How an “edge to industry” architecture enables ASUS to solve industrial AI challenges

According to Albert Chang, ASUS Corporate Vice President and Co-Head of AIoT BG, an “edge-to-industry” architecture that enables organizations to move from isolated proofs of concept to repeatable and scalable AI operations.

As AI adoption among enterprises continues to soar, the manufacturing industry is speeding up its AI adoption. Given the increasing competition in the manufacturing industry as well as supply chain concerns, enabling AI to optimize processes to automate and improve decision making with real time data is becoming a key goal for manufacturers globally.

Referred to as Industrial AI, it’s a combination of physical AI and other AI technologies. According to a study by Juniper Research, deployments of physical AI systems in manufacturing and logistics will reach 400,000 by 2030. This is an increase of 3,500% from 2026 which is driven by advances in real-time processing and AI models.

For technology vendor Asus, for industrial AI to work best, having the right infrastructure to enable it is key. As such, the vendor is focused on helping organizations solve the common challenges they face when developing and deploying industrial AI applications.

Albert Chang, ASUS Corporate Vice President and Co-Head of AIoT BG, and General Manager of the Motherboard BU, shares more in an interview with CRN Asia.

How is ASUS helping organizations deal with the challenges in Industrial AI?

Industrial AI has proven its value in pilot projects such as computer vision, predictive maintenance, and autonomous material handling. However, many organizations face a common challenge: while pilot projects succeed, scaling them across multiple factories or sites is difficult.

ASUS addresses this challenge by focusing on building an “edge-to-industry” architecture that enables organizations to move from isolated proofs of concept to repeatable and scalable AI operations. By deploying compute close to machines and sensors at the edge, ASUS solutions help reduce latency, improve reliability, and manage data movement more effectively.

ASUS supports these deployments with a comprehensive industrial portfolio that includes rugged rackmount systems, fanless DIN-rail computers (industrial-grade embedded PCs), industrial servers, and hardened networking. ASUS enables organizations to deploy the right level of compute infrastructure for different industrial environments while maintaining centralized manageability.

Do organizations need to overhaul their infrastructure to support Industrial AI, or can they upgrade based on their needs?

Organizations do not necessarily need to completely overhaul their infrastructure to adopt Industrial AI. Many successful deployments start with targeted implementations at specific operational points.

ASUS supports this approach by promoting a modular architecture, where organizations can deploy standardized compute tiers ranging from compact edge endpoints to high-performance servers depending on the requirements of each site. This allows companies to introduce AI capabilities gradually and expand deployments as operational needs grow.

Such an approach helps organizations scale AI more efficiently while maintaining flexibility across different industrial environments.

Why should organizations use ASUS Industrial AI solutions? How are they different from other vendor products?

ASUS Industrial AI solutions are designed specifically for the realities of industrial environments, where reliability, durability, and scalability are critical.

ASUS platforms are engineered to operate under challenging conditions, including wide operating temperatures, vibration, and demanding physical environments commonly found in factories, transportation infrastructure, and outdoor installations.

In addition, ASUS focuses on delivering a complete edge infrastructure foundation, combining compute platforms, industrial networking, and ecosystem collaboration. Through the ASUS IoT Partner Alliance Program, ASUS works with industry partners to integrate domain-specific software with ASUS edge computing platforms, enabling organizations to deploy practical and scalable AI solutions.

The destination is clear: an edge to industry architecture where AI is not an experiment but a dependable capability of everyday operations—from the factory floor to city streets.

Can you share a few success stories with customers that have deployed ASUS Industrial AI systems?

ASUS collaborates with partners across multiple industries to support real-world Industrial AI deployments.

In manufacturing, collaborations with partners such as Comau demonstrate how on-site AI inferencing can enable predictive maintenance, helping teams anticipate equipment failures and optimize maintenance schedules.

In urban environments, ASUS works with CTHINGS.CO to power recycling kiosks that use sensor fusion and AI-based fraud detection to validate returns and automate refund processing.

Additional deployments include partnerships with Key Future for reliable parking and payment infrastructure, and with Stereolabs for warehouse environments where real-time spatial perception can improve navigation and operational safety.

These examples illustrate how properly designed edge infrastructure enables AI deployments to move beyond pilot projects and become part of daily operations.

How is ASUS dealing with the RAM shortage, and is that impacting the cost and deployment of Industrial AI products?

Industrial AI deployments depend on a range of hardware components, and supply chain conditions can vary over time. ASUS works closely with its global supply chain partners to maintain stable component sourcing and ensure continuity for industrial deployments.

In addition, ASUS designs its platforms with modular configurations, allowing customers to select system specifications based on their specific AI workload requirements. This flexibility helps organizations manage performance and cost while maintaining predictable deployment timelines.

Moving forward, we will continue to work closely with all our DRAM solid-state suppliers and respond flexibly, including by increasing inventory further. As for pricing in the channel, we will take into account overall costs, channels, partners, and consumer demands. We will adjust the product mix accordingly and make price adjustments where appropriate. This will be managed as a flexible and dynamic process.

Lastly, what’s next for ASUS in Industrial AI?

ASUS sees Industrial AI transitioning from isolated deployments to scalable platforms that organizations can implement across multiple sites and regions.

Moving forward, we will continue to advance our edge-to-industry architecture, focusing on rugged edge computing platforms, compact intelligent endpoints, and industrial-grade networking infrastructure.

ASUS also plans to deepen collaborations with our ecosystem partners to combine ASUS platforms with domain-specific applications, enabling organizations to deploy AI solutions more reliably across diverse industrial environments.