eGain: Knowledge Automation Is Key to AI Success, Company States
eGain argues that many enterprise AI implementations fail due to poor knowledge management, not flawed technology. The company advocates for 'Knowledge Automation' as the crucial framework for AI effectiveness.
eGain Corporation has stated that the majority of enterprise AI customer service initiatives fail to deliver return on investment, attributing these shortcomings not to the sophistication of AI technology but to fundamental issues in knowledge management.
The company highlights that current approaches often point AI systems at existing knowledge bases designed for human agents, which are typically unsuited for AI. eGain's perspective is that AI requires knowledge that is atomized, contextually tagged, continuously validated, and governed for trust. Without this structured approach, even advanced AI struggles to provide accurate customer service.
eGain criticizes the common practice of spending 18-24 months "training" AI systems, which often involves extensive manual content cleanup and restructuring. The company suggests that major software vendors like Microsoft, ServiceNow, and Salesforce may profit from this prolonged training period through increased professional services revenue, while the core issue of knowledge operations remains unaddressed.
Introducing the concept of "Knowledge Automation," eGain defines it as treating knowledge as a product with its own development lifecycle, quality standards, and metrics. This discipline focuses on systematically capturing, synthesizing, and structuring information from disparate sources, ensuring accuracy and relevance for AI-driven customer interactions. This contrasts with traditional methods that rely on manual organization and may lead to fragmented or unreliable data for AI applications.