Common Mistakes in the Development of AI Assistants

How AI assistants are often developed for companies, why they fail and what we can learn from them.

The fault-finder will find faults even in paradise in the development of AI assistants.
(Henry David Thoreau ChatGPT)

How fortunate that people make mistakes: because we can learn from them and improve. We have closely observed how companies around the world have implemented AI assistants in recent months and have, unfortunately, often seen them fail. We would like to share with you how these failures occurred and what can be learned from them for future projects: So that AI assistants can be implemented more successfully in the future!

How AI assistants have been developed in companies so far

A Story in 5 acts: Pitfalls in AI assistant development

What were the pitfalls that caused the team to fail?

At this point, we have good news: The mistakes that many teams make when developing AI assistants, as described here, can be traced back to three frequently incorrect assumptions:

3 common, incorrect assumptions in AI assistant development

False Assumption 1

"Publicly available materials and articles tell the full truth about how cutting-edge AI systems actually work in business."

False Assumption 2

"If we break our documents into small pieces and then insert them into a vector database, the AI will be able to create meaning from them as if by magic."

False Assumption 3

"We need to build an all-powerful AI assistant with complex architecture to bring real value to the business."

What concrete measures can be derived to successfully implement AI assistants?

Discover our tried-and-tested tips & tricks to help you avoid common pitfalls during implementation and build good AI assistants for companies cost-effectively.


Would you like to use KI Assistants in your company?

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Christoph Hasenzagl

Christoph.hasenzagl@timetoact.at


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