Kenny Yeo: To start, could you tell us a bit about NEC and the work of NEC Laboratories Singapore?
Christopher Lam: Thank you very much. NEC is a Japanese multinational company headquartered in Tokyo, focused on delivering ICT solutions and services to governments and large enterprises worldwide.
Here in Singapore, NEC Laboratories Singapore is one of NEC’s 11 R&D centres globally. We focus on applied research—developing innovative AI solutions to support digital transformation for our customers, particularly in government and enterprise sectors. Our mission is to turn research into real-world applications that help our clients modernise securely and effectively.
Kenny Yeo: AI seems to be a central theme in NEC’s work. How significant is it for the company today?
Christopher Lam: AI is definitely a major focus for NEC. In fact, most of our laboratories are involved in AI-related research in some ways. We’ve even developed our own large language model, Cotomi, which was trained using NEC’s AI supercomputer in Japan—powered by nearly a thousand GPUs.
Generative AI has opened up new possibilities, and NEC is investing heavily in creating secure, explainable AI systems that organizations can trust. We want to ensure AI doesn’t just remain an R&D exercise but delivers real value in production environments.
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Kenny Yeo: Why do you think AI transformation is so critical for organizations today?
Christopher Lam: In today’s digital economy, speed, efficiency, and customer centricity are essential for growth and sustainability. AI has proven to be a key enabler across these dimensions.
For example, many tech companies now say that up to 30% of their code is written by AI—accelerating development and improving quality. Meanwhile, AI-driven tools such as chatbots and digital humans are transforming customer service, offering faster and more accurate responses than traditional call centres.
In short, AI helps businesses do more with less—reducing time, improving consistency, and freeing people to focus on higher-value work.
Kenny Yeo: What are some of the biggest challenges organizations face when embarking on AI transformation?
Christopher Lam: One common challenge is overly high expectations. Many customers expect AI to be a magic solution, capable of solving every problem instantly. The truth is, AI systems are powerful but have clear limitations. That’s why education is critical—we work closely with customers to set realistic goals and clarify what’s achievable.
Another challenge is accuracy. Every AI system has performance limits depending on data quality, environment, and scope. The key is to align on acceptable accuracy levels from the start and validate them through pilot projects or proofs of concept (POCs) before scaling.
Without this groundwork, projects can suffer from misaligned expectations and disputes down the road. Clear communication and incremental progress are essential.
Kenny Yeo: How can organizations secure management buy-in for AI initiatives?
Christopher Lam: Fortunately, it’s easier today than a few years ago—most leaders already recognise AI’s strategic importance. But scepticism remains, especially among those who haven’t seen successful implementations firsthand.
To build confidence, organizations should share success stories and benchmarks from similar use cases. Training and education are equally important so project teams speak a common language and understand the challenges ahead.
Another key point: AI transformation doesn’t end after deployment. Models degrade over time due to changing environments—a phenomenon called model drift. Continuous monitoring, retraining with updated data, and lifecycle management are crucial for long-term success.
And, of course, data privacy and governance remain top priorities. We apply standard cybersecurity measures—hardening, access control—but also additional privacy practices like data anonymization, masking, and minimal retention. These help ease management concerns about compliance and risk.
Kenny Yeo: From a technical standpoint, what should organisations prioritise when starting their AI journey?
Christopher Lam: Data—both quality and quantity—is absolutely fundamental. A model is only as good as the data that trains it.
Take a video analytics example: a system trained to detect fights might misclassify people playing basketball as fighting if the dataset isn’t diverse enough. Context matters. That’s why we emphasise localized training with real operational data to ensure models perform accurately in their intended environment.
So, while pre-trained models are a good start, they’re never sufficient alone. Tailoring and retraining with context-specific data are essential to achieving business-grade performance.
Kenny Yeo: Data privacy has become a global concern. How does NEC manage this within AI projects?
Christopher Lam: Every region has its own data protection laws—PDPA in Singapore, GDPR in Europe, CCPA in California, and so on. We design our systems to be adaptable to these requirements and always work closely with customers to ensure compliance.
For instance, in a U.S. airport project during the pandemic, regulations prohibited us from retaining images of individuals who didn’t show fever symptoms. Our system automatically deleted such footage while masking personal identifiers for others.
This example highlights our approach: privacy by design. We store only what’s necessary, anonymize wherever possible, and retain data for the minimum required duration. These principles make compliance smoother and build trust with clients and end-users alike.
Kenny Yeo: Finally, what practical advice would you give to companies just beginning their AI journey?
Christopher Lam: Start now—and start small. AI is here to stay, and delaying only widens the competitive gap. Even simple tools like ChatGPT or Microsoft Copilot can help employees write reports, code, or emails more efficiently.
Encourage experimentation within safe boundaries. Once teams gain confidence and understanding, scale up to business-specific use cases that improve productivity or customer experience.
AI transformation isn’t just a technology shift—it’s about future-proofing your organization. Embracing AI helps attract and retain talent, enhance efficiency, and deliver smarter, more responsive services to customers.
Key Takeaway
AI transformation is no longer optional—it’s a journey every organization must undertake thoughtfully. As Christopher Lam emphasizes, success comes from education, realistic expectations, robust data governance, and a willingness to learn and adapt.
From NEC’s perspective, the goal isn’t simply to adopt AI—it’s to use it responsibly, securely, and sustainably to unlock innovation that matters.
About Christopher Lam
Christopher Lam is Vice President and Head of NEC Laboratories Singapore (NLS), responsible for the strategic direction, vision, growth and performance of the Singapore-based solutions lab’s cutting-edge research and development. Christopher is responsible for maximizing operational excellence and as one of its chief architects, transforming it into one of the most advanced Applied Research Labs NEC operates globally.
