- European AI factories combine supercomputing, big data and talent to develop advanced models aligned with the AI Act.
- Spain leads the way with AI Factories in Barcelona and Galicia, focused on generative AI, health and biotechnology with strong public-private investment.
- On the factory floor, AI drives predictive maintenance, digital twins, quality control, and process and supply chain optimization.
- Success depends on reliable data, AI-ready IT architectures, specialized talent, and ethical and secure use of models.
La manufacturing for artificial intelligence It has become one of the major drivers of industrial and technological transformation in Europe. From the connected plants From supercomputing centers to other industries, AI is changing how we design products, how we organize factories, and how we make real-time decisions.
At the same time, the European Union is promoting a network of AI factories and gigafactories designed to train advanced models and bring supercomputing closer to businesses, universities, and government agencies. All of this intersects with the realities of the factory floor: predictive maintenance, digital twins, automated quality control, and cobots who work side by side with the operators.
What exactly is an Artificial Intelligence factory?
An Artificial Intelligence factory (AI Factory) It is much more than a data lab: it is an infrastructure where [various elements] converge. supercomputinglarge volumes of data and specialized equipment for design, test and deploy AI solutions high-impact. They act as innovation hubs where generative models, artificial vision systems, applications for industry 4.0, or advanced analysis tools are developed.
In this type of facility, researchers, startups, SMEs and public administrations They work together to convert industrial, healthcare, or environmental data into AI models that can be used at scale. Strategic areas such as the health and biotechnology, the climate and sustainability, the industry and energy and precision farming.
The European Commission has framed these factories within the infrastructure of the European High-Performance Computing Joint Undertaking (EuroHPC)This means they leverage supercomputers optimized for AI, capable of training generative models with huge volumes of parameters, while ensuring safety, traceability and alignment with European values.
This approach allows AI to extend beyond a few tech giants and reach the entire business sector, making it easier for medium-sized or even small businesses access computing resources that they could not finance or maintain on their own.
The EU's vision is for these factories to be open environments where the trust, ethics and sustainability are integrated from the design of the models to their operational deployment, especially under the regulatory umbrella of AI Act.
The European project of AI Factories and Gigafactories
The deployment of AI factories is part of two major political blocs: the AI Innovation Package and the AI Continent Action PlanBoth programs aim to consolidate a competitive digital ecosystem In Europe, with AI and supercomputing as pillars for technological sovereignty.
Between 2025 and 2026, the European Commission has set itself the goal of having them active at least 15 AI factories and several associated antennas, connected to supercomputers specifically optimized for AI workloads. In parallel, the deployment of at least nine new AI-focused supercomputersThis will more than triple EuroHPC's current computing capacity for artificial intelligence.
These factories will act as platforms for accessing advanced models for startups, SMEs, universities, technology centers, and public administrations. They will offer services ranging from generative model training to ethical and responsible AI consulting, going through testing and validation environments.
Furthermore, they are integrated with other EU instruments, such as Testing and Experimentation Facilities (infrastructures for testing AI solutions in near-real-world conditions), the network of Digital Innovation Hubs and the new fund InvestAI Facility, endowed with some 20.000 millones de euros to finance up to five AI Gigafactories with massive capabilities.
The AI Gigafactories These will be large-scale facilities, designed to train next-generation models with trillions of parameters. To achieve this, they will combine more than 100.000 advanced processors AI-specific features, robust energy infrastructures, ultra-low latency networks, secure hardware supply chains, and high levels of automation.
All this public-private effort aims to Strengthening European technological sovereigntyto reduce dependence on non-European infrastructure and ensure that the AI used on the continent is reliable, traceable and respectful with fundamental rights.
Spain at the forefront: Barcelona and Galicia as AI hubs
Within this European map, Spain stands out as one of the few countries Spain boasts two AI factories approved by the Commission: one general-purpose facility in Barcelona and another specializing in health and biotechnology in Galicia. Along with Germany and Poland, Spain has one of the highest numbers of recognized centers.
The first of these factories is located in the Barcelona Supercomputing Center – Centro Nacional de Supercomputación (BSC-CNS), one of Europe's major supercomputing hubs. This facility was selected as one of the seven pioneering AI Factories in 2024, benefiting from its state-of-the-art supercomputer.
The main objective of the BSC's AI Factory is democratize access to supercomputingallowing not only large scientific centers, but also SMEs, startups and emerging research groups, can train advanced AI models, especially in the field of generative AI and big data analytics.
The second factory approved in Spain will be deployed in the Galician Supercomputing Center (CESGA), under the initiative 1HealthAIIn this case, the priority is the development of AI models applied to health, biotechnology, and life sciences, addressing areas such as personalized medicine, healthy aging, blue biotechnology or bioenergy.
The 1HealthAI project mobilizes a total investment of 82 millones de euros, with a very distributed co-financing: some 41 million come from EuroHPC, 24 million from the Government of Spain through the Recovery Plan and 17 million of the Xunta de GaliciaIn addition, the following participate: three Galician public universities, the CIGUS network of research centers, the European hub DATAlife and entities such as the technology center gradient.
