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Considering the novel concept of Industry 5.0 model, where sustainability is aimed together with integration in the value chain and centrality of people in the production environment, this article focuses on a case where energy efficiency is achieved. The work presents a food industry case where a low-code AI platform was adopted to improve the efficiency and lower environmental footprint impact of its operations. The paper describes the adoption process of the solution integrated with an IIoT architecture that generates data to achieve process optimization. The case shows how a low-code AI platform can ease energy efficiency, considering people in the process, empowering them, and giving a central role in the improvement opportunity. The paper includes a conceptual framework on issues related to Industry 5.0 model, the food industry, IIoT, and machine learning. The adoption case’s relevancy is marked by how the business model looks to democratize artificial intelligence in industrial firms. The proposed model delivers value to ease traditional industries to obtain better operational results and contribute to a better use of resources. Finally, the work intends to go through opportunities that arise around artificial intelligence as a driver for new business and operating models considering the role of people in the process. By empowering industrial engineers with data driven solutions, organizations can ensure that their domain expertise can be applied to data insights to achieve better outcomes.

The direct selling industry presents many opportunities for people who wish to obtain income through the generation of their own business, based on a sales network. In this business model, direct sellers have objectives that transcend the sales activities themselves, such as establishing sustainable interpersonal relationships with their clients in the medium and long term and abilities in administration and management. In this work, we study the performance of direct sellers using traditional data in combination with personality traits and personal profiles of sellers through the DISC test. Results are subjected to statistical analysis, using Data Mining techniques and analytics, such as Principal Component Analysis and Clustering. Results validate those desirable traits for a traditional seller in this industry and show how they are combined with traditional data to identify and describe different groups of behaviour. Besides, we approach the guidelines for an optimal process of sales engineering in this industry.

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