How AI Agents Are Performing Manufacturing Operations

In the past, the core philosophy behind performing manufacturing operations revolved around the concept of collecting information, analyzing reports, and making decisions accordingly. This formula worked as long as the production environment was predictable and products were simple. The scenario is no longer the same today.

Modern manufacturing companies function in an environment characterized by uncertain demands, complicated global supply chain management, high-quality requirements, and the need to boost efficiency. Manufacturing companies produce a lot of operational data daily, but many of the decisions made tend to be reactive rather than proactive.

It is precisely this disconnect between the existence of information and action that makes artificial intelligence agents an important development in the future of manufacturing. While software requires instruction to carry out a task, AI agents work constantly by monitoring, analyzing, and acting on the streams of information.

Why Traditional Automation Is No Longer Enough

Automation has been a central pillar of manufacturing for decades now. However, automation systems in manufacturing operate on the basis of fixed sets of instructions. Should any particular condition arise, there would be a set of instructions for what action needs to be taken. This approach suits manufacturing well as long as conditions remain predictable.

However, machine performance, quality of raw materials, and consumer demand keep changing. Moreover, machine breakdowns happen without prior notice. Conventional automation has difficulty dealing with situations that go beyond fixed sets of instructions. An AI agent works quite differently. Instead of following sets of instructions, an AI system analyzes different conditions, identifies patterns and determines what action to take.

The Rise of AI-Powered Manufacturing

In its Smart Manufacturing Survey 2025, Deloitte noted that manufacturing companies that use smart manufacturing techniques showed 10–20% growth in production volume, 7–20% growth in labor productivity, and 10–15% improvements in capacity utilization. Another interesting finding is that 92% of manufacturing companies expect smart manufacturing technologies to drive their competitiveness in the coming three years.

These findings demonstrate how companies approach manufacturing in modern times. Companies stop focusing solely on automation and seek to implement systems that can make real-time decisions based on data collected. Such an approach requires the involvement of AI agents.

AI Visual Inspection: The First Major Operational Shift

Quality control has been one of the most evident uses of AI agents. Previously, it was done mostly manually by highly qualified inspectors. However, while being efficient, this method has certain drawbacks, including fatigue, subjective perception, and speed of work that can influence its efficiency. AI visual inspection systems can help with this problem by using industrial cameras, machine learning, and real-time data analytics. They manage to analyze thousands of products within an hour and assess each component according to predefined criteria.

They may include, but are not necessarily limited to:

• Cracks and scratches on the surface
• Deviation from the required dimension
• Assembly mistakes
• Lack of a component
• Discrepancies in color and texture

But perhaps more important than all of this is the speed with which any problem is recognized. The system analyzes components as soon as they are created and helps prevent the generation of numerous defective items. But the benefits of such systems go even further.

Each inspection provides valuable information to the manufacturing company. With time, AI visual inspection systems will allow the discovery of recurrent patterns of defect formation and the implementation of preventive measures.

AI Agents Across the Production Lifecycle

The impact of AI-based agents goes far beyond inspection purposes. They play an increasingly prominent role in carrying out manufacturing processes during various stages of production.

Intelligent Scheduling for Manufacturing

Historically, scheduling of production has been dependent on assumptions and manual plans. AI-based agents provide a more flexible way to handle scheduling. By monitoring machine availability, inventory status, priority of orders, available labor, and other limitations on production, AI agents can create schedules that reflect current realities instead of predictions.

In the case of any disruptions, like malfunctions or a shortage of materials, the schedule can be automatically changed, thus preventing delays and ensuring that deadlines will be met.

Predictive Maintenance

Maintenance failure is still one of the biggest issues when it comes to running expenses in factories. Unplanned stoppages influence the process of production, utilization of workforce, customers' requirements, and profits of companies. AI-assisted preventive maintenance robots constantly check up on the condition of equipment via vibration, temperature, electricity use, and performance. Instead of responding to a problem once it happens, companies get an opportunity to see warning signs weeks before the occurrence. It was found out that using preventive maintenance can lower machine failure time by 30-50%, while increasing its longevity by 20-40%.

Inventory and Supply Chain Optimization

Visibility is also enhanced within the supply chain using AI. The analysis of purchase patterns, production scheduling, supplier behavior, and inventory levels will enable the manufacturers to forecast shortages, eliminate surplus inventory, and enhance material availability. Thus, there will be a more robust process that can react fast to market conditions without needing working capital.

The Integration Challenge Holding Manufacturers Back

Though there are plenty of advantages in this regard, many businesses find it hard to utilize artificial intelligence. It is not because of the lack of AI tools; rather, it is due to the issue of data fragmentation.

Data used for critical operations is fragmented across systems like ERP systems, Excel sheets, machine controllers, IoT sensors, MES platforms, legacy systems, etc. With such a lack of integrated data architecture, many AI programs fail to yield any fruitful results.

Building the Foundation for AI with Dhumi

AI implementation cannot just rely on algorithms but needs a connected system. Dhumi is here to help companies close the gap created by unconnected operational technology through its low-code integration and workflows platform.

Dhumi does not aim to replace the existing technological setup within the company but rather connects ERP systems, manufacturing machinery, IoT devices, the database, and other business applications to one operational system.

The data exchange can become seamless, and the workflows can be automated while also allowing for the implementation of AI solutions without much need for custom development.

Such a solution will allow companies with legacy technological setups and a lack of technical personnel to adopt AI solutions without much difficulty.

The Future of Manufacturing Operations

Factories utilizing AI agents don’t differ physically from those that don’t employ any. The difference consists in their approach to change. Quality problems are detected before they affect consumers. Problems in maintenance are solved prior to becoming an obstacle to work. Schedule problems are sorted out before disrupting production. Decisions are made faster based on continuously incoming information rather than lagging behind reports.

This is what makes the use of AI agents valuable. Not in replacing humans. Not in creating autonomous factories. But in addressing small operational issues that lead to increased expenses. Data about the operations of the factory is already available to its management. The new competitive edge is the ability to act upon this data.

The better the capabilities of AI agents get, and the easier it gets to implement them using tools such as Dhumi, the less important the question becomes of whether intelligent operation would be a standard. The issue is who will be the first to adopt intelligent operations.