Both projects are part of the National Artificial Intelligence Strategy and in the Agenda Spain Digital 2026acting as levers of Recovery, Transformation and Resilience Plan to promote a humanistic digitalization, based on public-private collaboration and an innovative business fabric.
Smart manufacturing: how AI is being applied in the industry
Beyond the large infrastructures, the AI is transforming manufacturing at the factory floor level, in what is known as smart factories or smart manufacturing within the umbrella of the Industry 4.0The core of this change is the ability to capture real-time data from machines, production lines, warehouses, and logistics, so that algorithms can continuously optimize processes.
Artificial intelligence analyzes massive flows of data coming from sensors, PLCs and vision systemsERPs and MES solutions. Based on this data, it detects patterns, anticipates defects, recommends parameter adjustments, reassigns resources, or even autonomously modifies production to adapt to variations in demand or supply incidents.
This combination of automation, connectivity, and advanced analytics It allows for much more flexible plants, with fewer unplanned shutdowns, less waste, and shorter production cycles. In practice, AI systems end up being an intelligence layer that oversees the entire operation.
Generative AI is also beginning to gain traction in industrial settings. It is used for advanced search for technical information, automatic document summaries, after-sales service support, customer service, or rapid generation of software prototypes or operator interfaces adapted to specific processes.
In parallel, AI is driving a new wave of Human-robot collaboration through so-called cobots. These collaborative robots, equipped with vision and safety algorithms, can share workspace with people, take over repetitive or ergonomically demanding tasks, and allow operators to focus on higher value-added work.
Key use cases: from predictive maintenance to digital twin
One of the most widespread examples of AI in manufacturing is the Predictive MaintenanceIn this approach, data on vibration, temperature, power consumption, or pressure from machines are analyzed using machine learning models that They predict the next likely failure of a component, equipment, or system.
When the algorithms detect anomalous patterns, they generate early alerts that allow maintenance teams to plan interventions at the optimal timeavoiding unexpected shutdowns and reducing emergency repairs, which are usually more expensive and complex to manage.
Many companies have moved from maintenance plans based on fixed schedules to dynamic models, in which inspections are carried out based on the actual state of the machineIn some cases, real-time anomaly detection models deployed at the edge are combined with historical analytics in the cloud to improve the accuracy of predictions.
On another level, AI is used to build digital twins of lines, machines, or even entire factories. These twins are virtual replicas that receive continuous data from the physical world and allow for the simulation of the impact of parameter changes, new recipes, layout configurations, or maintenance strategies, reducing risks and testing times in the real environment.
In the field of quality control, computer vision with AI has enabled automated visual inspections on parts, food, packaging, or welds, among other things. The systems capture images or videos and compare them with learned patterns to identify defects, assembly errors, or anomalies that are difficult to detect with the naked eye.
Process optimization, recommendations, and personalization
Another powerful application of AI in manufacturing is the optimization of complex processeswhere many simultaneous variables are involved: temperatures, times, pressures, humidity, material compositions, line speeds, etc. Models can identify non-obvious relationships between parameters and quality or productivity results.
In long processes, such as those found in certain food or chemical industries, algorithms have been deployed that, based on historical data and real-time environmental conditions, They propose specific adjustments on process variables to reduce rejections and homogenize quality.
This type of "smart recommendations" acts as a co-pilot for operators, suggesting, for example, slightly modifying a temperature, adjusting the flow rate of an ingredient, or varying the baking time if ambient humidity conditions change significantly.
AI is also used for analyze the performance of machining cells or assembly lines, identifying bottlenecks and points of inefficiency. By combining high-frequency data at the edge with cloud analytics, some companies have achieved double-digit improvements in cycle times, resulting in increased production with the same machine base.
AI is also being used in the product design and customizationBy analyzing market trends, sales data, and customer preferences, the models help define new product variants, virtually test alternative designs, and simulate their behavior before building physical prototypes.
Supply chain management and connected factory
These models can indicate early risks of disruption For critical suppliers, recommend inventory relocations, optimize transport routes, or automatically adjust safety levels to avoid stockouts without excessively inflating tied-up capital.
In parallel, AI models are used to evaluate the reliability and quality of suppliersby combining data on deliveries, failure rates, response times, and contractual variables. With this information, companies can make more informed decisions about who to work with or what alternatives to activate in case of problems.
In the warehouse, AI powers systems that optimize locationsPicking routes, space utilization, and task sequencing are all used to reduce travel and order preparation times. This results in a more agile, less expensive, and, in many cases, more sustainable logistics system.
La smart manufacturing It is completed with an integration layer between ERP, MES and plant-specific systems, on which advanced analytical capabilities are deployed and, increasingly, conversational assistants that allow users to obtain indicators and recommendations by speaking in natural language instead of navigating through multiple screens.
