AI Hype: How to recognize features that are not really Artificial Intelligence

If you’ve attended a technology trade show, received a sales proposal, or even just scrolled through LinkedIn, you’ve probably noticed the same thing: AI is everywhere. Every product seems to use it, and every company presents it as a core part of its offering. Yet behind this label, very different realities coexist. Alongside systems that can process information and generate responses, there are also automations that have existed for years but are now being described with a different vocabulary. This is where the term AI Hype comes from—an informal expression used to describe solutions that adopt the language of artificial intelligence to attract attention and strengthen their market positioning, even when the underlying functionality remains largely unchanged.

In many cases, this is not even a deliberate choice. The market exerts significant pressure. Investors are looking for AI-related projects, many funding programs encourage AI adoption, and competitors are already highlighting AI in their communications. In this context, failing to mention artificial intelligence at all can be perceived as a disadvantage. For organizations evaluating technology solutions, the result is clear: vendors often use very similar messaging and make promises that sound increasingly alike. Given this landscape, distinguishing between solutions that genuinely leverage artificial intelligence and those that merely borrow its language is becoming increasingly important for making informed decisions and selecting the tools that best fit business needs.

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Let’s look at five examples of functionalities that are not artificial intelligence, even though they are often described as such:

Rule-Based Automation — A system that follows instructions such as “if X, then Y” is executing logic manually written by a human being. It does not learn from usage, adapt to user behavior, or improve over time. Every response was already anticipated by someone before the question was even asked.

Statistical Analysis Through a Dashboard — A dashboard that displays business data trends processes numbers, not meaning. Aggregating and visualizing information are valuable operations, but a chart that updates automatically is not reasoning: it is counting.

Keyword-Based Search — A search engine that relies on ranking logic returns results based on literal matches between what the user writes and what exists in the database. The real difference comes when a system can understand what someone is looking for even when it is not stated explicitly.

Segment-Based Personalization — Displaying different content to groups of users based on age, geographic location, or purchase history is a well-established marketing technique. A true AI system observes individual behavior and adjusts its predictions independently, without anyone defining categories in advance.

Third-Party Models Resold as Proprietary Technology — Integrating the APIs of an external language model into a product is a legitimate choice, but it does not automatically make that company an AI company. Understanding whether someone has actually developed their own technology or is simply repackaging someone else’s is what allows you to assess the real added value — and justify the price.

Keeping all of this in mind, and knowing that many functionalities presented as AI are not actually artificial intelligence at all, it becomes possible to evaluate technology solutions using different criteria. Rather than focusing on the labels used in commercial presentations, it is worth looking at what the system is truly capable of doing. Does it understand natural language, or does it simply search for keywords? Can it process information from multiple sources to formulate an answer, or is it merely executing predefined rules? Can it generate content, suggestions, or analyses autonomously? Is it able to adapt to new and unexpected requests? And, above all, what technology is it actually using, and what value does it add compared to what was already available before the arrival of generative AI? These are the questions that help cut through market noise and focus on what really matters when choosing a tool designed to support daily work.

With Deepser AI Studio, you do not need to wonder whether you are looking at a case of AI Hype. Artificial intelligence is native to the platform and is based on an LLM that has been developed to understand language and operate directly within business activities. This is the difference that makes otherwise impossible functionalities achievable.

Our chatbot consults tickets, activities, and the knowledge base and, when the available information is not enough, asks targeted questions to identify the right solution — something no system based on fixed rules or keywords can do. AI agents interpret requests, execute operations, and monitor the progress of activities. Intelligent workflows analyze data and updates to support operational decisions, while automated case management classifies requests by understanding their content rather than simply recognizing predefined terms. The platform also summarizes tickets and conversations, facilitating the transfer of information between the people involved, a task that requires genuine text comprehension.

The result is authentic artificial intelligence working within everyday operations and simplifying the work of organizations while generating value throughout the entire service management lifecycle. Try the free demo and discover how we can support your business.