AI Disruption & Businesses of Tomorrow: An Exclusive Interview with Leaders Navigating a New World Order

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, and Knovel Engineering.

 

AI Landscape & Outlook

In the recent 2-3 years, what are some of the significant developments you’ve observed in the field of AI? What stood out for you?

Over the past 2–3 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 and accelerated its accessibility and everyday relevance, moving from niche experimentation to mainstream use, reshaping how people and businesses work, create, and interact with technology.

A major highlight, shared by Hee Chuan from Knovel Engineering and Laurence from AI Singapore, is the democratization of AI. Tools like ChatGPT and Claude have made AI user-friendly, encouraging wider adoption. However, Hee Chuan noted that adoption still lags in some sectors due to unclear ROI.

Laurence 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 from JJ Innovation highlighted rapid advancements in AI applications across media—text, video, and audio—citing tools like Google’s Veo 3 that showcase remarkable improvements in realism and capability.

Together, these insights illustrate an AI landscape evolving in technology, reach, and strategic impact.

 

AI Adoption in Singapore

Can you share some examples of successful AI transformation projects you have led or been involved in?

The projects shared by the interviewees demonstrate that AI has been applied across diverse sectors to streamline both operational and strategic business functions—ranging from backend automation to enhancing user interfaces and decision-making. Broadly, these initiatives highlight AI’s role in optimizing workflows, improving user experiences, and augmenting human decision-making.

Carol from JJ Innovation, for instance, led a project using Natural Language Processing (NLP) to analyze employee feedback at a global tech firm, significantly accelerating HR insights and responsiveness.

Daniel from JJ Innovation 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.

Laurence from AI Singapore, 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 optimization for a local SME, uParcel. Collectively, these examples underscore AI’s versatility in transforming both routine and critical business functions.

Hee Chuan from Knovel Engineering shared that one of their customized workflow productivity tools, powered by AI, has helped a local heritage brand.

Read more: Unlocking Growth: AI Adoption for Singapore Businesses (Part 1)

 

Challenges & Solutions

What are some common challenges local companies face when adopting AI?

A key barrier identified by all three companies is data readiness—many organizations 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. Hee Chuan from Knovel Engineering 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.

Laurence from AI Singapore 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, overlooking the need for specialized infrastructure like GPUs, scalable storage, and MLOps tools.

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 linguistic and cultural context.

Read more: Support for AI Adoption for Singapore Businesses

 

What strategies or advice do you typically offer to help companies overcome these challenges?

To overcome common AI adoption challenges, interviewees advocated for a pragmatic, phased approach grounded in education, data readiness, and clear business objectives. A shared recommendation across all three companies was to start small—by piloting a focused project or proof of concept to test value and feasibility before scaling. This helps manage risk, demonstrate early wins, and build internal confidence.

Education and mindset shifts were also seen as critical. Interviewees emphasized the need to foster AI literacy across the organization to dispel fears and align expectations, citing initiatives like AI Singapore’s AI4E as effective tools for building internal readiness. Carol from JJ Innovation highlighted the importance of training and upskilling—through internal programs or new hires—to build the capabilities needed to support AI efforts.

Data readiness emerged as another key theme. Companies were encouraged to begin organizing their data early—even without a defined use case—to ensure it’s clean, accessible, and secure when needed.

Finally, Daniel from JJ Innovation stressed the importance of linking AI efforts to clear business problems, ensuring AI is adopted with purpose—not just for novelty.

Read more: Leverage Data to Enhance your AI Implementation Efforts

 

Between an off-the-shelf AI solution versus a customized solution, how can companies assess the right fit for their business need?

Interviewees agreed that choosing between commercial off-the-shelf (COTS) and customized AI solutions depends on the business problem, complexity, and AI maturity. Carol from JJ Innovation and Laurence from AI Singapore advised starting with clearly defined needs—COTS tools suit general use cases like chatbots, offering speed and cost-efficiency.

For unique workflows or competitive edge, custom solutions offer deeper integration. Daniel from JJ Innovation and Laurence highlighted their value in proprietary contexts.

Hee Chuan from Knovel Engineering suggested evaluating the operational workload first and whether a COTS can solve 70-80% of the existing problems. From there, he recommended to discern whether any higher value problems remained that could be solved by a customised solution.

Cost-benefit analysis, POCs, and internal capability assessments are key to making the right choice.

 

Once an AI project is completed, how do you advise your clients to transfer the necessary skills to their employees?

To ensure post-project continuity, interviewees stressed the need for structured knowledge transfer and internal capability building.

Laurence from AI Singapore shared that their 100 Experiments (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 and hire apprentices.

Carol from JJ Innovation highlighted the role of “change champions,” while Daniel recommended appointing “AI custodians.” The consensus: sustained success requires ongoing training, collaboration, and ownership.

 

What guidance do you offer clients to address employee concerns about job displacement due to automation or AI?

At the current state of AI, complete displacement of jobs and human intervention is still not possible. In addition to this, AI is not meant to replace human, it should augment human capabilities —one that simplifies and automates routine tasks to improve productivity and efficiency across businesses.

