Production is moving into an era where quality intelligence is as important as manufacturing capabilities. In precision-based sectors like electronic manufacturing services (EMS), a single error in assembly can cause costly production issues, logistics issues, and bad publicity. However, the majority of factories rely on inspection procedures that identify mistakes only after they occur.
As production environments become more complex, companies that invest in adaptive, data-driven technologies today will be positioned to lead the next generation of efficient, resilient, and sustainable industrial operations.
There are over 27 million industrial enterprises across the world, and India accounts for nearly 8.6 million manufacturing enterprises, making it one of the world’s largest manufacturing ecosystems. According to Analytics Insight’s AI Adoption in Manufacturing Report 2026, nearly 88% of these manufacturers are using artificial intelligence in some way, and 94% are incorporating digital transformation efforts. According to another 2025 study by McKinsey, more than 50% of these manufacturers are using AI in at least one business operation, and one of the key areas is quality control. In EMS production, AI-driven quality intelligence is essential to enhance the accuracy and resilience of the manufacturing ecosystem.
Transition to intelligent manufacturing
The move towards intelligent production is driven not only by technological advances but also by a series of global geopolitical and economic disruptions. Rising tariffs, geopolitical uncertainty, and continued disruption to supply chains are forcing companies around the world to rethink traditional industrial practices. Given the uncertainties surrounding international trade, companies are moving away from rigid, cost-driven frameworks to innovative and adaptable manufacturing systems.
The introduction of technologies such as artificial intelligence (AI), machine learning, computer vision, interconnected sensors, virtual modeling, and industrial measurement techniques have significantly contributed to this revolution. With the help of these technologies, manufacturers will be able to generate large amounts of data that will help them make decisions. Before implementing these technologies, manufacturers had to manually monitor their production systems and perform maintenance only when problems occurred.
However, in today’s complex EMS and advanced production processes, these AI systems continue to monitor production lines and predict malfunctions in the process before problems occur. This approach changed the fundamentals of manufacturing, moving from solving problems after they occurred to preventing them in the first place.
Real-time defect detection and quality optimization
One of the most important applications of artificial intelligence in manufacturing is real-time defect detection with computer vision systems. Computerized sensors and cameras, combined with machine learning algorithms, can detect process and product deviations during the production cycle itself.
Unlike traditional methods that rely on human expertise and sampling, AI learns and builds knowledge from the past. As a result, AI will become increasingly capable of detecting deviations from process benchmark standards and quality guidelines. The end result is increased yields, fewer rejects, and minimized material waste. This is especially important when working under the tight margin constraints of EMS operations.
Additionally, there is a growing trend to use artificial intelligence to make corrections and adjustments during the process. Rather than detecting defects, AI-based solutions now use feedback from production activities to optimize production processes.
Predictive maintenance and equipment reliability
Equipment stability is an important factor in product quality. Small changes in equipment condition parameters such as vibration, temperature, and calibration can cause serious quality problems if unnoticed for long periods of time.
AI-powered predictive maintenance systems can help track these small warning signs, allowing manufacturers to move from their current break-and-fix approach to a more proactive strategy for addressing equipment issues. Machines are eligible for service based on expected failure scenarios rather than time windows.
This system not only improves the efficiency of maintenance operations, but also helps create a more predictable manufacturing environment and maintain consistency in product quality.
AI collaboration: secure and scalable
As manufacturers expand their operations to multiple production locations, there is an increasing need to securely scale AI systems. The security of production information, trademark ownership, and the confidentiality of customer information continues to be a priority, especially in sectors such as aerospace, defense, and high-tech electronics.
This is accelerating interest in collaborative AI frameworks that allow manufacturers to collaboratively improve and train AI models without directly sharing sensitive production data.
Interconnected models allow manufacturers to safely scale their AI systems because data remains in each site’s specific environment and training and improvement processes can be performed collaboratively.
Future smart manufacturing
Intelligent systems that are constantly self-improving represent the next stage in manufacturing. Traditional production reacts to equipment or quality control failures, but future plants will be able to predict problems.
Instead of checking product quality through inspection, factories will become aware of product performance throughout the production process. Additionally, AI can be implemented to optimize supply chains, manage energy consumption, predict production processes, and monitor sustainable development. But more importantly, the concept of quality is changing.
For EMS manufacturers, this transition is no longer optional. As product complexity continues to increase and tolerances shrink, AI-driven quality intelligence is becoming a core requirement rather than a future upgrade.
Intelligent quality systems are now essential to scaling operations while meeting increasing demands for customization, speed, and accuracy. As production environments become more complex, companies that invest in adaptive, data-driven technologies today will be positioned to lead the next generation of efficient, resilient, and sustainable industrial operations.
Guest author Naman Shah is Managing Director and CEO of LeSol Group., A vertically integrated electronics manufacturing company focused on scalable, quality-oriented production solutions for Indian and global markets. LeSol Group operates a well-established OEM business with two well-known brands: ReneSola and Usha Shriram.
