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Manufacturing small and medium-sized enterprises (SMEs) have already noticed the tangible benefits offered by artificial intelligence (AI). Several approaches have been proposed with a view to support them in the processes entailed in this innovation path. These include multisided platforms created to enable the connection between SMEs and AI developers, making it easier for them to network each other. While such platforms are complex, they facilitate simultaneous interaction with several stakeholders and reaching out to new potential users (both SMEs and AI developers), through a collaboration with supporting ecosystems such as digital innovation hubs (DIHs).

Mixed methods were used. The literature review was performed to identify the existing approaches within and outside the manufacturing domain. Computer-assisted telephonic (in-depth) interviewing , was conducted to include perspectives of AI platform stakeholders and collect primary data from various European countries.

Several challenges and barriers for AI platform stakeholders were identified alongside the corresponding best practices and guidelines on how to address them.

An effective approach was proposed to provide support to the industrial platform managers in this field, by developing guidelines and best practices on how a platform should build its services to support the ecosystem.

In this era of Industry 4.0, characterized by disruptive technologies, there is a need to identify and understand the role of the quality function in the excellence journey. Quality 4.0 refers to the digitalization of quality work in the context of Industry 4.0. As Quality 4.0 is a new concept, empirical research on the subject is extremely scant. Therefore, this study aims to identify and understand the criticality of the dimensions of Quality 4.0.

The present research identifies 12 axes (dimensions) of Quality 4.0 based on literature review and inputs from experts. The identified axes have been prioritized using the analytic hierarchy process (AHP) technique.

The study concludes that the 12 dimensions contribute to outcome indicators such as organizational performance, agility and sustainability. It further adds that though technology is vital for Quality 4.0, elements of traditional quality such as leadership, quality culture, customer focus, quality systems, compliance, competence, analytical thinking, data-driven decision making, etc. are mandatory for the transformation journey. In today's context except for a few matured organizations, others are even struggling to implement the traditional aspects of quality.

Cues to further research are provided which would help in the better understanding of Quality 4.0 and its role in the Industry 4.0 scenario.

This research would help the practitioners understand the determinants of Quality 4.0 system and their effects on organizational performance, agility and sustainability.

The present research work strives to throw light on the criticality of the dimensions of Quality 4.0, thereby contributing to theory building, especially given the paucity of literature in Quality 4.0.