Apprenticeship Programme (AIAP) and AI Readiness Index (AIRI). Daniel Yip, Technology Project Consultant at JJ Innovation highlighted rapid advancements in AI applications across media—text, video, and audio. Tools like Google’s Veo 3 showcase remarkable improvements in realism and capability.
AI adoption in Singapore
The projects shared by the interviewees demonstrate AI’s application across sectors to streamline both operational and strategic business functions.
Carol Wong, Regional Head of Technology Services at JJ Innovation, led a project using Natural Language Processing (NLP) to analyse employee feedback at a global tech firm, significantly accelerating HR insights and responsiveness.
Yip shared how integrating a Generative AI assistant into a logistics company’s systems simplified complex processes, enabling non-technical staff to manage inventory and documentation through a prompt-based interface.
Liew illustrated the breadth of AI’s impact—from real-time multilingual emergency call transcription for SCDF to AI-enhanced dental diagnostics with Q&M Dental Group, and even route optimisation for a local SME, uParcel. Collectively, these examples underscore AI’s versatility in transforming both routine and critical business functions.
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Chuan shared that one of their customised workflow productivity tools, powered by AI, has helped a local heritage brand—HarriAnns Nonya Table—transform its manual backend ordering process from hotels and its own cafes to a centralised kitchen, streamlining operations and reducing human errors.
Challenges and solutions
A key barrier identified by all three companies is data readiness—many organisations lack sufficient data, have fragmented or poor-quality datasets, or lack the infrastructure to prepare data effectively for AI.
Mindset and cultural resistance also pose major obstacles. Chuan noted that unrealistic expectations—such as seeking one-size-fits-all “silver bullet” solutions—and common misconceptions, like fears of job loss or over expectations of AI’s current capabilities, continue to hinder progress.
Liew highlighted the lack of internal expertise, especially among SMEs, where teams may not have the technical skills to deploy or maintain AI systems. He also pointed out that many companies wrongly assume their existing IT setups are AI-ready.
JJ Innovation further noted difficulties in identifying practical use cases and adapting AI models trained on Western data, which may not reflect Singapore’s unique cultural context.
To overcome common AI adoption challenges, interviewees advocated for companies to start small—by piloting a focused project or proof of concept to test value and feasibility before scaling.
Interviewees emphasised the need to foster AI literacy to dispel fears and align expectations. Wong highlighted the importance of training and up-skilling to build the capabilities needed.
Companies were encouraged to begin organising their data early to ensure it’s clean, accessible, and secure.
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Finally, Yip stressed the importance of linking AI efforts to clear business problems, ensuring AI is adopted with purpose—not just for novelty.
To ensure post-project continuity, interviewees stressed the need for structured knowledge transfer and internal capability building.
Liew from AI Singapore shared that their 100E programme involves internal engineers from the start, with sprints, testing, documentation, and formal handovers. Companies are also encouraged to train staff in foundational AI.
Wong highlighted the role of “change champions,” while Yip recommended appointing “AI custodians.” The consensus: sustained success requires ongoing training, collaboration, and ownership.
At the current state of AI, complete displacement of jobs and human intervention is still not possible.
As Yip explained, “AI is not out to replace your job just yet. In the present, AI should be thought of as an assistant to boost your effectiveness in your current job.”
Liew supported by sharing that AI adoption is less about wholesale reskilling and more about what one expert called “plus-skilling.” He elaborated, “For example, an accountant doesn’t need to become a data scientist; rather, they should remain an accountant who is now empowered to use AI tools effectively in their daily work.”
Moving forward
AI success should go beyond technical metrics like accuracy or speed. Liew emphasised that true indicators lie in business outcomes—such as deployment rates, time or cost savings, efficiency gains, revenue impact, and employee adoption.
He shared that tracking organisational maturity through frameworks like the AI Readiness Index (AIRI) and monitoring AI literacy efforts are also important. Chuan added that success can be seen in the number of jobs redesigned or up-skilled, and that AI should be viewed as a long-term investment, not just a cost-saving measure.
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Equally critical is embedding responsible design principles from the outset. Interviewees consistently stressed that ethical standards and compliance should be treated as key measures of AI success, not afterthoughts. Ensuring AI solutions are trustworthy, explainable, and human-centric requires maintaining governance frameworks and establishing human oversight in the workflow to validate safety and reliability throughout development.
To prepare for the future of work driven by AI, organisations should start early by building a strong foundation—this includes digitalising processes, preparing clean and structured data, and developing AI literacy across all levels of the workforce. Success comes from starting small, experimenting quickly, and learning by doing, rather than waiting for perfect conditions.
Equally important is shaping employee mindsets and fostering a culture of curiosity and adaptability. Organisations should also prioritise human oversight by forming diverse, multidisciplinary teams—not just to drive innovation, but to ensure AI systems remain understandable and trustworthy. After all, trustworthy AI is not just about meeting compliance standards; it’s about building systems people can understand and rely on.
Ultimately, AI should be viewed as a tool to augment human capabilities, not replace them. Long-term transformation is best supported through collaborative partnerships with startups, universities, and national programmes.
As AI reshapes the future of work and business, organisations yet to begin their transformation journey should start now. Starting small by addressing existing pain points can drive productivity and efficiency. AI transformation is an ongoing process of growth and adaptation.
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