Why AI projects often fail - tips on how to do it better
Up to 80 percent of AI projects fail. This is not due to the technology - but to the implementation. Here are a few practical tips on how to overcome typical stumbling blocks in AI projects.
Generative AI gives companies the opportunity to make email processing more efficient, increase customer satisfaction, and reduce the workload of employees.
Large language models (LLMs) are currently outdoing each other not only through better performance, but also through ever larger so-called context windows. But what does that actually mean?
Technological developments are happening one after the other, and this also applies to the further development of LLMs. What used to be text-based language models are now omni-modal.
Comprehensive testing is crucial for companies to be able to use large language models (LLMs) safely and effectively. This is because models that are not thoroughly tested can deliver incorrect or biased results.
One urgent security issue at the moment is the implementation of the EU AI Act - and therefore the protection of personal data through anonymization and pseudonymization. Tip: There is technical support for this.
Why AI projects often fail - tips on how to do it better
Up to 80 percent of AI projects fail. This is not due to the technology - but to the implementation. Here are a few practical tips on how to overcome typical stumbling blocks in AI projects.
Generative AI gives companies the opportunity to make email processing more efficient, increase customer satisfaction, and reduce the workload of employees.