From Turing’s theory to translating reality into data: how AI has entered our everyday lives and the energy sector
The history of artificial intelligence did not begin with silicon, but with thought: the attempt to systematise human reasoning into a logical-sequential model has guided generations of scientists, transforming theoretical visions into tools for industrial efficiency. If, in 1726, the writer Jonathan Swift, in Gulliver’s Travels, described with literary flair a mechanical device capable of generating ideas by combining words in different positions, and Ada Lovelace, in the nineteenth century, theorised the first algorithm intended to be processed by a machine, it was in 1950 that the British mathematician Alan Turing laid the scientific foundations of artificial intelligence.
His paradigm, according to which a machine can be considered intelligent if its behaviour is indistinguishable from that of a person, marked the beginning of a specific research trajectory. Six years later, during the Dartmouth conference, scientist John McCarthy formally coined the expression “Artificial Intelligence”, establishing a discipline that today has become a pervasive part of our everyday lives.
Behind increasingly fluid and responsive conversational interfaces, there is no real semantic understanding of the text, but rather complex statistical processing. Every input from the real world, whether a text string, an image or a voice command, is broken down and converted into binary code, meaning numerical sequences. Specific technologies operate within this mathematical perimeter: natural language processing (NLP) enables machines to analyse and replicate the structure of human language, while Large Language Models (LLMs) use neural networks to map the relationships between billions of these numerical sequences, known as tokens.
When generative intelligence formulates a response, it calculates the statistical probability that one word will follow another within a given context. This principle is the basis of machine learning: systems are not programmed to perform a single predefined operation, but are trained to analyse large volumes of unstructured data in order to learn from experience, progressively refining their solutions based on the information processed.
Within a corporate ecosystem, artificial intelligence becomes a concrete tool for improving operational efficiency. At Plenitude, these technologies act as a co-pilot: they work alongside people to simplify workflows and optimise processes at every stage. This evolution is based on an advanced IT infrastructure. As illustrated in the in-depth article on the new Data Platform the shift to cloud-based data management provides the flexibility and power needed to process large volumes of information and run machine learning models.
In practice, this translates into solutions that help redesign the organisation of internal work and the service offered. One example is DataLens, a system that uses visual recognition, or computer vision, to read a photo of a bill and automatically extract the data needed to pre-fill forms. As a result, the time needed to sign a contract from a smartphone has been reduced by 60%.
The evolution of customer service (read the article) instead relies on the use of advanced language models, or LLMs. This technology provides targeted answers to those looking for information, but it also actively supports operators. During a phone call, the algorithm analyses technical manuals in real time to suggest the quickest and most accurate solution. The system eliminates search times, while leaving the professional in full control of the relationship and dialogue with the customer.
The same algorithmic approach supports experts involved in infrastructure planning. In the case of electric mobility, for example, the cross-referencing of geospatial data through the Site Locator helps identify the most suitable locations for new charging points. On the operational side, tools such as the Doc Analyzer support technicians in converting plant reports into structured data and operational KPIs. In all these contexts, technology facilitates the work of professionals during the information collection and initial processing phases, while keeping human supervision firmly at the centre of the entire decision-making process.
As we have seen, artificial intelligence, integrated with Cloud architectures and Big Data analysis, represents a new digital tool that can contribute to the growth of the industrial fabric. This infrastructure does not simply automate existing procedures, but acts as an enabler of processes, whose real effectiveness is determined by the strategic vision of each individual company and by the scope of impact it intends to define. In this scenario, Plenitude has integrated these technological capabilities into its operational dynamics. The aim is to translate innovation into benefits for people, partners and customers, while at the same time ensuring the commitments and responsibilities undertaken within its framework as a Benefit Company.
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