The Over The Top companies such as Facebook, Google, Amazon, Apple, and Microsoft are battling not only to bring innovative startups in the field of A.I. into their own but also to start and feed research projects of which we are already seeing some fruits (such as the recognition images, faces, voice applications, language translations, etc.).
Today, technological maturity means that Artificial Intelligence leaves the riverbed of research to enter everyday life. If, as consumers, we have important “tastes” of it above all thanks to Google and Facebook, in the business world, the maturity (and availability) of technological solutions has brought the potential of A.I. to many segments. These are the ones most in turmoil right now:
Artificial intelligence applied to Sales has already demonstrated important results, in particular thanks to expert systems [applications that fall within the branch of artificial intelligence because they reproduce the performance of an expert in a specific domain of knowledge or field of activity – ed ]. The solutions that integrate expert systems allow users (even non-experts) to solve particularly complex problems. The intervention of a human being expert in the specific sector, activity, or knowledge domain where the problem arises would be necessary.
In simple terms, they are systems that allow people to find a solution to a problem without requiring an expert’s intervention. From the technological point of view, the expert systems allow automatically implementing some inference procedures (i.e., of logic: with an inductive or deductive process, a conclusion is reached following the analysis of a series of facts or circumstances). In particular, the so-called rule-based expert systems exploit the well-known principles in IF-THEN computer science. It is the condition and Then the action (if a certain condition occurs, then a certain action occurs).
Expert systems fall within the branch of artificial intelligence rather than in the context of normal software programs. Given a series of facts, expert systems can deduce new facts thanks to the rules of which they are composed. These systems are particularly performing and suitable for commercial configurators (solutions used by Sales in business models where the proposal is particularly complex due to the very nature of the products marketed, for the possible combinations of solutions, for the variables that affect the final result and, therefore, on the realization of the product itself and its price).
In general, a product configurator must perform the task of simplifying the choice of an asset to purchase; the process is not always immediate when the variables involved are numerous (sizing, large number of components, use of particular materials, combination of raw materials, and various materials with consequent impacts on physical, mechanical or chemical properties, etc.).
When products that need to be configured are to be included in complex projects (we are thinking of manufacturing plants or systems and machinery that must operate in particular climatic conditions or “critical” industrial environments), the product configurators must be “expert and intelligent “the point of enabling users to independently search, identify, evaluate and request what they need, without resorting to the technical expert.
It is precisely here that expert systems – such as those developed by My – best express their potential. DECLARE, for example, is a “rule engine” that allows the product configurator to propose the right questions to the non-expert user, followed by other correct questions. The accumulation of experiences (between questions and answers) accelerates and makes the configuration of the solution suited to your needs more effectively and becomes a company knowledge base system that is continuously enriched.
In the solution developed by Myti, the “engine” of questions and answers is presented as a common web interface. The If-Then rules are built upstream by the domain expert. Still, the system is then able to ask questions to a non-expert user and based on the answers – by accessing the knowledge of the expert who defined the rules – do other questions that help the user (for example, the seller) to choose and then to configure a complex product or an articulated commercial proposal.
Voice / virtual assistants ( chatbot, Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa ) that use artificial intelligence both for natural language recognition and for learning and analyzing user habits and behaviors; real-time analysis of large amounts of data for understanding the “sentiment” and needs of people to improve customer care, user experience, assistance, and support services.
But also to create and refine sophisticated engagement mechanisms with activities that push up to the forecast of purchasing behaviors from which to derive communication strategies and service proposals. The A.I. in Marketing has shown all its maximum power for a couple of years. The largest area of use is managing the relationship with users.
Artificial Intelligence Marketing (A.I.M.), Algorithms To Persuade People
For several years a real discipline has been born, Artificial Intelligence Marketing (A.I.M.), a branch of Marketing that uses the most modern technologies that fall within the field of Artificial Intelligence, such as Machine Learning and Nlp – Natural Language Processing, integrated with mathematical/statistical techniques and behavioral marketing (behavioral targeting).
In concrete terms, it is the use of artificial intelligence and Machine Learning algorithms to persuade people to take any action, buy a product, or access a service (in other words, respond to a “call to action “). Aggregation and analysis of data (even those unstructured and based on natural language) in a continuous process of learning and improvement to identify, from time to time, the probably most effective actions, strategies and communication and sales techniques (those that have the potential higher efficacy/success for individual target users). This is essentially what A.I.M. does
A.I. has had the advantage of improving many technological systems already in use by people with disabilities (for example, the voice systems have improved to allow a completely natural relationship/communication even to those who are unable to speak). Still, it is on the front of the diagnosis and treatment of tumors and rare diseases it will be possible to see the new capabilities of A.I.
Already today, cognitive systems are available on the market capable of drawing, analyzing, and learning from an infinite pool of data (scientific publications, research, medical records, drug data, etc.) at speed unimaginable for humans, accelerating processes of often very critical diagnoses for rare diseases or suggesting optimal treatment pathways in the case of tumors or particular diseases. Not only that, AI-based virtual assistants are starting to see each other more frequently in operating theaters, in support of reception staff, or those who offer first aid services.
Fraud prevention is one of the most mature applications where artificial intelligence materializes with what is technically called “advanced analytics,” very sophisticated analyzes that correlate data, events, behaviors, and habits to understand in advance any fraudulent activity (such as cloning a credit card or executing an unauthorized transaction); these systems can also find application within other business contexts, for example for the mitigation of risks, the protection of information and data, the fight against cybercrime.
The optimization and management of the supply and distribution chain now require sophisticated analyzes and, in this case, A.I. is the effective system that allows you to connect and monitor the entire supply chain and all the players involved.
A very significant case of application of artificial intelligence to the Supply chain management sector is related to order management (in this case the technologies that exploit artificial intelligence not only aim at the simplification of processes but also the total integration of them, from purchases up to inventory, from warehouse to sales up to even integration with marketing for the preventive management of supplies according to promotional activities or communication campaigns).
The ability to analyze huge amounts of data in real-time and “deduce” through correlations of events, habits, behaviors, attitudes, systems, and geo-localization data and monitoring the movement of things and people offers enormous potential for the improvement of efficiency and effectiveness of public safety. For example, the safety and prevention of crimes in airports, railway stations, and metropolitan cities or the prevention and management of the crisis in cases of natural disasters such as earthquakes and tsunamis.
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