Franco T.,
Too Long; Didn't Read
AI projects do not fail because of the technology. They fail because of the wrong question. "How do we implement AI?" leads nowhere. "Which business problem are we solving?" leads to real ROI. Most companies invest in tools, not in strategy. That is expensive.
The Scene That Keeps Repeating
The CEO comes out of a conference. Or has read an article. Or has heard that a competitor is "doing something with AI."
"We urgently need AI."
Budget is approved. A team is assembled. Nine months later: There is a model lying around that predicts something. But no one uses it.
Why? Because the prediction is not valuable to anyone. Or because the quality is not good enough. Or because there was no plan for how people were actually supposed to use the thing.
(Does that sound exaggerated? It isn’t. We see this every few months.)
The Uncomfortable Statistic
Around 70% of company-wide AI initiatives fail to deliver the expected business value.
Not because the models are bad. Not because the data scientists are incapable. But because companies start with the wrong question.
"How do we use AI?"
That is the wrong question.
The right question: "What specific business problem can we solve with it?"
That sounds trivial. But it isn’t. The first question leads to a bottom-up approach: You gather tech experts, and they look for problems for their solution. That is backwards.
The second question leads to a top-down approach: You ask where it hurts, where costs arise, where customers are frustrated. And then you assess whether AI can help.
The Five Reasons Why AI Projects Fail
1. Hype Instead of Strategy
The pressure to "not be left behind" leads to investments without a clear objective. "We need to implement generative AI" is not a strategy. That is panic.
2. Data Is Not What Companies Think It Is
"We have so much data!" Yes. But having data in systems is not the same as having clean, structured data that is usable for machine learning.
Most companies massively underestimate the effort required for data preparation. It is not sexy. But it is crucial.
3. No Business Ownership
Many AI projects are driven by IT. That is a classic mistake. If the business owner is not behind it—if the person who is supposed to use the result is not actively involved—then you are building a solution no one wants.
4. Change Management as an Afterthought
Building a model is the easy part. Getting people to use it and adapt their processes—that is the hard part.
We have seen highly accurate predictive models that no one uses. Because users were not trained. Because integration into their workflows is painful. Because no one explained why it should be valuable.
5. ROI Is Not Defined
In the end, someone asks: "Was this successful?" And no one knows. Because what "successful" means was not defined in advance.
Was it time savings? Revenue growth? Error reduction? If that is not clear, you cannot measure anything. And the next manager says: "The project was expensive and delivered nothing."
Our article The AI Readiness Check: Is Your Company Ready for AI? offers a deeper look.
What AI Strategy Really Means
AI strategy is not "We are implementing AI." That is like saying "Our strategy is to use software." It says nothing.
AI strategy means:
A clear objective. Not "we want to use AI," but "we reduce the average response time in customer service from 24 hours to 2 hours through automatic ticket classification."
Realistic expectations. ChatGPT is impressive. But it hallucinates. It sometimes does not understand nuanced business logic. It may not work on your specific data.
Honest readiness assessment. Do you have the data? Do you have the skills? Do you have the infrastructure? These are not theoretical questions—they are practical blockers.
Clear metrics. Before you start: What does success mean? How do you measure it? When do you know whether it worked?
Generative AI: The Reality Behind the Hype
Generative AI (ChatGPT, Claude, etc.) is not a miracle cure. It is a powerful tool for specific use cases.
Where it works well:
- Summaries of long documents
- Content generation (with human review)
- Q&A based on knowledge bases
- Code generation (the code must be reviewed)
- Classification and extraction
Where it works poorly:
- Financial or critical decisions where accuracy is crucial
- Highly specialized domains where the model might hallucinate
- Privacy-critical use cases if data protection is not ensured
What most people underestimate:
Hallucinations are real. Generative models can present invented facts as if they were true. This is not a minor issue.
Data privacy is complex. If you use ChatGPT directly (not the Enterprise API), your data may potentially be used for training. That is a GDPR issue.
Costs scale. "Generative AI is free" is a myth. At one million API calls per month, that quickly reaches five figures.
The Pragmatic Path
If you want to approach AI seriously:
1. Start with the problem, not the technology
What are the three biggest pain points in your company? Where do the highest costs arise? Where do customers lose time? Where are error rates high?
Only when you know that do you ask: Can AI help here?
2. Assess your readiness honestly
Do you have the data? Is it clean? Do you have someone who can drive this? Is the business behind it?
If the answer to several of these questions is "No"—then start there, not with the AI project.
3. Start small
Not "we are transforming everything with AI." Instead: One pilot. One problem. Clear metrics. If it works, scale. If not, learn.
4. Define success beforehand
What does it mean for the project to be successful? How do you measure it? Write it down before you start.
5. Take governance seriously
The EU AI Act is not optional. If your model operates in sensitive areas—recruiting, lending, medicine—then there are regulatory requirements. Ignoring them is expensive.
What You Can Do
List the three biggest business problems - not IT problems, business problems. Where are you losing money? Where are customers frustrated? Where do errors occur?
Ask yourself for each one: Could AI help here? Be honest—sometimes a simple rule-based system is better than machine learning.
Assess your data situation - If you wanted to train a model tomorrow, could you give the data to a data scientist? Is it clean? Structured?
Talk to the business - Not just IT. Would someone from the business unit use the result? Is there genuine interest there?
The Point
AI is not a magical thing. It is a tool. A very powerful one, but still a tool.
AI creates value when you solve the right problems. When you are realistic about possibilities and limits. When your data is in order. When the business is behind it.
The companies that succeed with AI do not have the best technology. They have the clearest goals. They know what they want to achieve. They measure whether it works.
That is not sexy. That is not hype. But it works.
You want to approach AI strategically—not as a hype project? We help Swiss companies identify the right problems and achieve real ROI. Talk to us.
