Understanding Artificial Intelligence: Real vs. Fake AI
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Artificial intelligence has evolved rapidly in recent years and is no longer limited to research institutions or large technology companies. It now appears in business applications, everyday software and operational decision making.
This growing spread, however, also raises new questions. One of them is whether there is such a thing as real and false AI. Behind this question lies the desire of many companies to better understand the technologies they use and to realistically assess their potential.
Are there real and false forms of artificial intelligence?
The term false AI refers to applications that appear intelligent at first glance but do not exhibit the characteristics of learning systems. These systems often rely on fixed rules or rigid models and are unable to learn independently or adapt to changing data. This can lead to misunderstandings, as users may expect learning capabilities that are not technically present.
Distinguishing between real and false AI is difficult, however, because there is still no universally accepted definition of artificial intelligence. Even the question of what intelligence actually means is answered differently. Elaine Rich describes AI as the effort to design computers so that they can perform tasks at which humans are currently still superior. This definition is helpful because it links AI to concrete capabilities such as image recognition, language understanding, pattern detection or predictions based on experience.
As a result, there is much debate about “fake AI” and the definition of real AI.
Some argue that true AI must be able to learn independently and continuously adapt to new data and environments. Others emphasize that the definition of AI should be based less on functionality and more on the ability to make intelligent decisions.
Despite the lack of a precise definition of artificial intelligence, there are some examples that are not regarded as AI (i.e. as “fake AI”) by the majority of AI experts:
1. The Illusion of Intelligence:
A prominent example of “fake AI” can be found in some virtual assistants and chatbots. They can appear sophisticated and even understand natural language, but their “intelligence” is often based on predefined scripts. They are limited in understanding and cannot learn independently or solve complex problems.
2. Rule-based Systems:
Traditional rule-based systems are also often considered “fake AI”. These systems work according to predefined rules and instructions without the ability to adapt or learn on their own. They depend on clear instructions and cannot react to unforeseen situations.
3. AI Washing:
Another aspect that contributes to the confusion is so-called “AI-washing”. That is the conscious or unconscious overemphasis of AI elements in products or services to make them appear more modern or advanced. For example, companies may sell simple automation processes as AI integration when the underlying technology is limited. The EU AI Act addresses this issue by strengthening transparency requirements for providers of digital systems. Companies must communicate more clearly when a system actually uses AI and what level of autonomy or learning capability it offers. For users, this provides helpful guidance to better understand which technology they are acquiring and which expectations are realistic.
Is only Generative AI “Real AI”?
Since generative AI capabilities have been made available to the general public through chat GPT, there has also been discussion about whether only this type of AI should be considered “real.”
Generative AI refers to models that can create new data or content, whether in the form of text, images, or other media. These models, particularly those based on neural networks, can demonstrate creative capabilities by learning from patterns in the data and generating new, previously unseen content.
It is important to emphasize, however, that generative AI represents only one facet of the broad spectrum of AI technologies. Other systems do not focus on creative output but on analysis, pattern recognition, decision support or forecasting. These systems also use neural networks or statistical models to learn from data and improve continuously. With the emergence of so-called agentic AI, which not only generates content but also plans tasks, performs actions and learns from outcomes, the boundaries between the different categories continue to blur. What qualifies as real AI therefore depends less on the type of output and more on whether a system demonstrates capabilities associated with intelligence. These include learning, generalizing, solving complex problems and adapting to new situations.
Generative AI is an impressive example of how AI systems can produce creative results based on large amounts of data. However, it would be too narrow to use this capability as the sole benchmark for real AI. Applications in forecasting, risk assessment or pattern recognition are equally valuable for many companies and are scientifically just as sophisticated.
Ultimately, the question of what is considered “true AI” lies in the definition and expectations of AI systems.
What Companies should Consider
Much more important than whether it is “real AI” is the question of the company’s own application goals and expectations. Many companies don’t want to miss out on the new AI trend and say, “We have to do something with AI!”
However, as we know from Elaine Rich, artificial intelligence systems are also tools for specific application problems.
That means it is important to understand what goals the system should achieve in your company. In some cases, simple automation mechanisms or rule-based systems may be completely sufficient. In other cases, when it comes to complex problem solving, very large data sets, or autonomous learning, artificial intelligence may be required.
With the EU AI Act, the question of transparency is becoming even more important. Companies should understand how a system makes decisions and whether it incorporates learning mechanisms that are meaningful for the respective business process. Especially in B2B and wholesale, AI proves most valuable when models learn from real transactional data, identify patterns and generate predictions for pricing, churn or cross-selling. Systems that rely solely on predefined rules quickly reach their limits in these scenarios.
Companies should also assess whether the AI they use can adapt to future requirements. Learning systems continue to evolve as new data becomes available, making them an essential component for organizations that aim to operate in a data-driven way over the long term.
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Understanding Artificial Intelligence: Real vs Fake AI – Conclusion:
It is crucial for companies to assess the capabilities of AI systems they are deploying or considering deploying realistically. This includes understanding whether these systems are actually capable of learning and adapting from data, which would make them “true AI.”
Investments in AI should be based on a clear understanding of your business strategy and specific requirements. It is important to look beyond the hype and soberly assess the technological capabilities and limitations.
Sometimes, a well-designed system tailored to specific requirements can be very effective even without “real AI”. In other cases, AI systems have clear advantages over their static counterparts. It is, therefore, important to carefully assess the specific requirements and objectives and select the technology accordingly.