Why AI is failing across the board despite the hype – and what you need to change now
Artificial intelligence has arrived in the boardroom. According to a current study by the Thomson Reuters Institute 62% of top managers see AI as a game changer for their company. 82% of C-level executives in eight countries surveyed even name digital transformation as their top priority – ahead of increasing sales or reducing costs.
So far, so ambitious.
But the reality shock follows on its heels: only 12% of employees actually use AI in their day-to-day work. There is a massive gap between vision and reality – the FAZ calls it the Large AI implementation gap.
The great AI implementation disasters
The implementation gap describes the discrepancy between strategic intent and actual use. While AI is celebrated as a savior at management level, it fails in day-to-day business due to a lack of integration, insufficient training and overburdened teams.
AI may be thought, but not lived. The result: opportunities remain unused, investments fizzle out – and employees turn away.
Causes of the implementation gap
1.// Strategies without a connection to operational reality
Visionary AI roadmaps are worthless if they are not linked to the actual workflow of the teams. Strategies are often developed in an ivory tower – far removed from the processes in which AI is supposed to work. The result: AI remains theoretical.
Our tip: Integrate specialist departments into the design process at an early stage. Only those who understand, where AI can help in concrete termswill also use them sensibly. Use our tried and tested formats such as the AI assessment or the Fit for AI Workshopto build precisely this bridge.
2.// Lack of empowerment of the workforce
One point that is often overlooked is that employees simply do not know how AI works – or how it can be used in everyday working life. The technologies are introduced “top-down”, without any accompanying training or meaningful introduction.
Our tip: Create a culture of curiosity instead of fear. Offer training courses that Practical and understandable are. Showcase use cases that bring real added value – for example through intelligent document analysis, AI-supported tenders or the automation of repetitive tasks.
You can also find insights into this at the FAZ Conference on Artificial Intelligence.
3. // Missing tools and interfaces
Many companies rely on individual solutions – without an overarching platform or data strategy. The result: silos, redundant data flows and a patchwork of tools that do not work together.
Our tip: Rely on a holistic AI platformthat networks processes, data and people. Technologies such as Knowledge Graphs and ontologies enable a comprehensive semantics and data structure – making knowledge usable across the organization.
More background information FAZ Pro – Digital economy.
4. // No room for experimentation
Fear of making mistakes blocks innovation. Many teams have neither the time nor the resources to experiment with AI. Innovations arise where tried out, discarded and rethought and rethought.
Our tip: Establish internal AI labs or pilot zones where teams can carry out low-risk testing. Iterative projects with clear goals create trust and measurable results – in the spirit of: Think big, start small, scale fast.
Conclusion: From vision to implementation
The figures clearly show that the potential of AI has been recognized – but the decisive step is still missing. The Large implementation gap is not a technological, but a cultural and cultural and organizational challenge.
If you want to implement AI successfully, you have to from buzzword to lived practice with people at the center. This means synchronizing strategy and operational reality, empowering employees, networking technologies and promoting a culture of trial and error.
Our experience from over 60 AI projects in the SME sector shows: If this gap is closed, productivity gains of more than over 60 % are not uncommon.
