AI Hype and FOMO: Between Potential and Exaggeration
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Why AI is often overestimated and underestimated at the same time and how companies can turn the hype into real business value.
For several years, we have been experiencing a wave of enthusiasm for artificial intelligence (AI). Terms like “Generative AI”, “Machine Learning” and “Predictive Analytics” appear in nearly every business discussion, and they often come with a growing fear of missing out.
This fear leads decision-makers to invest quickly, sometimes without a clear understanding of what exactly they are purchasing. But how did the AI hype emerge? Which forces are shaping it? And is it justified?
The roots of the AI hype
The idea that machines could think, recognize patterns and make decisions has existed for decades. As early as the 1950s, pioneers such as John McCarthy and Marvin Minsky created research environments that laid the foundations for the field of artificial intelligence. Over the decades, expectations shifted repeatedly between high hopes and disappointing realities. These cycles are known as AI winters and AI summers. Today, we find ourselves in a prolonged AI summer.
However, the dynamics have changed. Advances in computing power, data availability, cloud technology and algorithms now make substantial potential visible. At the same time, media attention and investment capital create rapidly growing expectations. AI is often framed as a technology that will transform the world. All of this together fuels the hype.
Hype dynamics: why technologies expand so rapidly
Certain patterns can be observed in the rise of any technological hype. A scientific or technical breakthrough is followed by a wave of media attention and investor interest. This usually includes a compelling narrative that promises large-scale transformation. At the same time, the fear of falling behind emerges. Companies worry that they will be overtaken if they do not participate. This fear is not unfounded, since history shows several examples of companies that lost their leadership position because they missed a major shift. One well-known example is Nokia. For many years, Nokia dominated the mobile phone market. The company failed to adapt to the transition toward smartphones and software ecosystems. The core issue was not a lack of technological capability but a strategic misjudgment regarding the speed and depth of market change.
This dynamic, which includes hesitation caused by uncertainty or adherence to old patterns, can also be observed in today’s adoption of AI. Many companies know that change is necessary, yet they wait, observe or invest out of fear of missing out rather than out of strategy. As a result, solutions are purchased and pilot projects launched without clear expectations or defined success metrics. The hype also encourages vendors to make promises that exceed what is currently feasible, which creates uncertainty and risk.
A look back: “The internet is just hype”
A well-known example of how technological predictions can miss the mark is Clifford Stoll’s 1995 article in Newsweek titled “The Internet Won’t Change Everything”. In it, he claimed that online databases would not replace daily newspapers and that computer networks would not change the way government works.
This misjudgment shows that the impact of new technologies is often either overestimated or underestimated. At that time, the infrastructure, market and user habits surrounding the internet were not yet developed. In hindsight, such claims seem almost humorous, but they appeared plausible at the time. The parallel to AI is clear. We are still at the beginning of many potential applications, and we should not assume that every use case will succeed immediately.
Is the AI hype justified? Potential and reality
There are strong reasons not to dismiss AI as mere hype. AI-driven analytics allow for pattern recognition in data volumes that humans can no longer process efficiently. Industries such as medicine, automotive, manufacturing and logistics already rely on AI systems. The same applies to wholesale distribution. AI can help optimize margins, predict customer behavior and identify cross-selling opportunities. At the same time, not every application is equally effective. Not every promise becomes reality overnight. Some projects will fail or generate less impact than expected. The risk lies in investments driven by fear of missing out without a clear business case or a solid data foundation.
Avoiding FOMO: how companies can navigate the hype wisely
To avoid falling into the hype trap, several practical strategies can help.
1. Begin with a real problem rather than with the technology itself. What specifically limits your sales performance, pricing strategy or operational efficiency? Identify the bottleneck and ask whether AI can help.
2. Limit yourself to a few systems or clearly defined use cases instead of attempting everything at once. A focused pilot project is far more valuable than a large and uncontrolled initiative.
3. Exchange insights with other companies. Who uses which AI solutions? What has worked for them? What has not? Learn from the mistakes of others.
4. Pay attention to data quality and internal processes. AI depends on data that is clean, accessible and meaningful. Without this, the hype becomes a burden.
5. Define clear performance metrics and goals. How much efficiency gain, margin improvement or customer satisfaction do you expect? And at what point should the project be scaled?
6. Finally, treat AI as a tool. It is not a replacement for strategy, relationships or expertise. Sales teams remain essential and AI supports their decisions with data.
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AI-Hype & FOMO – Conclusion
The AI hype is real, but it should not be confused with blind investment or exaggerated expectations. It exists between significant potential and overpromised visions. The parallel to the internet shows that what appears to be hype today may become everyday reality in a few years. The path toward this future requires patience, clarity and a realistic mindset. Companies that start with focus, stay intentional and resist the pressure of FOMO can gain a competitive advantage without taking unnecessary risks.
As futurist Roy Amara famously said, “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” This is exactly why AI deserves a balanced perspective. It should not be viewed as a miracle cure or a threat but as a tool with long-term impact.