Artificial intelligence in manufacturing: a complete technology overview

Artificial intelligence is becoming an integral part of manufacturing as automotive, electronics, metals, and other companies use it to stay competitive and resilient in an increasingly complex global market. Rising consumer demands, supply chain disruptions, and workforce shortages are putting significant pressure on factories. In light of this, AI is being actively introduced to help manufacturers address various operational challenges, such as factory downtime, material waste, and market unpredictability.

Last year, the global AI in manufacturing market was valued at $5.32 billion and is expected to grow nine times within five years at a compound annual growth rate of 46.5%. As interest in AI rises, manufacturers are increasingly collaborating with AI consulting providers and seeking guidance on selecting or building the right tools, preparing data for AI models, and integrating AI capabilities into existing processes and software.

Key use cases of AI in the manufacturing industry

There are multiple ways manufacturers can implement AI across their operations, from production planning to compliance monitoring. Below are the most popular AI use cases in the manufacturing industry today.

Predictive maintenance

Manufacturers use AI-driven predictive analytics to detect early signs of equipment failure and plan service activities before breakdowns occur. AI systems typically perform the following tasks:

• Processing telemetry from programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA) systems, and vibration, temperature, current, and pressure sensors to detect anomalies in equipment operation
• Estimating the remaining useful life of components, generating failure time predictions, and generating maintenance recommendations
• Assessing production risks and impact, as well as resources and spare parts availability, to schedule maintenance activities

Quality inspection & defect detection

AI-powered quality control systems automate the inspection on production lines by using advanced pattern recognition and data analysis techniques, which speeds up defect detection and eliminates the need for human oversight. They commonly carry out the following functions:

• Analyzing images and 3D scans with image classification and segmentation techniques to detect and categorize defects on products
• Identifying root causes of defects by analyzing process parameters and defect trends
• Detecting drift, calibration issues, and measurement inconsistencies in factory equipment

Human–robot collaboration on the factory floor

AI integrated into collaborative robots (cobots) makes them safer to work alongside human operators and enhances the robot’s operation in the following ways:

• Processing camera and force sensor data to detect human presence or obstacles and adjust the robot’s speed, movement range, or applied force in real time
• Recognizing product parts and their exact positions for precise grasping, placement, or assembly
• Identifying required tools, materials, and fixtures for the next production step and guiding automatic tool, material, or fixture adjustments
• Analyzing production data to recommend optimal task sequences and programs, enabling the cobot to switch between different products or manufacturing operations

Production simulation & planning

AI powers digital twins, virtual replicas of production lines, equipment, and workflows, to help manufacturers design, test, and optimize production processes without disrupting existing operations. AI tools are applied to handle the following activities:

• Generating and updating digital models from sensor data, 2D and 3D designs, and historical performance records
• Simulating production flows to help workers optimize factory floor layouts, task sequences on each line, batch sizes for different product runs, and shift schedules for operators based on equipment capacity, workforce availability, and material supply
• Generating working schedules for machines, workstations, and teams
• Running “what-if” simulations, comparing potential scenarios, and creating optimal production plans

Supply chain management & inventory optimization

AI-driven supply chain and inventory tools help manufacturers align production volumes and inventory levels with the real-time market demand. These systems typically handle the following tasks:

• Generating demand forecasts for each product by analyzing historical sales, seasonal patterns, and market trends
• Calculating optimal reorder points and safety stock levels to avoid shortages or overstock
• Analyzing supplier lead times and quality metrics to detect potential delays or reliability issues
• Recommending warehouse storage arrangements and pick paths that reduce travel time and ensure efficient replenishment

Energy & resource efficiency management

AI-powered analytics platforms help plant owners identify opportunities for reducing energy and material waste. These systems are applied to the following tasks:

