As commercial buildings evolve into intelligent, data-driven environments, the convergence of IoT, AI, real-time analytics, and low-power connectivity technologies is redefining the future of smart infrastructure management. From predictive maintenance and energy optimisation to indoor air quality monitoring and autonomous building operations, enterprises are increasingly leveraging connected ecosystems to improve operational efficiency, sustainability, and workplace experience.

In this interview with AI Spectrum, Lim Perng, VP APAC at Netmore, discusses how AI-ready smart building ecosystems are emerging through the integration of LoRaWAN, intelligent sensing, and advanced analytics. The conversation also explores the growing importance of digital twins, predictive automation, ESG-driven building strategies, and the role of the Netmore–Milesight collaboration in enabling scalable, future-ready smart infrastructure across large enterprise environments.

How is the convergence of IoT, real-time analytics, and AI reshaping the future of smart building management across commercial infrastructure?

We’re moving from buildings that are simply connected to buildings that are becoming intelligent and increasingly autonomous. Traditionally, building systems operated in silos; for example, HVAC, lighting, security, occupancy and energy management each generated data independently.

IoT allows us to connect these systems, real-time analytics helps us understand what is happening now, and AI adds the ability to predict and optimise what happens next. For example, instead of operating HVAC on fixed schedules, a building can dynamically adjust ventilation, temperature, and energy usage based on occupancy patterns and environmental conditions. Over time, this creates a digital representation of building operations, often referred to as a digital twin, where data continuously improves decision-making. The outcome is better operational efficiency, improved tenant experience, lower carbon footprint, and smarter asset management.

What role does LoRaWAN play in enabling scalable, low-power, and AI-ready smart building ecosystems, particularly in large enterprise environments?

LoRaWAN plays a foundational role because intelligence starts with reliable and scalable data collection. In large commercial environments, you may need to support hundreds or thousands of sensors across multiple floors or buildings. The challenge is doing that efficiently without excessive infrastructure costs or frequent battery replacements.

LoRaWAN is designed for exactly this use case: long range, deep indoor penetration, low power consumption, and support for massive device deployments.

But connectivity alone is not enough. To become AI-ready, you need reliable, high-quality data over time. Through Netmore’s carrier-grade LoRaWAN network and IoT platform, we ensure operational visibility, QoS, and scalability, creating a strong data foundation for analytics, automation, and future AI applications.

Given that your solutions generate large volumes of occupancy, environmental, and operational data, how can AI and predictive analytics help building operators derive actionable insights?

Data by itself does not create value; value comes from turning data into action. AI and predictive analytics can identify patterns that are difficult for humans to detect. For example, occupancy data can optimise workspace utilisation and hybrid work arrangements. Environmental data can improve HVAC performance and energy efficiency. Operational data can detect early signs of equipment degradation before failures occur. Instead of reacting to problems after they happen, building operators can shift toward predictive and proactive management.

The long-term vision is moving from dashboards and alarms toward systems that can recommend actions, and eventually automate decisions to improve operational efficiency and user experience simultaneously.

As Indoor Air Quality (IAQ) monitoring becomes increasingly important for employee wellness, how do you see intelligent sensing technologies evolving to support healthier and more adaptive workplaces?

Post-COVID, IAQ has shifted from being a facility management metric to becoming a workplace experience and wellness priority. The next evolution will move beyond simply measuring parameters like CO₂, temperature, humidity, or particulate matter. Intelligent sensing will become more contextual and adaptive. For example, sensors will increasingly combine occupancy, environmental, and behavioural data to dynamically adjust ventilation and indoor conditions in real time.

In the future, we’ll also see closer integration with AI and digital twins, enabling buildings to continuously learn and adapt to how spaces are used.

Ultimately, healthier buildings will not just react to poor conditions — they will anticipate and optimise them before users even notice.

What are the primary challenges organisations face when deploying connected smart building infrastructure at scale, and how does the Netmore–Milesight collaboration address these?

The biggest challenge is usually not installing sensors and gateways; it is maintaining reliability and scalability as complexity increases. In large commercial environments, organisations face challenges such as RF interference, multi-building coverage, interoperability between systems, long asset lifecycles, and integration with existing building infrastructure.

That is why ecosystem collaboration becomes important. The Netmore–Milesight approach combines reliable LoRaWAN connectivity and network intelligence with a broad portfolio of sensing technologies. Together with open standards and integration capabilities, this enables customers to deploy solutions more quickly while avoiding vendor lock-in. It also removes the deployment friction, allowing landlords to scale connected applications rapidly with minimal upfront infrastructure investment.

Ultimately, the end goal is not simply to connect devices; it is to create an architecture that remains scalable and adaptable over the long term

Looking ahead, how do you envision AI-driven automation influencing areas such as energy optimisation, predictive maintenance, and workplace experience over the next few years?

Looking ahead, I believe we are moving toward buildings that can increasingly operate as self-optimising systems. AI-driven automation will serve as the primary engine for automated ESG compliance and the full realisation of the autonomous building. AI won't just optimise energy day-to-day; it will forecast equipment failures months in advance, fundamentally optimising CAPEX. For older buildings, the combination of LoRaWAN and AI will be the strategic choice to decarbonise legacy assets without tearing down walls for a full-scale renovation.

The blueprint we’ve built today is ready to scale, and over the next few years, these digital threads will weave together the next generation of our sustainable global cities. The ultimate objective is not more technology, it is delivering better experiences, better efficiency, and better sustainability outcomes.