Among disruptive technologies, Artificial Intelligence (AI), Robotic Process Automation (RPA) and Machine Learning (ML) play a very important role in Businesses Transformation and continues to show great promise for creating new sources of wealth and new business models. The reality of AI in the company is not reduced to a simple process optimization. In fact, AI introduces new organizational schemes, new ways of working, new optimization niches, new services, other ways of thinking about interactions with customers and therefore a new way of doing business. It thus reshuffles competitive data and imagine innovative processes to create new business models, offering new opportunities not only for IT solution providers but also for innovators, investors and business owners. Even if the contribution of Artificial Intelligence is not to be proved, many companies face difficulties in adopting this technology, mainly due to the lack of a pragmatic approach highlighting the roles and responsibilities of the various stakeholders, especially IT professionals and business owners and the key steps to follow to make this experience a real success. This research aims to answer fundamental questions, in particular: What will bring the implementation of this technology to the business of the company? How to prepare for this adoption? and if the decision to go is confirmed, what kind of adoption approach should companies follow? and finally how can Enterprises monitor this shift to the Intelligent edge.

At the heart of the new enterprise, across all activities, is a decision factory governed by some kind of intelligence. Among the great promises of Artificial Intelligence (AI) is its ability to lead to a significant evolution in the amount of data received, processed, or generates by companies, particularly those with a digital connotation. To bring about dramatic changes, AI does not need to be science fiction but simply a new way of approaching computerization subjects whether in terms of design, development, or terms of expected results. It should be noted that traditional IT solutions present a form of AI called -Weak AI -while the AI that is the subject of much noise, hype, and promises of transformation and potential for growth is called -Strong AI -. This article aims to present, in a didactic way, a model called D2MO (For Data Ops, ML Ops, Model Ops, and AI Ops) allowing the company to operationalize, in a structured approach, AI subjects, activities, and projects. We target through this article to provide both IT and business experts with a new framework offering a perfect articulation between the different bricks and actors entering into the composition of an AI-based system thus allowing them to operate in harmony and an agile mode while taking advantage of this technology.

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Qu'il s'agisse de l'évaluation des formations ou de celle des enseignements voire des établissements, l'évaluation est un moyen de prêter attention à  la qualité de la prestation fournie. Le problême actuel des établissements d'enseignement supérieur n>étant plus uniquement de gérer l'afflux des étudiants, mais aussi et surtout de se focaliser sur la qualité de l'enseignement proposé.Les démarches d>évaluation des enseignements dans le cadre d'une approche globale et évolutive sont un moyen de sensibiliser toutes les parties prenantes dans le processus de formation à  cette qualité, pour améliorer les pratiques d'enseignement et donc pour améliorer la formation des étudiants.Le présent article porte sur les points suivants :- De l'évaluation des enseignements ;- De l'évaluation à  la démarche qualité ;- La politique des évaluations des enseignements au sein du groupe ESIG- De l'évaluation à  l'accréditation des établissements d'enseignement supérieur- Le projet d'accréditation des établissements privés d'enseignement supérieur au Maroc.