Benefits for productivity, costs and sustainability
The applications described translate into a range of tangible benefits for manufacturing companies. One of the clearest is the improvement of productivityBy being able to produce more with the same physical resources, thanks to fewer stops, optimized production cycles and a significant reduction in rework and waste.
La Product Quality It is also reinforced: automated control with machine vision and process data analytics makes it possible to detect causes of defects before they become massive problems, adjusting the process on the fly and reducing both bad parts and false rejections.
In terms of costs, AI acts as a multi-level savings leverIt extends the useful life of equipment through predictive maintenance, reduces energy consumption by adjusting operating parameters, optimizes inventory levels, and improves the efficiency of transportation and warehouse use.
Another important effect is on the decision makingThose responsible for production, maintenance, quality, or logistics can base their decisions on objective data and analysis, rather than solely on intuition or prior experience, which speeds up the reaction to changes and reduces the likelihood of strategic errors.
Finally, AI contributes to the environmental sustainability from manufacturing. Less waste, more efficient processes, optimized logistics routes and a more rational use of energy and water translate into lower emissions and a smaller ecological footprint, which also improves the company's image and helps to comply with increasingly strict regulations.
How to build an AI model applied to manufacturing
Behind all these use cases is a structured process of AI model development. The first step is to define the problem precisely The objective to be addressed: predicting breakdowns, classifying products, optimizing energy consumption, segmenting industrial customers, etc. The clearer the objective, the easier it will be to choose appropriate algorithms and metrics.
Next, the following steps are taken: collection of relevant dataIn an industrial setting, this includes sensor measurements, production histories, maintenance records, quality data, supplier information, or even open data from public bodies or commercial providers.
Once collected, the data undergoes a phase of cleaning and preparationIt is common to encounter missing values, measurement errors, duplicates, or inconsistent date and unit formats. This stage also includes the so-called feature engineeringThat is, the creation of new derived variables (e.g., ratios, temperature differences, seasonality indicators) that better capture the behavior of the process.
In parallel, a exploratory analysis to become familiar with the dataset, identify trends, relationships, and potential biases. The data is then divided into training, validation, and test sets, often applying techniques of cross validation to make better use of available data and ensure a robust assessment.
The next step is choose the type of modelFor regression problems, linear models, trees, or neural networks can be used; for classification, algorithms such as random forests, SVM, or deep networks; for labelless grouping, clustering techniques; for data with a strong temporal component, time series models or recurrent architectures; and for sequential decisions, reinforcement learning approaches.
During training, the model parameters are adjusted to minimize the error between your predictions and the actual dataIn the case of neural networks, this involves defining architectures, activation functions, number of layers, learning rate, batch size, number of epochs, etc., until the balance between accuracy, training time and available computational resources is found.
Once trained, the model is evaluated with the test set, focusing not only on accuracy, but also on aspects such as confidence in the predictions, response time, memory usage and the ability to adapt to new data without needing to be completely retrained.
If the performance is satisfactory, the model is deploys in a productive environmentWhether integrated into a corporate application, embedded in a plant control system, exposed as an API, or incorporated into a conversational assistant, continuous monitoring and model maintenance become critical, as data changes and phenomena such as [missing information] can occur. drift, which require retraining or adjusting the system.
Challenges: talent, IT architecture, and responsible use
Although the potential of AI in manufacturing is enormous, companies encounter several challenges. recurring challenges. One of them is the shortage of specialized profiles capable of designing, deploying and operating AI solutions integrated with industrial reality, from data scientists to automation engineers with knowledge of algorithms.
To mitigate this deficiency, many organizations resort to internal training programs, to the contracting of services from technology partners or to enterprise software solutions that already integrate AI capabilities into their ERP, SCM or MES modules, reducing the need to build models from scratch.
Another common obstacle is the complexity of IT architecture In large manufacturers, especially those that have grown through mergers and acquisitions, it's common to find a patchwork of legacy systems, disconnected databases, and plant-specific applications, complicating the creation of a unified data layer on which to efficiently train and run AI models.
The solution usually involves defining a clean core strategy and progressive modernization, relying on providers that offer AI-ready platforms, with standardized connectors and real-time integration capabilities with existing systems.
Finally, the question of responsible and safe use of AI This is especially sensitive in industrial environments, where critical business data, intellectual property, and, in many cases, customer and supplier information are handled. It is essential that manufacturers choose solutions and partners that respect principles of ethics, transparency, and privacy.
When selecting AI providers, it's important to consider that Do not use customer data to train general-purpose third-party modelsthat apply robust security controls, document models and allow for a degree of explainability, and comply with relevant regulations, from the AI Act to data protection regulations.
Taken together, manufacturing for artificial intelligence is shaping a new industrial model in which data, supercomputing and talent These technologies combine to create more resilient, efficient, and sustainable plants. As European AI factories mature and use cases become more widespread on the factory floor, organizations that can responsibly integrate these technologies into their strategy will be the ones that make the difference in competitiveness, innovation, and adaptability.