• Analyzing consumption data from individual machines and lines to quickly identify equipment with above-average energy use
• Adjusting parameters such as temperature, pressure, and motor speed for energy-intensive equipment to ensure the required output while lowering energy consumption
• Detecting leaks, overloads, and abnormal resource consumption patterns across utilities and production systems, sending automatic alerts to production teams, and recommending corrective actions
• Identifying load peaks and suggesting schedule adjustments to shift energy use to lower-tariff periods or stay within grid capacity limits

Safety & compliance monitoring

AI-based monitoring systems track workplace safety conditions and compliance with occupational health regulations, helping manufacturers prevent incidents, protect workers, and maintain readiness for safety audits. Typical AI use cases include:

• Applying object and facial recognition to verify personal protective equipment (PPE) use and control access to factory areas, devices, and software
• Processing data from wearables and workstation monitoring systems to detect ergonomic risks and early signs of strain or fatigue in workers
• Scanning environmental, health, and safety (EHS) logs, incident reports, and near-miss records to identify recurring causes and suggest preventive measures
• Extracting updated regulatory compliance requirements, mapping them to the company’s process documentation, and flagging gaps that could affect audit readiness

Real-world examples of AI in manufacturing

Global manufacturers are successfully using AI in daily operations for various purposes, from optimizing production planning to automating inspections. The examples below show measurable improvements achieved by prominent artificial intelligence adopters from the manufacturing industry.

BMW

Since 2019, the BMW Group has widely implemented AI in their plants to streamline production planning and manage complex assembly operations. To achieve this, they applied the Virtual Factory, a digital twin built with NVIDIA Omniverse, which simulates real-time factory layouts, robotics, and logistics, allowing teams to test and optimize production processes before implementing them on the shop floor. Aiming to scale the solution to more than 30 production sites, the company expects to cut planning costs by up to 30% and reduce collision-check time with its help.

As part of the BMW iFACTORY strategy, the company has also developed in-house cloud-based AI systems, Car2X and AIQX. Car2X allows vehicles on the production line to analyze their own production status, send real-time status updates and error alerts to equipment and staff, and document relevant messages. AIQX, short for Artificial Intelligence Quality Next, uses camera systems, sensors, and AI algorithms to automate quality checks, detect anomalies, and provide immediate feedback to assembly line staff via smart devices.

LG

LG uses various AI tools in the production process to detect issues faster, automate data analysis, and make factories more efficient. At one of their factories, the proprietary AI Production System analyzes all production data non-stop, inspecting the production processes and detecting issues and their potential causes. The system also sends alerts to relevant departments about production failures and can halt equipment to prevent production defects.

LG also provides smart factory solutions to clients’ factories and their own facilities. These solutions combine digital twins, big data, and vision AI and generative AI capabilities to improve product quality, industrial safety, and equipment performance.

Emirates Global Aluminium

An aluminium conglomerate, EGA uses manufacturing AI solutions to improve production efficiency, product quality, and workplace safety. On the shop floor, computer vision systems monitor equipment and processes, sending safety-related alerts 92% faster than human operators, and ensure compliance with procedures. EGA has also deployed computer vision-powered AI solutions to inspect carbon anodes for defects and AI models to automate routine procurement tasks and help optimize logistics to avoid delivery delays.

Drawing on their experience with AI projects, EGA also partnered with the UAE national AI office to advance their Industry 4.0 initiatives through joint research, workforce training, and collaborations with universities.

Challenges of AI adoption in manufacturing & their solutions

To ensure that AI initiatives deliver the expected value, manufacturers need to not only choose the right software and hardware but also properly address various technical, financial, and organizational hurdles.

Insufficient data quality & accessibility

The quality of AI solutions’ outputs depends directly on the quality and availability of training datasets and the data that the system receives once deployed. If sensors are not well-calibrated to collect accurate measurements or if data from production logs, quality reports, and maintenance records is incomplete, inconsistent, or spread across isolated systems, there is a risk of reduced accuracy in the insights the AI tool provides.

