+34 919 930 084 // +34 607 345 312 contact@neuraptic.ai


How can Artificial Intelligence help Industrial Sector grow?

Artificial Intelligence is here to revolutionise how industry works, offering a whole new way of looking at the relationship between men and machines. This progress in industry, also known as the Industry 4.0 concept, is based on smart companies where workers and technology systems interact to make businesses more competitive.

We might say that Artificial Intelligence intensifies business intelligence, given that it represents a significant, positive evolution for the global economy. It should come as no surprise that according to several studies carried out recently, a large percentage of Spanish companies are already exploring Artificial Intelligence.

Currently, scientific disciplines of AI such as Machine Learning, a speciality of the ENAIA platform, are getting very positive results in companies’ ROI, particularly in the industrial sector.

Machine Learning can bring important benefits for industry such as increasing and optimising production, making production processes more flexible, raising product quality and saving manufacturing costs and time, among other aspects; by introducing systems such as Automatic Visual Inspection, Natural Language Processing, Predictive Maintenance, Prediction of Time Series and Data Mining.



There are countless use cases for Artificial Intelligence in Industry 4.0, mainly in the areas of production, quality and logistics. Let’s look at a few examples:


Predictive Maintenance uses advanced analysis to determine the condition of an asset or set of assets.

The aim is to predict when maintenance should be performed on them. Predictive maintenance generally combines several sensor readings and carries out predictive analysis on thousands of registered data.

 The most advanced technique to predict the remaining useful life of an asset is Machine Learning. For example, breakdowns can be avoided by anticipating when worn parts need to be replaced by constantly checking their condition. This is something that particularly benefits process industries, where breakdowns can lead to major sales losses.


There are several ways of running quality controls with Artificial Intelligence but the most frequently used is Automatic Visual Inspection. This technique uses an autonomous camera to view the items in question (parts or manufactured products, raw materials, etc.) in search of flaws, deviations or quality defects.

The main benefit in this Artificial Intelligence use case applied to industry is the obvious cost saving, mainly benefiting large manufacturing plants where a small drop in the volume of defective products can save a lot of money. Thanks to the design of these solutions, the technology never stops learning (continual learning), so its performance is continuously improving, and it gets better the more it is used.



Supply chains benefit greatly from the implementation of Artificial Intelligence in industrial sector companies. Specifically, it is essential to be able to predict the demand when managing supply chains.

Predictive analysis can be used to optimise a wide variety of tasks related to inventory management such as improving anticipation of changes in demand, adjusting production programs accordingly.

These tasks use Machine Learning techniques such as predicting time series to identify demand patterns according to knowledge extracted from data stored in customers’ storage systems and their ERPs.


The most usual way of optimising processes is using autonomous machines to replicate monotonous tasks (with no added value) in the manufacturing processes. Before incorporating the machines or autonomous robots into these processes, they are trained with Artificial Intelligence, mainly Machine Learning, until they reach the accuracy required to work properly.  

One example is giving the machines and production units the capability to “self-optimise” by adjusting their parameters in real time using analysis and continual learning, working from their generated data (historical and current).

The metalwork sector uses AI to allow furnaces to adjust autonomously, identifying the lowest working temperature, guaranteeing process quality and in turn consuming the minimum energy to save costs. In all industrial sector environments, manufacturers can use Artificial Intelligence to reduce costs, increase speed and therefore improve productivity.

What’s more, thanks to AI, it is possible to tackle production complexities such as manufacturing personalised products for a specific customer.


Automated design of components or products is the most usual R+D application developed with Artificial Intelligence.

 AI solutions independently develop a variety of designs, differentiated according to a set of parameters or pre-defined restrictions. Algorithms explore all the possible design solutions based on defined objectives or limitations. After running these tests, the optimum design is selected.



Industrial companies store a large quantity of data in multiple systems. Accessing, analysing, classifying and managing them efficiently is no easy task. Consequently, the industrial sector is beginning to use automated data management solutions such as integration to be able to manage this asset efficiently in real time.


Nowadays, cybersecurity and privacy are two very delicate aspects for companies in all sectors and, of course, the same goes for companies in the industrial sector.

It is vitally important to detect cyber-threats. Solutions developed using AI are used for this and other needs such as controlling the infrastructure and analysing network traffic, limited malware environments etc.


Artificial Intelligence is also applied as a surveillance measure to detect possible physical threats in real time, either in facilities where security risks might arise or physical safety threats for workers.

We know that many workplace accidents happen in the industrial sector and so it is a top priority to make sure that protective equipment (PPE) is used correctly.

Noncompliance with these measures can cause very serious legal problems for industrial companies; and far more seriously, it can represent a risk for workers’ lives. In this respect, to give an example of an AI use case, by combining advanced analytics and automatic learning applied to processing images, it is possible to detect whether employees are using the protective equipment correctly, even in real time.



smart assistants

We can find different types of integrated smart assistants in the industrial sector, such as voice assistants. Thanks to them, workers can access information in real time without having to manage other controls or print documents or reports.



This is a processing technique for a massive volume of data, mainly radar, satellite or drone images, to detect optimum locations to extract natural resources. These Artificial Intelligence solutions are particularly interesting for the mining industry or for the oil and gas sector industry, essentially in areas that are hard to access.


As you could see in this post, applying Artificial Intelligence in industrial sector companies is the answer for a wide range of needs which can turn traditional factories into smart industries. To do so, multi-task Artificial Intelligence platforms have been developed by AI expert companies like the ENAIA platform. It is a technological ally that can extract all the value from your data and apply it in different types of tasks or use cases.


Resistance to change, lack of knowledge of new technologies and the digital divide are some of the main enemies of companies in the industrial sector on their path to digital transformation. ENAIA is determined that one of its major challenges will be to transmit to all companies in the sector the great potential and revolution represented by implanting Artificial Intelligence solutions in its business strategies. ENAIA’s specialisation and professionalism make it the perfect sidekick for industrial sector companies as they strive to incorporate new Artificial Intelligence technologies.