What Is AI: Meaning, How It Works and Examples

Artificial intelligence, often referred to by the acronym AI, is now part of many everyday activities. It suggests the fastest route in navigation apps, helps filter spam in email inboxes, enables voice assistants to understand requests, personalizes content proposed by digital platforms, and supports companies and professionals in analyzing ever-growing amounts of information.

In recent years, the spread of generative AI, capable of creating text, images, audio, video and code, has made artificial intelligence even more visible. However, AI encompasses a much broader set of technologies, some of which have long been used in fields such as industry, medicine, mobility, finance and energy.

According to the OECD‘s updated definition, an artificial intelligence system is a machine-based system that, from the inputs it receives, can generate outputs such as predictions, content, recommendations or decisions capable of influencing physical or virtual environments. Different systems may also display different levels of autonomy and adaptability after deployment.

Understanding what AI is therefore means going beyond the image of a machine that “thinks like a human being”. In most current applications, artificial intelligence is above all a tool capable of analyzing data, identifying patterns and using this information to perform specific tasks.

This capability is also playing a growing role in sectors that manage infrastructure and essential services. In the energy world, for example, AI can help to:

• forecast demand,
• identify anomalies,
• optimize plant operations,
• support network maintenance.

The International Energy Agency highlights how artificial intelligence can transform the way the energy sector operates, while at the same time pointing to the growing electricity demand associated with the data centers that support the development of these technologies.

What is artificial intelligence?

Artificial intelligence is a field of computer science that develops systems capable of performing activities that, if carried out by a person, would normally require certain cognitive abilities: recognizing images, understanding language, making predictions, identifying patterns in data or suggesting a decision.

The term includes very different technologies:

• a system that automatically recognizes a face in a photograph,
• an algorithm that identifies a possible anomaly in an industrial network,
• a generative model that produces text based on a prompt all belong to the world of AI, even though they work in different ways and serve different purposes.

This makes it useful to distinguish artificial intelligence from simple automation. A traditional automated system generally executes a sequence of instructions defined in advance: if a certain condition occurs, it performs a certain action. An AI system, on the other hand, can process data and use mathematical models to recognize more complex relationships, classify information or produce predictions.

The boundary is not always clear-cut. In modern industrial systems, automation, sensors, software and AI can work together, creating processes capable of responding more quickly to operating conditions.

Definition of artificial intelligence

There is no single definition of artificial intelligence that applies to every scientific, technological and regulatory context. A common element, however, is the ability of a computer system to process information and generate useful results to achieve a specific objective.

The OECD definition focuses precisely on this process: an AI system receives inputs and determines how to produce outputs in the form of predictions, content, recommendations or decisions.

A system used for industrial maintenance, for example, may receive data from sensors relating to temperature, pressure, vibration or equipment operation. By analyzing this data, it can identify abnormal behavior and signal the possibility of a malfunction.

An image-recognition application, by contrast, uses visual information to identify objects, people or characteristics in a photograph. A language model works on large amounts of text to analyze and generate natural language.

In all these cases, AI is not the same as the data or the algorithm taken in isolation. It comes from the integration of information, mathematical models, computing power and objectives defined by people.

When was artificial intelligence born? The origins

The ideas behind artificial intelligence predate modern computers, but 1956 is generally considered the year in which AI was born as a scientific discipline.

In the summer of that year, the Dartmouth Summer Research Project on Artificial Intelligence was held at Dartmouth College in the United States. The meeting brought together mathematicians and scientists interested in the possibility of describing certain aspects of intelligence precisely enough to reproduce them through a machine. It was in this context that John McCarthy helped introduce the term “artificial intelligence”.

Since then, AI development has gone through very different phases. Periods of great enthusiasm have alternated with times when technological limitations, costs and excessive expectations slowed research.

One of the main accelerations came with the increasing availability of digital data, the growth of computing power and the evolution of machine-learning algorithms. These factors made it possible to build models capable of tackling increasingly complex problems.

In more recent years, the development of large language models and generative AI has marked a further step, bringing artificial intelligence systems directly into the everyday activities of millions of people and businesses.

How does artificial intelligence work?

Not all AI applications work in the same way. In general terms, however, many systems start from large amounts of data, use algorithms to identify relationships and produce a result based on what they have learned or on the rules according to which they were designed.

Imagine a system used to recognize photographs of different objects. During training, the model may be exposed to many already classified examples. By analyzing them, it identifies recurring characteristics and gradually builds a mathematical representation that allows it to classify images it has never seen before.

The same logic can be applied to very different types of information: text, audio, images, industrial data, energy consumption, financial transactions or signals from sensors.

The result is not necessarily a text response. An AI system can assign a probability, recognize an anomaly, predict a future quantity, classify an item or suggest an action.

This is particularly relevant in industrial contexts. Thousands of sensors can continuously produce information about the operation of networks and plants. Analyzing it manually would be extremely complex; digital systems and algorithms can instead turn these flows into usable operational information.

Data, algorithms and machine learning: the foundations of AI

Three concepts help explain how artificial intelligence works: data, algorithms and machine learning.

Data is the raw material. It may consist of numbers, text, photographs, audio recordings or measurements collected by sensors. Quantity is not the only important factor: reliable results also require data quality, completeness and representativeness.

Algorithms are mathematical and computational procedures used to process this information. Defining how a system should learn, classify or predict means building and using algorithms suited to the specific problem.

Machine learning is one of the main fields within artificial intelligence. Instead of explicitly defining every rule the computer must follow, methods are used through which the system identifies patterns in data and improves its performance on a task.

