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Artificial intelligence (AI) is a multidisciplinary field of study that aims to provide machines with cognitive abilities similar to those of humans. It is also sometimes referred to as "algorithmic intelligence". Concretely, AI brings together a set of concepts, technologies and techniques allowing computers to imitate certain human capacities such as learning, reasoning, perception, recognition of shapes or objects, interaction in a physical or digital environment, solving complex problems, etc. Artificial intelligence draws on different scientific disciplines such as mathematics, computer science, logic, robotics, neuroscience, linguistics and cognitive psychology. The term "artificial intelligence" was coined in 1956 at a workshop held at Dartmouth University in the United States. Since then, AI has continued to progress thanks to advances in computer computing power and algorithms.

Artificial intelligence works by combining different complementary concepts and mechanisms. At the heart of AI are algorithms, which are sequences of instructions that allow machines to perform complex tasks such as image recognition or language processing. Some powerful algorithms, such as artificial neural networks, are capable of learning from large volumes of data. Machine learning is also an important part of modern artificial intelligence. It allows algorithms to improve themselves autonomously by discovering patterns in large amounts of data, without the tasks to be performed being explicitly programmed. There are three forms of machine learning: supervised, unsupervised and reinforcement learning. Data is also crucial for training AI algorithms and improving their performance. The more voluminous, varied and high-quality the data provided, the better the capabilities of artificial intelligences will be. However, processing all this data requires enormous computing power. Hence the use of high-performance computing infrastructures, cloud computing and specialized processors such as GPUs and TPUs. Finally, knowledge engineering structures and formalizes knowledge so that it can be understood by a machine equipped with artificial intelligence.

Artificial intelligence covers different types of technologies, with varying degrees of autonomy and purposes. There are three main categories of artificial intelligence. First, “weak AI”, or ANI (Artificial Narrow Intelligence), which refers to AI limited to a specific task. Chatbots, autonomous vehicles or facial recognition tools belong to this category of weak artificial intelligence, confined to a restricted field of application. On the other hand, “strong AI”, or AGI (Artificial General Intelligence), aims to reproduce human intelligence in a general way, with universal reasoning, learning and understanding capabilities. This type of strong artificial intelligence does not yet exist and is currently a matter of scientific prospection. Between these two extremes, we find “supervisory AI”, which aims to augment human capabilities rather than replace humans. Coupled with humans, this “augmented intelligence” increases their analytical and decision-making capabilities. Brain-machine interfaces are a concrete example of this. Some specialists also distinguish between so-called "narrow" artificial intelligence, capable only of specific tasks, and "generic" artificial intelligence, with broader and more versatile skills in a given domain. But the boundary between these subcategories remains porous. Ultimately, there is a broad spectrum of AI with varying attributes and purposes, some limited to a specific use, others aimed at matching or enhancing human capabilities in a much broader way.

In the consumer domain, AI powers, for example, facial and voice recognition technologies that allow us to unlock our smartphones or perform biometric identification. Conversational chatbots and intelligent virtual assistants are also increasingly sophisticated thanks to AI, such as: • Siri ; • Alexa ; • Google Assistant. It also continuously improves the performance of machine translation software, whether for translating texts or providing real-time translations. On video and music streaming platforms or e-commerce sites, AI even makes it possible to offer highly personalized recommendations based on each person's tastes. In the field of social networks, content moderation is increasingly entrusted to artificial intelligence algorithms instead of humans. In industry, AI is also transforming many sectors. In the automotive industry, it is enabling innovations in autonomous driving. In healthcare, it is improving medical diagnosis by reliably analyzing medical imaging exams. In banking and insurance, artificial intelligence can detect fraud and analyze risks in a very detailed manner. Its ability to produce automated content such as articles or reports is also increasingly used. AI also optimizes industrial processes, logistics and the supply chain by orchestrating physical flows dynamically. It strengthens cybersecurity by proactively identifying threats. In the manufacturing industry, robots with artificial intelligence now assist humans in many tasks. Finally, in customer services, AI automates back offices and improves customer relationship management through predictive systems. The possibilities offered by artificial intelligence are therefore already immense, and continue to grow as these technologies mature.

