🏭 AI in Manufacturing: Why On-Premise Solutions Are Preferred Over Cloud? 🚀

[Hasan S. Cemkan]

A Conversation with Tim Juan on Industrial Artificial Intelligence​

Tim Juan, NEXCOM's vice president of IoT automation, explains why industrial artificial intelligence (AI) infrastructure is preferred with on-premise solutions rather than the cloud. According to Juan, manufacturing and critical infrastructure operators continue to prioritize on-premise infrastructure for AI deployments. This differs from the "cloud-first" approach prevalent in the general market.

🤔 Why On-Premise AI? Security or Data Volume?​

Juan states that manufacturers are aware that industrial AI decisions need to be made where the work is done. In a factory environment, AI supports equipment, robots, or production processes that cannot tolerate delays. Factors such as security, data ownership, and bandwidth make on-premise AI more attractive than cloud systems. However, the most important reason is that operators want reliable performance in environments where even a brief interruption can affect safety or production output.

Therefore, he adds that edge AI continues to be the preferred architecture for operational workloads, while the cloud plays an important role for broader analytics and long-term optimization.

📊 The Importance of Control: NEXCOM Survey Data​

NEXCOM's survey data shows that approximately 95% of organizations prefer on-premise or edge-based AI inference over general cloud deployments. This indicates that control is a key concern for manufacturers.

For industrial operators, "control" means ensuring that critical systems respond as predicted. It also involves maintaining visibility into operational data, deciding where this data will be processed, and ensuring that AI supports existing production requirements rather than creating new uncertainties.

Running AI at the edge provides manufacturers with direct oversight into how decisions are made and allows these systems to continue operating even if network connectivity changes. In industrial environments, this level of consistency is often as valuable as the intelligence itself.

☁️ Will the Gap Between Cloud and Edge AI Close?​

Juan believes this gap will narrow over time but does not think industrial AI will become entirely "cloud-first." As manufacturers become more comfortable with AI deployments, he predicts they will build architectures that combine the strengths of both environments.

While real-time inference, robotics, and machine control will continue to benefit from edge computing, the cloud will remain more suitable for model development, fleet management, and analyzing data across multiple sites. Juan states that over time, we will see a more balanced approach, with organizations placing each workload where it provides the most value.

💰 Priorities in Industrial AI Spending​

Manufacturers often invest in AI in areas where they can improve daily operations. These areas include machine vision, robotics, predictive maintenance, and production optimization, where improvements in quality, uptime, and efficiency can be measured.

Compared to many enterprise AI deployments, industrial companies tend to spend more on strengthening core infrastructure (including edge computing, data collection, and system integration). This aligns with the common view that reliable AI depends on a reliable operational foundation.

This priority is particularly evident among small and medium-sized manufacturers. Instead of starting with large, enterprise-wide AI transformation projects, most are looking for practical entry points that can deliver measurable operational value.

With this approach, industry leaders are directing their AI spending towards platforms that can connect existing CNC machines and production data. The goal is to first establish a reliable data foundation, and then deploy AI applications that can support daily decisions and continuous improvement. This allows manufacturers to start with a focused deployment, demonstrate business value, and expand over time without changing their existing operational systems.

🎯 Which AI Workloads Should Stay at the Edge, Which in the Cloud?​

The distinction is often made based on timing and operational impact. Workloads involving machine control, robotics, quality inspection, or safety-related decisions are generally more suitable for the edge, as they require immediate responses and uninterrupted operation.

The cloud, on the other hand, is valuable for tasks such as training AI models, analyzing production trends across multiple facilities, or managing large amounts of historical data. Instead of asking whether AI should be at the edge or in the cloud, manufacturers evaluate each workload based on its latency requirements, reliability needs, and the consequences of delayed decision-making.

Production monitoring is a practical example of this. AI continuously assesses equipment status, machine utilization, and process conditions directly on the factory floor. Processing this information locally allows operators to identify anomalies and respond immediately, even if network connectivity is limited.

For workloads that directly impact production, reliability and response time are often more important than centralizing every piece of data. The cloud still has a valuable role in consolidating cross-facility data, retraining models, managing software updates, and identifying broader operational trends. Instead of favoring one environment over the other, manufacturers are increasingly matching each workload to the environment that provides the greatest operational value.