– and want to catapult themselves into the rocket age
“We want to use AI now, too!” – a sentence that is currently booming in many companies. The hope: increase efficiency, overtake the competition, unlock new business models.
But often the whole thing feels like trying to launch a rocket from a horse-drawn cart. What comes out of it? Disappointment, frustration, wasted resources—and, in the worst case, a complete failure of the AI initiative.
As the saying goes, “hope dies last”. But for those who do not invest as a mature AI company, the wrong AI initiative can indeed become the final nail in the coffin—especially in these challenging times.
In this blog post, I show why artificial intelligence does not work when the foundation is missing—and what companies should do to avoid these mistakes.
Digitalization is not a buzzword—it is the foundation
Before AI can create real added value in a company, it needs a solid digital base. Yet many companies still operate internally as if they were in the analog age:
- Processes are paper-based or only “semi-digital” (e.g., uncontrolled Excel sprawl)
- Data is scattered across silos—unstructured and outdated
- IT infrastructure is outdated, not very scalable, or barely integrated
- The company is full of isolated solutions, and no one really has the full picture
- Employees are digitally overwhelmed or not trained at all
Conclusion: If you have not mastered the basics, you cannot successfully introduce complex systems. AI thrives on data, speed, and automation—and that is exactly what is missing far too often. If the internal know-how of the people is also lacking, you quickly end up fighting a battle you cannot win.
AI is not a magic wand—it needs data quality and clear processes
AI does not work simply because you “buy a solution”. It only works if you:
- have good data (current, structured, available)
- know clear processes (so you can automate or improve them)
- pursue a clear goal (e.g., reduce costs, improve customer service, make predictions)
- build up in-house AI knowledge
- make it part of an overall strategy
Without a clean data foundation, even the best AI model cannot learn anything. “Garbage in, garbage out” applies more than ever.
And if you are not able to recognize whether the delivered results are garbage or not, you have a real problem.
How often does ChatGPT, for example, deliver incorrect results that the recipient does not even recognize as such because they neither know the data basis nor have the “human intelligence” to verify the result.
You simply “believe” the result because it comes from an “intelligence”!
See also my blog post: AI worship at an all-time high
Countless possibilities—how is a digital Neanderthal supposed to find the right AI goals?
The biggest dilemma: AI can do almost anything—but what of it is actually useful?
Companies with low digital maturity, in particular, are practically overwhelmed by the variety of AI tools, use cases, and promises.
Whether chatbots, image recognition, predictive maintenance, text generation, forecasting, automation, or decision support—where do you start?
Making the right choice is important!
Reality
A digital Neanderthal—i.e., a company without a clear data strategy, with patchy processes and a poor system landscape—cannot answer this question in a well-founded way.
Because without transparency about your own processes, without KPIs and digital visibility, any AI strategy remains a blind flight.
Typical consequence: You copy some hype examples from other industries—without checking whether they fit your own maturity level or business model.
Better:
- First, create digital transparency
- Improve data quality and processes
- Then specifically look for problems that AI can solve
- And only then decide on specific AI applications
Because if you do not know where you stand, you cannot plan a sensible route to your goal—certainly not with a rocket.
Digital maturity must grow before AI is used
Digital maturity means:
- Processes are digitized and efficient
- Systems are integrated and scalable
- Data flows freely and in a structured way through the company
- Employees understand the systems, actively work with them, and continue to develop them
If you do not have this maturity, you will not ignite a rocket with AI—at best, you will create a small spark.
Start with a small rocket first
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Where did all these millions of AI experts suddenly come from?
Since the boom around ChatGPT, Midjourney & Co., it seems to be swarming with “AI experts”. Hardly a LinkedIn post without “AI”, hardly a consulting offer without the promise: “We’ll make your business AI-ready!”
But: Where did they all suddenly come from?
And: Are they really experts—or just the one-eyed among the blind?
The truth is:
Many of these new “AI consultants” were building websites or doing social media a year ago—now they are explaining to companies how neural networks work or how to integrate AI into business processes.
The problem with that:
- They often do not recognize whether a company is digitally mature enough for AI
- They confuse tool knowledge with strategic competence
- They sell quick solutions—without sustainable integration
Of course, there are excellent AI experts with real technical and strategic depth. But especially in the current hype phase, it is extremely important for companies to distinguish between marketing talk and real expertise.
Tip: Ask your “AI expert” these questions:
- What do our current digital weaknesses look like?
- Which processes are actually AI-capable for us?
- How do we integrate AI into our existing data and IT landscape?
- What does this mean in the long term for the organization, culture, skills, and the people in the company?
If there are no concrete answers—move on. Because the damage from bad advice is often greater than the benefit of well-intentioned isolated solutions.
Unfortunately, there are far too many “wannabe AI experts”
The risk: Failed AI projects undermine trust
A premature AI attempt without a digital foundation can be expensive—financially, but also culturally:
- Employees doubt whether innovations make sense
- Leaders lose trust in future technologies
- Digital transformation is labeled as failed
- The result: Companies fall behind—technologically and economically.
The solution: Step by step to success
Before AI projects are launched, the company should honestly ask itself:
- Have we fully digitized our processes?
- Can we access clean, structured data?
- Is there a digital mindset in the company?
- Do we have concrete use cases for AI—and do we really understand them?
If these questions can be answered with “Yes”, then AI is no longer a rocket launch—it is simply the next logical step.
If you are unsure and need support from a genuine digitalization expert, you are welcome to book a free initial appointment with me here via the link.
Conclusion
Artificial intelligence is a powerful lever—but only if you use it on a solid digital foundation.
If you are still living in the digital Stone Age, you should not dream of AI first, but seriously take the path to get there.
First digitalization. Then automation. Then artificial intelligence.
That is how the journey out of the digital Stone Age truly becomes a rocket launch.
Related blog post: Digitize, Automate, or Die
Image sources: Shutterstock
