Artificial Intelligence: from expert systems to generative ai

Artificial Intelligence solutions

generative ai

Artificial Intelligence (AI) has been at the center of attention for many decades and has undergone rapid evolution in recent years. AI is a field that deals with creating machines that can perform tasks that require human intelligence, such as reasoning, learning, and understanding language. In recent years, we have seen the emergence of a wide range of AI approaches, from expert systems to generative AI, each of which has its own strengths and weaknesses.

But what exactly is meant by expert systems or generative AI?

To understand this and comprehend the potential of these technologies and the benefits of their development, let’s start by understanding how they work.

Expert systems: what they are and how they work

Expert systems, developed as a concept in the 1970s by computer scientist Edward Feigenbaum, professor of computer science at Stanford University and founder of the Knowledge Systems Laboratory at Stanford, were the first types of artificial intelligence to be used in various applications.

Designed to complement, not replace, human experts, these rule-based systems made it possible to:

  • Diagnose;
  • Make decisions;
  • Solve problems.

The fields of application where expert systems have been used include:

  • Financial services;
  • Mechanical engineering;
  • Telecommunications;
  • Healthcare;
  • Agriculture;
  • Customer service;
  • Transportation;
  • Law.

Expert systems are limited by their learning and adaptation capabilities, as they are based on predefined algorithms and cannot learn from new information. This has limited their industrial application in the past, while today we are witnessing a resurgence of these technologies thanks to the possibility of combining them with generative techniques, giving rise to so-called neuro-symbolic approaches.

Deep Learning and neural networks: what they are and how they work

As technology has advanced, more advanced AI approaches have been developed, such as neural networks, which use so-called “deep learning” to autonomously learn from the data they receive.

Neural networks are made up of layers of artificial neurons that process information through artificial synaptic connections. The learning of neural networks takes place through the adaptation of the weights of the connections, which allow the network to gradually improve its ability to recognize and classify the input data. Deep learning is used in many applications, such as image processing, speech recognition and machine translation.

Neural networks have been used in a wide variety of applications, including voice and facial recognition, autonomous driving, and financial market forecasting. Thanks to neural networks, AI has become able to solve more complex problems and adapt to new information.

Generative AI

In addition to neural networks, AI has seen the emergence of another advanced approach called generative AI.

Generative AI is an area of artificial intelligence that uses machine learning algorithms to generate new data such as images, text or sound by mimicking the characteristics and structures of the input data. Generative AI is used to create customized and adaptable solutions for different applications, such as digital art, music and fashion.

For example, generative AI can be used to create customized clothing designs for customers based on their style preferences, or to create automatically generated pieces of music based on a specific genre or mood. Generative AI can also be used to generate new content on its own, such as creating a digital painting or logo for a company. In general, generative AI is becoming increasingly important in creating customized and unique solutions for many different applications.

Hybrid approaches: advantages and applications in the manufacturing and smart cities sectors

The combination of different AI approaches is leading to innovative and potentially game-changing results.

The combination of expert systems and generative AI can lead to customized and adaptable solutions for the manufacturing sector. For example, a manufacturing company could use an expert system to analyze production data and identify inefficiencies, while generative AI could be used to create a customized production model for each customer. In this way, the company could offer unique solutions adapted to customer needs, increasing loyalty.

In the smart city sector, the combination of expert systems and generative AI can lead to innovative solutions to manage traffic, monitor air quality and improve public safety. For example, sensors could be used to detect the level of air pollution, and an expert system could analyze the data to identify pollution sources and propose mitigation solutions. Meanwhile, generative AI could be used to create a personalized traffic reporting system for each driver, based on their routes and preferences.

In general, the combination of expert systems and generative AI can lead to innovative and customized solutions for many different applications. However, it is important to carefully consider which AI approaches to use based on the specific needs of the application, in order to obtain the best possible results.

AI: the benefits for businesses

Understanding the main advantages that companies can obtain by adopting artificial intelligence solutions is now more crucial than ever.

Let’s take a look at some of the benefits and some of the solutions developed by Revelis for the optimization of various business processes.

  • Process automation, reducing costs and improving efficiency. In these cases, i-Plan, the solution developed by Revelis, can be the suitable solution to streamline processes. It is an automatic reasoning platform that allows for automatic calculation of work teams in logistics and manufacturing companies, optimizing shift scheduling processes.
  • Personalization, for example, in the field of marketing, to offer a more engaging experience. While 62% of consumers have expressed concern about AI bias, 69% of those surveyed by Salesforce said they were open to its use by brands if it improves their purchasing experience. In this case, ARTICA, one of the Revelis AI solutions for Text Analytics, is an excellent example of how AI application functionalities can improve customer experience. Its operation is based on Natural Language Processing algorithms combined with machine learning and deep learning models, and its purpose is to analyze users’ ticket texts and support their resolution.
  • Product quality improvement, for example, through the analysis of large amounts of data to identify weaknesses and suggest improvements. In these cases, implementing Computer Vision techniques can enable quality control even in industrial contexts. Colibrì, the platform that uses neural networks to implement Computer Vision techniques developed by Revelis, is an excellent example of how to leverage AI applications to improve product quality.
  • Innovation, for example, for the development of services and products.
  • Decision support, thanks to the information extracted from big data analysis, it is possible to make data-driven decisions.
  • Reduction of errors, for example, in the processing of large amounts of data.


As early as 2022, Gartner had included generative artificial intelligence in its list of strategic tech trends for 2022, and today the DAMO Academy of Alibaba confirms this trend for 2023 in its recent predictions on the main technological trends that could shape many industries in the near future, highlighting how generative AI will be among the main technological trends in 2023.


According to the World Economic Forum, the field of generative AI can generate trillions of dollars in economic value and, in fact, over 150 startups have already emerged that are operating in the field.

Generative artificial intelligence technologies, which have made great strides and achieved great progress for companies in various sectors by using machine learning, natural language processing, computer vision, and deep learning, are therefore destined to grow even further in the near future, and its impact will be seen both at the economic-financial level and at the social level.

Continue reading the Revelis blog to stay up-to-date with the latest news in the field of artificial intelligence, and if you too want to make the most of AI applications for your business, contact us.