Solution:

Start with a comprehensive data audit to identify gaps and inconsistencies in the collected data, as well as sensor calibration needs across production equipment and measurement devices. If needed, calibrate them according to internal quality requirements and schedule routine calibration checks to ensure the devices deliver accurate readings. Alongside these efforts, consider implementing a centralized data management system to automatically consolidate data from different systems. Besides, apply standardized naming and formatting rules to the ingested data through ETL processes and assign data stewards responsible for ongoing data validation and cleaning.

Lack of internal AI expertise

Manufacturing AI adoption can stall due to the lack of personnel with experience in AI algorithms, data science, or industrial analytics, which complicates the effective development, implementation, and maintenance of AI-driven systems.

Solution:

Invest in targeted training programs for engineers, analysts, and operators, focusing on AI applications you’ve selected and each role’s responsibilities. While building in-house AI expertise, partner with software development agencies, technical institutes, or AI software vendors to bring in domain experts for AI system implementation and maintenance. Additionally, organize internal knowledge-sharing sessions where early AI adopters and project leads share lessons learned from using AI tools and provide practical guidance to their colleagues.

High implementation & integration costs

Implementing AI systems requires significant capital for the technical infrastructure, software licenses, and staff upskilling. Proper implementation and integration of AI with numerous plant equipment and legacy software, including machinery, ERP systems, and shop floor control systems, can further raise AI project costs.

Solution:

Implement AI solutions incrementally, starting with a limited set of processes and gradually expanding once teams have mastered the tools and workflows. As custom AI solution development can be quite expensive, prioritize cost-effective off-the-shelf manufacturing platforms with embedded AI features. To avoid the high costs of modifying existing equipment or legacy systems to support AI, choose solutions that connect directly to your production, quality, or maintenance systems through APIs or pre-built connectors, which minimizes the need for costly custom integration.

Resistance to change

Some employees consider AI systems disruptive to established workflows or a threat to their job security, which slows down AI adoption and prevents new solutions from being fully utilized.

Solution:

Educate employees early on AI implementation and usage, explaining how it can make daily work easier or safer. When rolling out AI, start with use cases that can deliver visible gains within a short time, such as faster problem detection or fewer product design errors. Additionally, form small cross-functional pilot groups of operators, engineers, and supervisors to test new workflows, provide feedback, and fine-tune the AI solution to meet the exact needs of your team, eliminating adoption friction. When the AI tool is up and running, share the improvements it brought to increase employee trust and confidence.

Cybersecurity & IP protection risks

Manufacturing AI systems process and store sensitive information, such as proprietary product designs, production schedules, and supplier details. If compromised, this data can be held for ransom or leaked publicly, leading to financial losses, reputational damage, and potential legal action.

Solution:

Begin by segmenting plant networks so production, office, and AI systems run in separate, secured subnetworks, reducing the risk that a breach in one operational area can affect others. Also, restrict access to AI tools and data they handle based on employees’ job roles and use strong authentication methods such as multi-factor verification. At the same time, monitor AI system logs and network activity to detect suspicious actions early.

When implementing out-of-the-box AI software, make sure its data encryption, data storage, and IP protection measures align with recognized frameworks such as ISO 27001. Finally, conduct regular security audits and penetration tests of both AI systems and the broader connected infrastructure to identify and fix vulnerabilities before attackers can exploit them.

To conclude

Along with other industries like retail, insurance, and agriculture, manufacturers are adopting AI at a fast pace, using it for predictive maintenance, visual inspection, and workplace automation. This popularity can be explained by the transformational effect of the use of AI, as the technology helps companies lower production costs, shorten product planning cycles, and improve product quality while preventing issues such as production disruptions, labor shortages, and supply chain delays.

If you’re looking to harness the power of AI in manufacturing, our team at Iflexion can help you. We provide AI consulting and development services, delivering tailored AI solutions for automating production processes, dedicated AI tools for analytics and business process optimization, and chatbots that streamline customer interactions.