Industrial Domains

In the context of the ENFIELD project, the industrial domains represent specific areas of application where artificial intelligence (AI) technologies will be developed and deployed to address critical societal and industrial challenges. By focusing on fields like Healthcare, Energy, Manufacturing, and Space, the project aims to drive technological closely linked to key research areas – Green AI, Adaptive AI, Human-centric AI, and Trustworthy AI – which provide the scientific foundations and technological advancements necessary to deliver effective solutions.

Mapping the Industrial Domains

Industrial Domains

The Industrial Domains are structured around 4 core dimensions:

Energy
Healthcare
Manufacturing
Space

Subdomains

The Industrial Domains are organized into distinct Subdomains:

15 subdomains serve as contexts for AI deployment across defined use cases.

Use Cases

Each Subdomain will operationalize AI through relevant and context-specific Use Cases:

47 use cases were identified based on the relevance of AI-related challenges within the Pillars, their suitability for AI testing and validation, and their potential to contribute to sustainable development goals — including the integration of renewable energy sources (RES) and the provision of affordable energy.

Industrial Domains

Energy

In the Energy industrial domain, the project aims to develop innovative solutions to optimize energy consumption and distribution, support the transition to renewable sources, and reduce carbon emissions, aligning technological innovation with Green AI principles. The goal is to improve the efficiency of energy systems by enabling smarter resource management and faster responses to unexpected events, including short-term forecasting, predictive maintenance, energy storage dispatch, demand peaks, and system failures.

Leader: INESC TEC
Co-leader: ISK
Research entities: TALTECH, INESC TEC
Industry players: MAGGIOLI, PREDICT, ISK, NewCo, EDPCNET

Healthcare

The project will develop AI-based solutions to enhance the efficiency, personalization, and intelligence of healthcare systems. Contributing directly to the Healthcare industrial domain, it will enable faster and more accurate diagnostics, optimized treatment pathways, and cost reduction. It will apply multidisciplinary, human-centric AI research to ensure ethical, patient-focused outcomes, leveraging health digitalization and clinical Big Data through advanced AI for effective analysis, inference, and decision-making.

Leader: ICCS
Co-leader: KNOW
Research entities: ICCS, NRS, TU/E, TUC, BME, UPB, KNOW 
Industry players: LUSIADAS, INOVA+, MAGGIOLI

Manufacturing

The project will develop AI solutions to support the transformation of the manufacturing industrial domain, focusing on empowering human workers across design, production, and logistics. It will deliver human-centric and trustworthy AI tools that enhance human capabilities, extend working life, and ensure transparency and reliability in industrial processes. To address the challenges of domain shift—especially in process industries—the project will implement adaptive AI models, while green AI approaches will be explored to support the goal of net-zero, sustainable manufacturing.

Leader: POLIMI
Co-leader: PREDICT
Research entities: POLIMI, BME, DTI, TALTECH, SINTEF
Industry players: ENG, INOVA+, PREDICT

Space 

The initiative is set to deliver AI-driven solutions to advance the Space industrial domain, with a focus on satellite management, autonomous navigation, and space data analysis. Trustworthy and Adaptive AI will be applied to meet the sector’s stringent safety and accuracy standards, enabling applications in space security, environmental monitoring, and sustainable resource management.

Leader: ECOE
Co-leader: BAS
Research entities: ECOE, NTNU, CHALMERS, NRS, ICCS, SINTEF, VTT
Industry players: NewCo, BAS

Contribution to environmental, social and governance objectives

Ambiental
Research and innovation within the environmental pillar focus on leveraging AI to optimize energy consumption, reduce emissions, promote circular economy practices, and enable smart urban sustainability. These efforts directly contribute to advancing Sustainable Development Goals 7, 11, 12 and 13, fostering a greener and more resilient planet.

Social
The social pillar emphasizes the development of human-centric and trustworthy AI systems that promote equity, inclusion, and wellbeing. By addressing challenges such as energy poverty prediction, healthcare accessibility, and ethical AI adoption, this research supports Sustainable Development Goals 3, and 10, enhancing quality of life and social justice.

Governance
Governance-oriented AI research aims to ensure transparency, security, and ethical compliance across industries, strengthening institutional trust and innovation ecosystems. By enabling explainable AI, data privacy, and resilient infrastructures, it contributes to Sustainable Development Goals 9, 11 and 16 promoting responsible leadership and collaborative sustainable development.