 

“AI adoption is less about wholesale reskilling and more about what one expert called “plus-skilling”

Laurence Liew, Director, AI Innovation,
AI Singapore

 

As Daniel from JJ Innovation 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.” This sentiment is further supported by the perspective of Laurence from AI Singapore, who shared 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

What metrics or indicators should organizations use to determine the extent of success of their AI adoption/transformation efforts?

 

“Define your organisation objectives first when determining the success metrics. An example of such metrics could be seen in the number of job redesigned or upskilled.”

Seah Hee Chuan, CEO,
Knovel Engineering

 

AI success should go beyond technical metrics like accuracy or speed. Laurence from AI Singapore emphasized that true indicators lie in business outcomes—such as deployment rates (not just pilots), time or cost savings, efficiency gains, revenue impact, and employee adoption. In their 100E projects, over 50% were deployed within six months, with results like uParcel’s 20% efficiency gain and Continental saving hundreds of man-hours. He also shared that tracking organizational maturity through frameworks like the AI Readiness Index (AIRI) and monitoring AI literacy efforts are also important. Hee Chuan from Knovel Engineering added that success can be seen in the number of jobs redesigned or upskilled, and that AI should be viewed as a long-term investment, not just a cost-saving measure.

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, establishing human oversight into the workflow—especially for high-risk use cases—and applying tools like AI assurance tests (such as Knovel Engineering’s DeepAssure) to validate safety and reliability throughout development.

 

What advice would you give to other organizations preparing for the future of work driven by AI and emerging technologies?

To prepare for the future of work driven by AI, organizations should start early by building a strong foundation—this includes digitalizing 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, empowering AI champions, and fostering a culture of curiosity and adaptability. Organizations should also prioritize human oversight by forming diverse, multidisciplinary teams—not just to drive innovation, but to identify design blind spots and 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—and long-term transformation is best supported through collaborative partnerships with startups, universities, and national programmes.

As AI reshapes the future of work and business, organizations yet to begin their transformation journey should start now. This doesn’t require a full overhaul—starting small by addressing existing pain points can drive productivity and efficiency. Success comes from continuous learning and experimentation, not quick wins.

📢 AI transformation is an ongoing process of growth and adaptation. As we embark on this journey, we would like to invite you to share your feedback in a short 10-minute survey and contribute to shaping related policies for Singapore businesses like yours:

Contribute to policymaking: Participate in our upcoming research studies by registering your interest.

 

About Our Interviewees

AI Singapore

AI Singapore (AISG) is a national programme launched by the National Research Foundation (NRF), Singapore, to catalyse, synergise and boost Singapore’s artificial intelligence (AI) capabilities to power our future digital economy. AISG will bring together all Singapore-based research institutions and the vibrant ecosystem of AI start-ups and companies developing AI products, to perform use-inspired research, grow the knowledge, create the tools, and develop the talent to power Singapore’s AI efforts.

Laurence Liew is a distinguished AI innovator and thought leader who has played a pivotal role in shaping Singapore’s AI landscape. As the Director of AI Innovation at AI Singapore, he spearheads groundbreaking initiatives that are transforming Singapore into a leading AI-First nation.

 

JJ Innovation

JJ Innovation is a forward-looking tech company focused on delivering practical, people-centric solutions. We help businesses navigate digital transformation with scalable, sustainable technology. From cloud services to smart platforms, our goal is to make innovation accessible, meaningful, and impactful—driving growth while empowering teams to thrive in a rapidly evolving landscape.

Carol Wong is the Regional Head of Tech Services at JJ Innovation. She champions simplicity in technology by starting small and building momentum. Committed to practical innovation and lifelong learning, Carol continually upskills to stay ahead in a fast-evolving tech landscape—empowering others to embrace technology with clarity and confidence.

 

Daniel Yip is a Technology Project Consultant at JJ Innovation, known for driving clarity and collaboration across complex initiatives. With a hands-on approach and a focus on outcomes, Daniel empowers teams to deliver with precision. He champions continuous improvement and thrives on turning challenges into structured, scalable solutions.

 

If you are interested to learn more about JJ Innovation or their products/services, you can contact Gary Wong, Managing Director, via email at gary.wong@jj-innov.com or Carol Wong, Regional Head of Tech Services, via email at carol.wong@jj-innov.com.

 

Knovel Engineering

Knovel Engineering is a technology consultancy and solution provider built on a spirit of innovation. We specialize in crafting novel AI and cloud-based solutions designed to shape what’s next. From data and analytics to scalable infrastructure, we deploy cutting-edge technologies that drive transformation and uncover new possibilities for our clients.

Seah Hee Chuan is the CEO of Knovel Engineering. With over a decade of leadership experience, he helps organizations grow and optimize through AI adoption. He also identifies emerging technologies world-wide to drive innovation and establishes strategic partnerships to accelerate innovation to bring real-world impact across diverse industries.

If you are interested in knowing more about Knovel Engineering or their solutions, please visit their website at www.knoveleng.com or email them at hello@knoveleng.com

 

This article is contributed by Bahvaani A, Assistant Research Manager, IndSights Research.