Four Misconceptions About AI: Why Artificial Intelligence Is More Than ChatGP

Misconception 1: "AI equals ChatGPT."

Large Language Models are currently the most well-known type of AI. They can compose text, write code, or generate images. This is impressive and inspiring for good reason: Language models can complete tasks that once took days or even weeks in a matter of seconds. Such efficiency gains illustrate the immense potential of this technology.

However, despite their power, language models represent only a portion of the AI universe. Artificial intelligence is a toolbox of diverse methods, including machine learning, mathematical optimization, simulation, rule-based systems, and decision-making models used in business operations. The most effective method depends heavily on the specific use case.

In aviation, for instance, optimization algorithms calculate how to align staff, vehicles, and resources. These systems have been successfully deployed for many years and are now being enhanced with new technologies. Therefore, AI did not begin with ChatGPT.

The same applies to manufacturing. AI systems help with planning thousands of work steps across multiple machines in mechanical engineering, for instance, in a way that meets delivery deadlines and minimizes costs. These are examples of decision intelligence, demonstrating that AI encompasses more than just natural language processing. AI is a gateway to many other powerful applications.

Misconception 2: "AI will replace all human decision-making."

While the idea that AI could one day replace all human decisions is fascinating, it overlooks the inherent complexity and responsibility of human judgment. Although the concept of fully autonomous systems exists, AI is primarily used as a decision support system in practice.

AI can integrate data from various sources, detect patterns, simulate scenarios, and provide well-informed recommendations. In some cases, AI can make autonomous decisions, but typically only for standardized, repetitive tasks governed by clear rules. Complex, strategic, or exceptional situations still require human involvement.

A practical example from workforce management illustrates this. For instance, suppose a company needs to create weekly shift schedules while considering maximum working hours, rest periods, personal availability, legal requirements, production demands, and more. AI can quickly generate multiple schedule options that best meet these conditions while balancing workload and fairness.

Ultimately, humans make the final decision. This occurs when social aspects outweigh pure efficiency, such as avoiding too many consecutive night shifts for certain employees, or when sudden changes occur, such as sick leave or urgent orders. Management then selects the most suitable plan from the AI-generated options based on efficiency, employee satisfaction, and strategic priorities.

As AI technology matures, it will take over more autonomous decision-making processes. However, there will always be cases requiring human oversight, including exceptions, quality control, and strategic differentiation. AI does not replace human decision-making; rather, it enhances and supports it within a framework of conscious design and control.

Misconception 3: "AI is a black box that is impossible to understand."

Many companies hesitate to use AI because they fear that its decision-making process is not transparent. This perception is only partly true. Generative models and deep neural networks can be complex and difficult to explain. However, there are also many highly transparent AI approaches.

Optimization algorithms, for example, can be traced step by step. Every decision made by such a system is based on clear rules and mathematical logic, which is crucial in heavily regulated industries.

 Today, banks and payment service providers use explainable hybrid AI models to detect potential fraud. These models combine explainable rules with learning components. When a transaction is flagged as suspicious, it is possible to identify the factors that triggered the alert. These factors may include geographic anomalies, sudden foreign transfers, an unusual number of small transactions, or deviations from past behavior.

Ultimately, the decision to block a transaction or refer it for manual review remains with a human. At the same time, compliance and regulatory authorities can clearly see how the decision was made.

Similarly, telecommunications providers use these models to evaluate customer creditworthiness when offering installment plans or device financing. Therefore, AI is not necessarily a black box. The key is choosing the right technology for the task at hand.

Misconception 4: "AI is only relevant for digital pioneers."

Some assume that AI solutions are only useful for tech-driven companies, such as software firms, startups, and digital-first businesses. These companies often have large datasets, digital infrastructures, and an innovative culture. However, this view is far too narrow. AI technologies already create tangible value across a wide range of industries and processes.

In vehicle logistics, for instance, AI systems optimize the order in which vehicles are loaded onto trains or ships. This may sound trivial, but it has major consequences. If cars are loaded inefficiently, they can block others during unloading, resulting in longer wait times, unnecessary movement, and additional costs.

AI calculates the optimal loading sequence, ensuring vehicles are immediately accessible at their destination. This saves time and reduces costs and the CO₂ footprint by minimizing detours and idle time. In the maritime industry, AI helps plan the distribution of containers across ships and terminals, reducing turnaround times and preventing bottlenecks. In retail, AI-based demand forecasting prevents overstocking and strengthens supply chain resilience.

These examples demonstrate that AI is not just a futuristic topic for Silicon Valley "digital pioneers." It is already a critical factor in industries where efficiency and resilience are paramount.

From Hype to Practice: What Really Matters

AI is not a monolithic system. It is a flexible toolbox. Those who equate it solely with language models miss out on crucial opportunities. The key to success lies in differentiation. What problem am I trying to solve? Which technology is best suited to solve it? How can I integrate it into my processes?

At INFORM, we help companies from diverse industries answer these questions. Our solutions incorporate Decision Intelligence into complex business processes, increasing efficiency and ensuring future readiness. This is how much-discussed hype turns into tangible practice and technology becomes real value.