AI and language: why the way we talk about artificial intelligence limits its potential

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The language problem in AI

Artificial intelligence language is at the heart of the news: The way the technology industry describes artificial intelligence is becoming an obstacle to its effective understanding and implementation, especially in the enterprise sector. According to an analysis byTom's Hardware Italy, the vocabulary used to talk about AI has stopped being descriptive to become purely commercial. Terms like “remember,” “reason,” “plan,” or “dream,” as applied to AI models, are verbs that work in marketing but do not correspond to the technical reality of how these machines work.

Human verbs for machines

The main problem lies in the use of verbs that attribute human cognitive abilities to systems that, in their essence, operate through complex mathematical and statistical algorithms. An AI model does not "reason" in the human sense of the term; rather, it processes data to identify patterns and produce outputs based on those patterns. This anthropomorphization, while it may make AI more accessible and fascinating to the general public, creates a discrepancy between common perception and operational reality.

When we say that an AI "learns", we mean that it is trained on a large set of data to optimize its parameters. When we say “plan,” we are referring to computational processes that determine a sequence of actions to achieve a predefined goal. These processes are far from human consciousness, intentionality, or creativity.

Implications for the enterprise

For companies looking to adopt AI-based solutions, this misleading terminology can lead to poor strategic decisions. Unrealistic expectations about an AI system's capabilities can result in poor investments, ineffective implementations and, ultimately, disappointment. Lack of a clear understanding of how these tools actually work can prevent you from realizing their full potential, limiting innovation and operational efficiency.

Towards a more precise language

It is critical that the tech industry adopts more precise and technically accurate language when describing artificial intelligence. Although marketing requires a certain degree of simplification, it is necessary to balance commercial appeal with technical accuracy. This doesn't mean making AI incomprehensible, but rather using terms that better reflect the underlying mechanisms, such as "processing," "pattern identification," "optimization," or "prediction."

More transparent and technically sound communication would allow companies to make more informed choices, to implement AI solutions more effectively and to build a relationship of trust based on a real understanding of the potential and limits of these technologies. Only in this way will artificial intelligence truly be able to transform the enterprise world without being hindered by the language in which it is presented.