Knowledge Gaps Can Undermine AI, Says eGain
eGain Corporation identifies knowledge issues, not model flaws, as the primary cause of AI unreliability. The company emphasizes the necessity of a trusted knowledge foundation for AI success.
Many organizations face AI reliability issues not because of flawed models, but due to poor or outdated knowledge bases, according to eGain Corporation. The company states that AI systems built on incorrect assumptions rather than trustable knowledge foundations can lead to significant problems.
These issues often surface once AI is integrated into daily operations. Inaccurate answers, conflicting guidance, or recommendations that do not align with current practices can erode user confidence. While individually minor, these discrepancies accumulate, increasing operational costs and reducing overall efficiency.
Efforts to diagnose AI unreliability frequently focus on models or data, overlooking the underlying knowledge. This knowledge is often fragmented, outdated, or siloed within personnel. Without clean, structured content, AI struggles to differentiate current information from legacy processes, resulting in confidently incorrect outputs and flawed advice.
eGain advocates for a modern approach to knowledge management, defining it as an ongoing process to ensure guidance is clear, current, and trustworthy. Successful AI implementations, according to the company, begin by analyzing customer and employee inquiries. This data reveals the most critical content and questions, forming the basis for a coherent and unified knowledge base that accurately reflects organizational practices and user needs.