A common misconception about AI is that it should do everything for us simply because we can write a prompt. In my opinion, AI is designed to enhance human effort, not replace it. It’s a powerful tool, but it still requires human judgment, creativity, and decision making to deliver meaningful results. To work effectively with AI, we need to reskill our mindset and learn how to collaborate with the technology rather than delegate everything to it. If we rely entirely on AI and accept its outputs without adding our own voice, tone, or critical thinking, our work quickly becomes generic and loses authenticity. At that point, AI begins to shape how we think and communicate, which can limit originality and diversity of ideas. The true value of AI lies in combining its speed and analytical power with human creativity and perspective. For example, AI can generate drafts, analyze data, or suggest ideas, but it’s up to us to refine, contextualize, and make them resonate with our audience. In short, AI is a partner, not a replacement. The most successful outcomes happen when we use AI as a starting point and then apply our unique insights to create something that is ours and not the AI’s.
It is often seen as something harmless that simply processes real data and gives neutral answers, without people realizing that AI systems also learn from patterns in the data they are trained on and, in some cases, from the way they are used. Many non-experts assume that what they input disappears without consequence, but in reality, interactions can contribute—directly or indirectly—to how these systems are improved, tested, or adapted over time. This misunderstanding leads to the belief that there are no risks in sharing information, when in fact it’s important to be cautious about what kind of data is provided, especially if it is personal or sensitive. While AI does not “remember” things in a human way or store every individual conversation for later use, it can still be influenced by aggregated data and usage patterns.
I think that there is an aspect of AI that is misunderstood by almost everyone, and I include myself here. Most people think that AI thinks or understands like humans. They believe that AI has emotions, intentions or true intelligence, when in reality it works by analysing data, recognizing patterns and following programmed rules. AI does not have common sense, self-awareness or personal experiences. It can sound confident and give convincing answers, but that does not mean it truly understands what it is saying. This misunderstanding can lead people to trust AI too much, assuming it is always correct or neutral. We need to understand that AI is a tool, not a thinking being.
AI in general is often described as a tool or software in line with previous technological development. This unfortunately fails to encapsulate the massive difference there is between AI and other technologies. Other technological leaps historically advanced specific or even multitudes of domains. But AI is increasingly general purpose and advancement in AI intelligence has shown to impact the basis for all fields by assisting (and in some narrow areas surpassing) our intelligence and knowledge gathering. Put simply, a scientific breakthrough in e.g. biotech might advance our medicine, materials we can create and waste management. A breakthrough in AI capabilities can impact biotech itself, as well as robotics, software engineering, physics and even AI research. This can lead to an unprecedented intelligence explosion, and the rapid advancement makes it near impossible to predict the future. We should be careful with exact numbers, but AI capabilities are showing exponential looking progress on multiple parameters, where capacity is doubling roughly every 7 months.
That it does things for you - magically. Let's be clear: without proper knowledge of how to interact with these systems the outcome will not be valuable. The ideal scenario would be to know how everything is working under the hood, but that may not be the most optimal time spent for many. Depending on who you are as a business or individual, a possibility would be to surround yourself with data/AI champions to guide you to create better results and understand things on a more holistic level.
In my experience – both in my own handling of AI and in conversations within the framework of the Digital University Hub and in the working environment at universities – I encounter the same misunderstandings again and again: 1. Myth 1: "AI is built on actual knowledge from trusted sources." In truth, many models train on huge, uncurated datasets. The quality and origin of the training data are often unclear. 2. Myth 2: "Data sources of AI are right." AI hallucinates – i.e. it invents facts, sources and figures that seem plausible but are false. This is not a mistake, but a structural feature. 3. Myth 3: "The output of AI is correct without testing." Outputs must always be evaluated with expertise. If you blindly take over the output, you also take over the mistakes. 4. Myth 4: "AI automatically filters ethical values and bias." In fact, AI systems reflect the biases of their training data. Bias is intrinsic to the system – even if it is not always visible. The most important competence in dealing with AI is therefore not technical operation, but critical thinking: questioning, classifying, verifying. This applies to all stages of digital transformation – from the first AI experiment to the AI-integrated organization. This is precisely the core of AI competence and a core task of knowledge management at universities of the future.
That AI is a search engine – only faster. Many people don't understand what's really behind it: not looking up, but generating. This leads to two equally problematic extremes: Some trust blindly, others reject it fundamentally – and still use it without thinking. The actual potential, namely as a real thinking partner and work accelerator, falls by the wayside.
Probably the idea that it uses water, which was rooted deeply into people. Maybe derived from a misinterpretation of the usage of steam in a lot of generators, but there seem to be people who genuinely believe that water is being somehow drained by its usage. Or the psychosis of everybody getting replaced at their job. I think the rise of AI is coincidental with an ongoing economic recession in Europe and America, and a lot of CEOs are finding the perfect excuse on AI to either lay-off people or just not hire, but human work is heavily appreciated. I ask the reader, if they have the time, to make the exercise of going to Anthropic’s or OpenAI’s website and check for open job positions, and then ask themselves, why then are they requiring more and more humans at these roles.
I use algorithms on a daily basis to support my training activities. In the last 24 hours, one example is how an algorithm helped me adapt a “Human Bingo” game to fit the purposes of a training on radicalisation. It assisted me in restructuring the content, refining the prompts, and aligning the activity more closely with the learning objectives - making the exercise more relevant and effective for participants.
In the last 24 hours, algorithms have supported me in multiple small but important ways, often without me noticing at first. For example, when I checked social media, the content I saw was already filtered and prioritized based on my interests and behavior. This saved me time and helped me focus on relevant information. In my professional work, AI played a key role in researching destinations, doing quick and efficient analysis of data, and preparing presentations. It also helped me summarize tasks and provided practical suggestions that improved my decision-making and productivity. When I used maps or checked traffic conditions, algorithms optimized my route in real time. Even email filtering and spam detection quietly improved my workflow by organizing information and reducing noise. What is interesting is that most of these interactions feel natural and almost invisible. We don’t actively think about the algorithms, but they shape many of our daily decisions and significantly increase our efficiency.