The objective of the work is to evaluate the influence of Artificial Intelligence in the sales activities of B2B companies. The case researched was the Danfoss company, a multinational of Danish origin with B2B sales in more than 100 countries for the markets of refrigeration, heating, inverters and hydraulic in the main industries. A unique case study was employed through participatory observation, with an evaluation of annual reports and semi-structured interviews with 22 employees from various global sales areas, human resources, segment directors, regional presidents and members of the global executive committee who actively participate in defining the sales activities of each region, and globally through digital tools with Artificial Intelligence. In the organization studied, 4 dimensions were identified: Contributions, Possible Disadvantages, Current Moment and the Future with 8 categories of analysis: Internal Processes, Sales Efficiency, Sales Adaptation, Data Security, Behavioral Change, Traditional Salesman, Future Salesmen and the Future of the Company. The data analysis showed different results for each hierarchical level of the company on the contributions and convergences in relation to the Possible Disadvantages. In addition, it was identified that there is a low level of knowledge of Artificial Intelligence and its applications in sales activities and that all respondents do not see a Future without the use of Artificial Intelligence at Danfoss. Dubinsky (1981) was the initial milestone in the discussion of a B2B sales process that is based on seven stages with tasks well defined by the salespeople:

Artificial Intelligence, IA, is a new technology with enormous potential to change the world forever as we know it. It finds applications in many fields of human activity, including services, industry, education, social networks, transportation, among others. However, there is little discussion about the accuracy and reliability of such technology, which has been used in situations where human life depends on its decision-making process, which is the result of its training, one of the stages of development. It is known that the learning process of an Artificial Intelligence, which can use the Artificial Neural Networks technology, presents an error of the predicted value in relation to the real value, which can compromise its application, being more critical in situations where the user's security is a major issue. In this article, we discuss the main technologies used in AI, their development history, considerations about Artificial Neural Networks and the failures arising from the training and hardware processes used. Three types of errors are discussed: The Adversarial Examples, the Soft Errors and the Errors due the lack of Appropriate Training. A case study associated with the third type of error is discussed and actions based on Design of Experiments are proposed. The objective is to change the way the AI models are trained, to add some rare conditions, and to improve their ability to forecast with greater accuracy in any situation

Data is considered a primary resource for innovation. The existence of a large amount of available data, as well as technological tools capable of explore them, allows companies to extract information that can be used to create and implement new ideas and new projects. To this end, the details regarding the care that organizations should have with data are explored. The difficulties regarding the adoption of datadriven approach and some measures to implement this type of decisionmaking approach are also discussed. Some examples of data-driven approaches for diverse industries and products are shown. A real example of prediction model for decision making that is based on industrial data is also discussed. This example shows the difficulties in the preparation of data for the building of these models, which confirms that most of the time spent in the construction of predictive models it is due to this step. The use of the data-driven approach allows organizations to obtain superior results in their processes, thus becoming a tremendous competitive advantage and a special strategic factor in a highly competitive market, regardless of the field of activity.