From internal adoption to real impact on communication, marketing, and innovation: this is the thread that shaped Teoresi’s participation in the “New Generative AI Era” event organized by ESCP Business School , in other words, what actually happens when AI is introduced into a company.
During the event, we shared our approach to artificial intelligence through the experience of Beatrice Borgia, Chief Marketing, Innovation & Technology Officer at Teoresi Group.
In our case, the journey has been gradual. It started with internal training and early applications, and then expanded into different areas: research and development first, followed by communication and marketing.
“GenAI accelerates many activities, especially in content production and adaptation,” explained Beatrice Borgia. “But the real point is understanding what actually changes in the way we work.”
For example, the role of people is changing. As some tasks become faster, such as drafting a first version, summarizing documents, or adapting content across channels, other skills become more important: defining clear objectives, designing the process, validating outputs, and making decisions.
At the same time, alongside tools, rules become increasingly central. More automation requires greater attention to issues such as information quality, data bias, traceability, and accountability.
There is also a less visible but highly relevant shift: how companies are discovered online. As more and more answers are generated directly by AI tools, without users visiting websites, it is no longer enough to appear among the top results on Google. What matters is being a recognizable, reliable source — and being cited correctly.
The most tangible impact, however, is in innovation. Here, artificial intelligence is a component that directly contributes to the development of new products and solutions.
In the transportation sector, for example, AI is used to develop predictive diagnosis models: systems that analyze component data and identify potential failures before they occur. In other mobility-related applications, AI is combined with computer vision technologies to monitor driver parameters in real time, in a non-invasive way.
In MedTech, the focus is on the analysis of medical images and clinical data: from the assessment of cardiac conditions to systems that automatically generate descriptions of dermatological images, as well as the use of generative models to create synthetic data for research, especially when real datasets are limited or sensitive.
AI also finds concrete applications in fintech, such as more transparent and interpretable credit risk models, or systems that support investment decisions by taking into account not only returns, but also factors such as sustainability and risk.
These are different fields, but they share a common element: AI becomes a valuable ally in the workplace when it is embedded within a clear process and when decisions are validated through human judgment. Ultimately, the way these tools are used, and the level of awareness behind their use, are what define a mature approach to their potential.