The current situation in the artificial intelligence market resembles a gold rush. Everyone feels that a huge opportunity is waiting on the horizon and the train to the future is just leaving the station. There is a rush in boardrooms and board meetings just to get a seat on it.
The question is: is this momentum driven by a considered strategy or is it driven by a panicky fear of being left behind? Unfortunately, more and more data points to the latter. This phenomenon, referred to as AI FOMO (Fear Of Missing Out), is becoming a silent killer of budgets and innovation.
Diagnosis of the phenomenon: ‘Defence’ investment is not a strategy
In an ideal world, every decision to implement a new technology would be preceded by an in-depth analysis. The team identifies the problem, looks for the best solution and then implements it, measuring specific success metrics. The reality of the AI era, however, is different.
A worrying picture emerges from the latest European market research, conducted by the ESSCA Management School’s AI Sustainability Institute. It turns out that only one in four companies (23%) are implementing AI in response to a clearly identified, pre-existing need.
What motivates the others? As many as six out of ten organisations admit that they invest for what could be called ‘defensive’ reasons – not to lose competitive advantage and ‘not to miss the train’ – or under vague suggestions from the board or external consultants.
This is a classic example of technology looking for a problem to solve, rather than the other way around. This approach leads to ghost projects: costly, without clear objectives (KPIs) and without a defined return on investment (ROI).
It is action for action’s sake, which at best ends up creating a tool that is of little use, and at worst wastes resources that could have been spent on real innovation.
Scale matters: Who is most feared and why?
Interestingly, AI FOMO syndrome has different facets depending on the size of the company.
Large corporations often fall victim to image pressure. Management, bombarded by headlines about the AI revolution, feels the need to act to show shareholders and the market that the company is modern.
This leads to ‘innovation theatre’ – projects that look great on slides in a presentation, but add no value in practice.
Medium-sized companies seem to be the voice of reason. Operating with limited resources, they cannot afford to experiment aimlessly.
Therefore, their investments in AI are much more often pragmatic and aimed at solving specific pain points – optimising logistics, automating customer service or streamlining production processes.
Small and micro companies are at the other extreme. For them, fear leads not to panic but to paralysis. Overwhelmed by costs, lack of expertise and the scale of the challenge, they often give up altogether, which in the long run can prove just as dangerous as their larger competitors burning through their budgets.
The hidden costs of haste: The risks no one is looking at
Haste and lack of strategy have another dark side – they lead to ignoring the fundamental risks of artificial intelligence. IT managers focus on problems that have been known for years, such as security and data management, while underestimating completely new risks:
- Bias: AI learns from the data provided. If the data reflects human stereotypes, the system will uncritically replicate and reinforce them, which can lead to discriminatory decisions in recruitment or credit assessment processes.
- “Hallucinations: Language models can generate information with absolute certainty that is completely false. Basing key business decisions on such ‘facts’ is a simple path to disaster.
- Ethical challenges: Who is responsible for an error made by an algorithm? How to ensure transparency in its operation? These are questions that companies need to find answers to.
These are no longer theoretical considerations. The aforementioned study shows that almost one in five companies (18%) have already had to stop or fundamentally modify an AI project precisely for ethical reasons.
This is hard evidence that ignoring these aspects generates real costs and risks.
From fear to strategy: How to board the AI train wisely?
Artificial intelligence is undoubtedly a transformative technology. However, the key to success is not the mere fact of having it, but how it is used.
Instead of succumbing to pressure and asking “Should we invest in AI?”, leaders should ask themselves a different, much more important question: “What is our biggest business problem and can AI help solve it?”.
Changing this perspective is the first step from reactive fear to proactive strategy. Instead of big, vague projects, it makes sense to start with small, well-defined implementations that solve a real problem and deliver measurable benefits.
It is crucial to think about ethics and risks from the outset, not when the crisis is already knocking at the door.
It’s not about being on the train to the future at all costs. It’s about knowing where you want to go on it. Otherwise it’s just a very expensive ride in circles.
