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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.

The purpose of the paper is to provide a methodology aimed at improving the setting up of air‐jet looms by clarifying the function which links different important variables involved in the setting procedure and by proposing a method to measure the quality of fabrics depending on the factor values.

The proposed procedure is based on the use of a load sensor: the tension profile received from it is used to analyse the weft behaviour and, sometimes, to predict any quality problems. Because of the high number of variables influencing the set up, the factorial experiments have been used to develop the setting procedure. Numerical results have been analyzed by means of a regression analysis and an ANOVA analysis.

Relationships among different variables and their influence on the quality of the fabric have been derived thanks to the use of a load sensor.

So far, the proposed procedure has been developed for air‐jet looms and for a limited set of fabrics, but it could be adapted in other situations as well.

Use of the proposed procedure allows practitioners to reduce cost and time of the setting up of air jet looms. High productivity of air jet looms could be thus better exploited also for producing small batches of products.

The combination of the load sensor and the statistical analysis allowed the development of a systematic setting procedure for air‐jet looms.