Within machine learning is deep learning, based on artificial neural networks made up of multiple processing layers. These approaches have contributed to major advances in recent years in image recognition, language understanding and generative AI.

The relationship between these concepts can be summarized as follows:

Generative AI, therefore, is not synonymous with artificial intelligence: it represents one of its most recent and visible applications.

A generative model is trained to recognize structures and relationships within large amounts of data and uses what it has learned to produce new content consistent with the user’s request. This does not necessarily mean that it “understands” the world in the same way as a person: the results derive from mathematical processing of information and must also be assessed in light of the model’s limitations.

What are the types of artificial intelligence?

When discussing types of artificial intelligence, it is important to specify the criterion being used, because different classifications exist.

A widely used distinction concerns the breadth of a system’s capabilities. Most applications available today fall within so-called Artificial Narrow Intelligence (ANI), or narrow AI. These systems are designed to perform specific tasks or limited sets of activities, even when they can do so with very high performance.

A system that detects financial fraud, a recommendation algorithm, image-recognition software or a platform used to predict possible infrastructure anomalies are all examples of specialized AI.

Artificial General Intelligence (AGI), by contrast, refers in theoretical terms to systems capable of performing a wide range of cognitive tasks with flexibility comparable to that of humans. There is currently no universally accepted definition, nor a system broadly recognized as AGI: the concept remains at the center of scientific research and debate.

Artificial Superintelligence (ASI) is also sometimes cited, meaning a hypothetical form of artificial intelligence superior to humans in most cognitive activities. It is, however, a theoretical scenario rather than a technology available today.

This capability-based classification should not be confused with classifications based on technologies or functions. Machine learning, deep learning, computer vision, natural language processing and generative AI describe different approaches or application areas that can be used within specialized systems.

To navigate the main terms:

The distinction also helps explain why speaking generically about “AI” can be imprecise. A generative assistant and an algorithm that controls the behavior of an industrial plant may both belong to the world of artificial intelligence, but they have very different objectives, data, risks and operating methods.

Artificial intelligence: examples of applications in everyday life and business

Many people already use artificial intelligence systems in everyday life without necessarily identifying them as such.

Streaming services and digital platforms use algorithms to suggest content based on users’ preferences. Navigation systems analyze traffic, location and historical data to recommend a route. Smartphones can use AI to improve photographs, recognize speech or automatically organize content and information.

In e-commerce, recommendation algorithms help identify potentially relevant products. In banking, automated systems can help detect unusual transactions. In healthcare, artificial intelligence applications can support the analysis of diagnostic images and large amounts of clinical data, within processes that require appropriate controls and professional accountability.

Generative AI has further expanded the possibilities, providing tools that can assist with writing, programming, information search and content creation. For businesses, this can mean automating or accelerating some activities, but also the need to define rules for quality, security, information protection and verification of results.

In industry, AI plays a different and often less visible role. It can analyze data from machinery and sensors to anticipate failures, optimize production processes, monitor quality or improve energy use.

In the energy sector, the integration of artificial intelligence, digital infrastructure and sensors opens up particularly interesting possibilities. The IEA identifies applications of AI in optimizing energy systems, innovation and energy security, while stressing the need to consider at the same time the growing energy impact of the digital infrastructure that makes AI development possible.

For an energy network, having large amounts of data means being able to know more precisely what is happening across the infrastructure. AI can help turn this data into predictions, alerts and operational guidance, supporting activities such as predictive maintenance, anomaly detection and flow optimization.

At Italgas, this approach has a concrete application in DANA (Digital Advanced Network Automation), the system developed in the Group’s Digital Factory for remote command and control of networks. DANA collects and analyzes data such as pressure, gas volume, odorization level, signals and alarms, and uses algorithms to predict and anticipate potential malfunctions, improve operational efficiency and support real-time decisions.

The combination of digitalization and artificial intelligence therefore makes it possible to move from mainly reactive management to an increasingly predictive model. Knowing network behavior in real time and rapidly analyzing a large number of parameters can help identify a problem before it produces more significant consequences.

This evolution is particularly relevant for critical infrastructure, where continuity, safety and service quality are essential requirements. The adoption of artificial intelligence must therefore be accompanied by data governance, cybersecurity, human oversight and reliable systems.

Artificial intelligence and smart networks: Italgas’ approach

For Italgas, AI is now an integral part of the operating model and one of the levers on which the Group is relying to support the evolution of its infrastructure and services. In the 2026-2032 Strategic Plan, artificial intelligence is applied to operations, business processes and customer management, with the aim of increasing operational efficiency, service quality and sustainability across gas, water, energy efficiency and digital innovation.

AI is therefore part of a broader transformation of networks, which are becoming increasingly smart, digital and flexible. In smart gas networks, data, sensors, automation and algorithms enable remote control, predictive maintenance and more precise flow management, while preparing infrastructure for the integration of renewable gases such as biomethane, hydrogen and synthetic methane.

Artificial intelligence is therefore not only a tool capable of generating content or interacting through a chatbot. It is a cross-cutting technology that can transform the way data is analyzed, decisions are made and complex systems are managed.

In the energy sector, this capability has strategic value: more digitalized networks generate more data, and more advanced AI systems can turn that data into useful information to make infrastructure more efficient, predictive and resilient. It is in this relationship between technology and infrastructure that artificial intelligence can make a concrete contribution to the evolution of essential services and to the transition toward an increasingly sustainable energy system.