Data quality challenges impact AI effectiveness in business

Joakim Jannerfeldt, Vice President and Head of Columbus EIM, warns that artificial intelligence is only as effective as the information used to train it. Many companies risk reinforcing existing organizational problems by training AI on unstructured or fragmented data spread across various business systems, emails, and local folders.

When information is incomplete, contradictory, or difficult to locate, it becomes harder to trust AI-driven analyses and decision support. Jannerfeldt notes that if AI is to provide relevant answers, it must be based on correct, coherent, and easily accessible information to avoid automating incorrect decisions.

However, the need for a strong information foundation should not prevent companies from adopting AI. Instead, the technology can help organizations identify areas with the greatest potential for development. As AI becomes integrated into daily workflows, it becomes clearer which processes require improvement and how information must be structured to maximize business value.