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The hard truth: AI is not intelligent
Why AI won't solve your problems (and what does work)
AI has been a hot topic for years, but the term “intelligence” can be misleading. Even tech experts at Apple have publicly expressed doubts about what is labeled “AI” these days. Think of the “Apple Intelligence” on the latest iPhones or systems like ChatGPT, Claude and Gemini” imprinted but they still lack features that we as humans associate with true intelligence. The biggest shortcoming? The ability to really reason. But that's not all. Where real intelligence is about learning, understanding, logical thinking and dealing with new, complex situations, AI remains strong in recognizing patterns and predicting outcomes based on those patterns.
And that difference? That's crucial for anyone making decisions about IT - from managers to CTOs. Because if you misunderstand AI, you run big risks.
AI excels at imitating
Most AI applications we use today are based on Machine Learning (ML). These models are trained with huge amounts of data where the relationship between input and output is already known. Based on that, when new input is received, they predict which output is correct with the highest probability. Simply put: they predict, but they do not understand.
Popular AI tools such as ChatGPT, are extremely complex ML models. Thanks to gigantic amounts of text data, they can amazingly estimate which words you are likely to want to see based on your prompt. However, they do so not because they “understand” what you are asking, but because they have learned that if words A, B and C occur together, word D usually follows. It's like the text prediction on your phone, but with whole sentences or paragraphs, for example, instead of single words. It's an advanced computational process, not intelligence.
Learning is limited to what humans put into it
AI's learning is entirely dependent on the data we humans put into it. AI does not learn anything on its own; it is fed data sets carefully curated and labeled by humans. This means that the quality, diversity and completeness of that data directly determine what AI can “learn.” When the input is one-sided, incomplete or incorrect, you see that reflected in the output.
But here comes the real difference: AI lacks the ability to think beyond what it has been taught; it cannot create new insights or spontaneously make connections not present in the data. AI does not understand what it learns. It recognizes patterns in data, but without any context or meaning. This is fundamentally different from how humans learn. Where we as humans make connections, expand knowledge and learn from our mistakes, AI can only operate within the framework of its training.
Creativity, curiosity and the ability to push the boundaries of knowledge itself is reserved for the human brain for now. It is ultimately a mirror of the information we put into it, nothing more and nothing less.
AI cannot reason
AI lacks the ability to really reason. Why? Because it has no understanding of the concepts behind the data it works with. Whereas human reasoning is based on logical thinking, making connections and understanding context, AI works exclusively with patterns it has learned from huge data sets. It can predict a highly probable outcome, but not explain why that outcome is correct, nor consider alternatives based on logic or moral considerations.
In addition, AI lacks the flexibility to deal with unfamiliar situations. Reasoning requires the ability to reflect, apply previous experience to new problems and ask critical questions. AI performs calculations only within the limits of the data and algorithms it has been trained with. It is a powerful tool for recognition and prediction, but without a true understanding of cause and effect, reasoning remains beyond its reach. And that is a very important fact for IT managers and CIOs.
Dealing with unfamiliar situations still reserved for humans
AI cannot deal with new and challenging situations because it depends entirely on the patterns it has learned during its training. It operates within the frameworks of previously collected data and predicts outcomes based on what it has already seen. When a situation arises that falls outside these frameworks, AI lacks the flexibility to come up with new solutions on its own. It has no intuition, creativity or ability to interpret unexpected variables.
For IT managers and CIOs, this means being alert to the risks that arise with complex systems. In many organizations, programmers use AI to generate code, but even there things often go wrong. Poorly generated code or incomplete data sets increase the likelihood of errors and inefficiencies because AI cannot fathom complexity the way a human can.
In addition, AI lacks the ability to understand context, which is crucial in complex or unfamiliar situations. Whereas humans - and programmers in particular - can assess the broader implications and nuances of a problem, AI is limited to the data it knows. This can lead to errors or ineffective decisions when confronted with circumstances not present in its training data. Without understanding cause-and-effect relationships or the larger context, AI cannot anticipate changes or respond appropriately to unforeseen challenges.
Another important point is that AI cannot learn from new situations as they arise. Whereas humans can reflect, adjust and come up with creative solutions in real time, AI relies on a renewed training process to acquire new knowledge. This makes it unsuitable for situations that require quick, innovative or intuitive decisions. This is precisely why it is essential that IT managers and CIOs use AI to complement human expertise, rather than relying on it blindly. AI is a powerful tool within known and structured frameworks, but it remains limited in its ability to navigate the unknown.
Even the Turing test is no longer proof
Passing a Turing test does not prove a machine's intelligence. This is because this test only measures how well a machine can imitate human communication, not whether it actually understands what it is saying. The Turing test, devised by Alan Turing in 1950, determines whether a machine can be considered intelligent when a human interlocutor cannot distinguish whether it is communicating with a human or a machine. Modern AIs such as ChatGPT sometimes pass this test gloriously but that is purely due to recognizing and mimicking patterns from huge amounts of text data. They do not really understand the content of the conversation, have no awareness and lack the capacity for abstract reasoning, contextual understanding or original thought. It is an illusion of intelligence, not proof of it.
AI is not intelligent, but it is useful
AI is an extremely valuable tool, if used in the right way. It can analyze vast amounts of data and recognize patterns that would be difficult or time-consuming for humans to detect. This makes it ideal for tasks such as process automation, spotting and detecting anomalies and improving efficiency in a variety of industries.
For IT managers, this means that AI can be a powerful tool, but not a replacement for human expertise.
When AI is used to complement human expertise-and not replace it-it can help organizations make better decisions, cut costs and accelerate innovation. However, it requires a keen understanding of AI's limitations so that it is not deployed where human thinking, creativity or strategic decisions are needed.
Use AI as 'Augmented Intelligence'
Augmented Intelligence - or as we call it Computer Aided Human Intelligenge - combines the best of Artificial Intelligence and human capabilities to achieve better results. Rather than trying to replace human intelligence, Augmented Intelligence uses AI as a support tool to make work more efficient and effective.
For IT managers, this offers the best of both worlds: AI can provide insights from data, automate repetitive tasks and make recommendations, while humans can apply their intuition, creativity and contextual understanding. That collaboration leads to better decisions, more efficient processes and innovation, without getting bogged down by the limitations of AI.
Using Augmented Intelligence to maximize cloud savings
At Sciante, we use Augmented Intelligence to fully understand and optimize complex cost structures, such as cloud costs. By combining the power of Artificial Intelligence with human expertise, we detect and realize hidden inefficiencies and realize cost reductions of tens of percent.
Our AI tools analyze large amounts of data and identify patterns that are often hidden to the eye. Our specialists then translate these insights into concrete, strategic actions that fit your organization. For IT managers, this means direct savings and sustainable optimizations, without additional risk or workload for your team.
Make a no-obligation appointment now and discover how we can reduce your cloud bill by dozens of percent.