Data quality challenges impact corporate AI implementation

Joakim Jannerfeldt, Vice President & Head of Columbus EIM, warns that artificial intelligence is only as effective as the information used to train it. Many companies investing heavily in AI risk reinforcing existing organizational problems if they train models on unstructured or incorrect data.

When documents, data, and processes lack structure—often scattered across various business systems, email, and local folders—it creates risks of duplicate work and unreliable decision-making. For AI to provide relevant and useful results, it must be based on information that is correct, coherent, and easily accessible. Without this foundation, companies risk automating incorrect decisions rather than achieving intended efficiencies.

Despite these challenges, Jannerfeldt suggests that the need for a strong information foundation should not prevent companies from starting their AI journey. Instead, implementing AI can help organizations identify where there is the greatest potential for operational development and highlight which processes require better structuring to create business value.