AI Tools Overview: ChatGPT, Claude, Gemini, and why that is the wrong question.
Yes, we give you the overview you expected. And then we explain why it is not what will actually move your business forward.
ChatGPT, Claude, Gemini. Here is what you wanted to know.
ChatGPT — The all-rounder. Best everyday assistant, strong at writing.
Claude — The analyst. Brilliant for large documents and precise code.
Gemini — The Google twin. Perfect if you already live in the Google ecosystem.
There you go. All three are good. All three get better every month. And none of them is the decisive question for your business.
Because AI projects do not fail because of the tool. They fail because nobody asked beforehand what the tool is actually supposed to solve.
The hype around the tools is bigger than the hype around real use cases.
ChatGPT was the first tool of its kind. Gemini could not keep up at first. Few people had heard of Claude, even though Anthropic was founded in 2021 by former OpenAI employees. Then suddenly a new model arrives that outperforms the previous one. Everyone talks about it. Nobody asks: for what exactly?
In practice we see this all the time. The focus is on the tool. The question of what we actually want to do with it comes later, or not at all.
Three situations that arise when the tool comes before the strategy.
We see these situations in almost every company that comes to us.
Every department has its own AI tool
Marketing uses ChatGPT, IT has built its own model, leadership wants Gemini. Shadow AI everywhere, no shared data foundation, costs nobody can track, and every department reinvents the wheel.
The result: Shadow AI projects everywhere, strategy nowhere.
AI gets rolled out but nobody uses it productively
The tool is there. The license is running. But nobody defined what it should be used for. So employees open it occasionally, ask something and close it again.
The result: AI as an expensive experiment instead of a real lever.
The project starts, then the data problem arrives
The model is selected, expectations are high, then someone looks at the data foundation. Unstructured, incomplete, no APIs to surrounding systems. The AI project becomes an unintended data project.
The result: Budget spent before the first automation goes live.
Buy the tool first. Then ask what to do with it.
It is the same pattern we see with CRM rollouts, ERP projects, every software introduction of the last twenty years. The tool gets evaluated, demonstrated, purchased. And then comes the question: how do we actually make this work?
With AI the problem is even more pronounced because the tools are so broadly applicable. A CRM has a clear purpose. ChatGPT can do almost anything, and that is exactly what makes it difficult. Those who do not define what they want end up with an expensive chat window that gets opened occasionally.
On top of that: there is no shortage of tools. There are open-source models you can self-host. There are specialised models for specific tasks. The choice is not the problem. The absence of a clear question is the problem.
Use cases first. Tool second.
We do not start with the tool question. We start with a simple question: what do you have to do manually over and over again today?
Is the goal to make company knowledge available to the team faster? Do you want to automate a process? Should emails be pre-sorted? Should a customer service bot handle recurring requests?
Depending on the answer, completely different approaches make sense. Sometimes it is ChatGPT. Sometimes a self-hosted open-source model. Sometimes a specialised tool. And sometimes the answer is: start with the simplest use case that can be implemented in a few hours, and get a first win.
Because that is the real currency in AI projects: a first concrete proof that it works. That builds trust, builds momentum, and makes the next step easier. It is not about building the perfect all-in-one solution overnight. It is about finding the first real lever.
Use case first. Tool second. Result in four weeks.
A company with 60 employees. Leadership wants to introduce AI. First instinct: which tool do we buy? We stop right there.
Instead we spend a week understanding where the most repetitive work sits in daily operations. Finding: the support team answers the same 40 questions by email every day, manually, each time slightly differently worded.
- Use case identified in three days
- Knowledge base built from existing documents
- AI assistant for support live after four weeks
- 40 recurring requests answered automatically
- Support team focuses on complex cases
- Tool costs: under 100 euros per month
“We spent months debating tools. In the end one question moved us forward: what annoys your team the most every day?”
What you usually ask us about AI tools.
WHICH AI TOOL YOU NEED, WE ONLY KNOW ONCE WE KNOW WHAT YOU WANT TO SOLVE.
Tell us briefly where the most manual work sits in your business, we will tell you in one conversation where the first lever is.