The potential offered by artificial intelligence is immense and opens the way to numerous benefits in a multitude of areas. First, the intelligent automation of repetitive and time-consuming tasks enabled by AI brings significant productivity gains. Employees are freed from tedious, low-value-added work to focus on higher-value-added activities. Moreover, thanks to their formidable computing power, machines with artificial intelligence are able to process information at a speed and on a scale inaccessible to the human mind. Decision-making is significantly accelerated. Moreover, once properly trained, AIs often display higher accuracy and reliability than humans, with fewer errors and approximations. Their performance already surpasses humans in some specific tasks. Unlike human workers, AI systems can also operate 24/7 without interruption or loss of attention. Their constant availability is a major asset for certain uses. Thanks to their machine learning algorithms, AIs are also capable of offering hyper-personalized, tailor-made services and recommendations that are difficult to achieve manually. Furthermore, artificial intelligence is paving the way for a new generation of innovative products and services that did not exist before. This capacity for innovation gives a decisive competitive advantage to companies that quickly adopt these technologies.

Although artificial intelligence has made spectacular advances in recent years, it still faces several obstacles and limitations that should be kept in mind. First of all, the internal workings of AI algorithms remain very opaque and difficult to understand for humans. We speak of a "black box" to designate this difficulty in tracing the precise reasoning that led artificial intelligence to make a decision. This opacity can pose problems of reliability and trust with regard to these technologies. Furthermore, biases present in the datasets used to train AIs risk being reproduced and even amplified by the latter. Sexist or racist biases in the training data can thus be found in the decisions of the artificial intelligence trained on this data. Access to massive amounts of quality data is also a crucial issue. Without sufficient representative, varied and well-labeled data, AI cannot train properly and generalize its capabilities to new situations. Cybersecurity represents another challenge for artificial intelligence: malicious or poorly developed AIs can pose problems of hacking, manipulation or leaks of sensitive data. Furthermore, developing reliable AI requires significant technological resources for data storage and processing, as well as human investments in terms of data and algorithm experts. From an ethical perspective, certain applications of artificial intelligence raise questions that should be anticipated and managed, particularly concerning the autonomy of machines, privacy and transparency. Finally, these technologies raise legitimate concerns about their potential impact on jobs, hence psychological resistance to change that must be taken into account. To sum up, artificial intelligence is a promising technology, but it requires transparency, ethics and human supervision to deploy its full potential for the common good.

In travel agencies, sales consultants have little or no time to qualify the themes and preferences of their customers. When creating the customer file, it is possible to "force" the entry of the theme (by creating a mandatory field). However, customer themes change over time. These updates are not always recorded in the "customer" database. Often due to lack of time. It is a tedious task but nevertheless essential to correctly target a mailing. Otherwise, they are blind mailings! Themes: Stay / tour / cruise / family / golf … Preferences: • Hoteliers: 5* 4* charming hotel… • Transport: Favorite company AF, business… • Catering: gourmet, vegetarian… Entertainment: museums, festive… • Cruises: exterior cabins, balconies … Anniversary dates: Clients – accompanying persons – children – wedding A customer changes in his travel habits. He may be a Club Med user and the following year prefer to go on a cruise. We conducted an audit on a sample of 800 users of our CRM. It was a maturity audit to find out if the CRM was used correctly and in particular in the qualification of traveler records. It emerged from this audit that the qualification of traveler records was not done correctly. The results were different depending on the size of the company audited. The smaller you are, the less you respect the rules. The larger the company, the more the rules and procedures were respected. Which seems normal in a more structured company. Audit results based on 3 categories of travel agencies: Agencies 3-5 positions: 50% of customers are unqualified or poorly qualified Agencies 5-10 positions: 40% of customers are never qualified Trays 10 and more: 30% of customers are never qualified ​ AI without information is useless: Without sufficient representative, varied and well-labeled data, AI cannot train properly and generalize its capabilities to new situations. ​ That's why we created the Match&Target robot, which analyzes phenomenal amounts of data on a daily basis and uncovers trends that would otherwise be impossible to detect in customer preferences and themes. Depending on the clients' themes, Match&Target will choose from a library of "newsletters" the one that concerns each client according to their preferred themes. It will send the right news to the right person at the right time without any human intervention! It is the same problem in the hotel industry and even more so in small luxury hotels. CRM software is often "too heavy - too configurable" and difficult to use for the management of a few rooms or suites. This is why we have adapted "Match&Target" to the specific needs of small hotels.