The industrial landscape is undergoing a transformative shift towards Industry 5.0, a paradigm characterized by the convergence of sustainability, digital autonomy, and human-centric design. This article focuses on the adoption, enhancement, and implementation of AI-driven hardware, tools, methodologies, and semiconductor technologies in this progression. We present here a comprehensive strategy from the AIMS5.0 project with the objective of connecting academic developments with practical industrial use, fostering a harmonious relationship between humans and machines to improve efficiency, spur innovation, and enhance adaptability. Hence we show here our global vision, and examples of how the creation of AI-based industrial solutions is supported by novel AI-tool chains, advancements in hardware, and tools supporting human aspects.

This paper introduces a framework for implementing Autonomous Elements in Controlled Environment Agriculture (CEA) systems, leveraging a Service-Oriented Architecture (SOA) and aligned with Industry 4.0 principles. By integrating artificial intelligence and the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) control loop, the framework optimizes environmental conditions and resource management in CEA facilities. The system includes a set of Autonomous Elements—Humidity Management, Temperature Management, Nutrient Solution Control, Air Quality Management, Light Management, and Visual Plant Mass Estimation—to address critical aspects of crop cultivation. These elements are designed using SOA, decomposing system functionality into modular, reusable services, ensuring seamless communication and interoperability across components. By incorporating Industry 4.0 technologies such as IoT and edge computing, this framework enables continuous data-driven decision-making and autonomous operation to meet the demands of sustainable, high-efficiency agriculture.

The rapid evolution of industrial artificial intelligence (AI) has given rise to a diverse landscape of developers and users across various sectors. Understanding their personas—including their backgrounds, expertise, and work environments—is crucial for fostering innovation and ensuring compliance with AI regulations. This study presents insights from a persona-based questionnaire aimed at mapping the industrial AI ecosystem and informing the development of a user-centred self-assessment compliance tool. Through a structured survey of 61 participants from European AI research projects, the study identifies key attributes of AI professionals, their industry affiliations, and their approaches to AI adoption and compliance. Findings reveal gaps in awareness and application of AI guidelines, with half of the respondents uncertain whether their organizations follow formal AI standards. Moreover, AI familiarity influences risk perception, with experienced users more likely to identify algorithmic and resource-related challenges. The study integrates the Quantitative Effect and Technology Acceptance Model (QETAM) to assess chatbot acceptance for AI compliance support, ensuring the proposed tool aligns with user needs.

As artificial intelligence (AI) regulations evolve and the regulatory landscape develops and becomes be more complex, ensuring compliance with ethical guidelines and legal frameworks remains a challenge for AI developers. This paper introduces an AI -driven self-assessment chatbot designed to assist users in navigating the European Union AI Act and related standards. Leveraging a Retrieval-Augmented Generation (RAG) frame-work, the chatbot enables real-time, context-aware compliance verification by retrieving relevant regulatory texts and providing tailored guidance. By integrating both public and proprietary standards, it streamlines regulatory adherence, reduces complex-ity, and fosters responsible AI development. The paper explores the chatbot's architecture, comparing naive and graph-based RAG models, and discusses its potential impact on AI governance.

Incorporating various AI-controlled devices coupled with a human-centric focus in the industrial process has revolutionized the industry, leading the shift from Industry 4.0 to Industry 5.0. Devices like sensors and actuators lack essential computational resources. Thus, they rely on microcontrollers to be networked to send and receive data. The proliferation of a broad palette of inexpensive, resource-limited microcontrollers has motivated industries to integrate them into their industrial processes or to build industrial prototypes. However, installing and networking those microcontrollers in factory areas is tedious and has many critical requirements at various security, technical, scalability, flexibility, and data flow levels. It also poses additional challenges in terms of writing software for the microcontrollers, involving a lot of manual work, hardcoding, and complex configurations to allow for proper and secure communication with subscribed data consumers. This paper introduces a novel generic framework and software components addressing the mentioned challenges. This generic framework enables configuring and networking of microcontrollers and IIoT devices in the deployment phase at the field and edge levels, achieving more security, flexibility, and scalability.

To encourage the uptake of AI in industrial use cases, tools are required to support the engineering process throughout the life cycle of the AI application that is central to the use cases. Providing guidance on using AI in industrial setups is vital for creating trustworthy, reliable, and ethically compliant AI-based solutions. The related standardization landscape and available guideline repositories are large, scattered over the web, change rapidly, and are hard to keep up with. This limits the access and ease of use of these standards and guidelines. To address these limitations, we propose developing a self-assessment tool, empowered through AI algorithms and models such as Large Language Models, to improve the process of accessing and benefiting from those standards and guidelines. This self-assessment tool will support various user groups while engineering their applications by identifying the most applicable guidelines according to the individual attributes of the specific user group. We argue that modeling specific attributes and mapping appropriate controls for self-assessments could be achieved by applying AI-based technologies. This paper outlines our ongoing efforts concerning the suggested supporting tools, offering a human-centric methodology. Additionally, we present initial results demonstrating how the needs of a particular user group can be accurately modeled. The results of this study will be used for applications that are deploying AI in an industrial setting with the objective of enabling the two most important goals of Industry 5.0, which are the well-being of workers at the center of the production process, and sustainable and resilient industries.