eGain: Knowledge Serves as Instruction for AI Systems
Companies that recognize this and build the necessary engineering discipline will own the decade, the firm asserts.
eGain Corporation is highlighting the growing trend of enterprise AI strategies being built on robust knowledge foundations. The company posits that organizations which understand the critical role of knowledge as instruction for AI – enabling it to guide AI models – and invest in the corresponding technical discipline, will lead the next decade.
The significance of knowledge management platforms is becoming paramount as businesses aim to develop reliable AI solutions suitable for production environments. For instance, the CIO of a major health insurer indicated that their enterprise architecture relies on two core platforms: a cloud-based data lakehouse and a knowledge platform that aggregates, curates, and governs the information used by their AI systems.
Historically, software development has maintained a distinct separation between code (instructions) and data. Code has been rigorously developed and monitored, while data was treated more as raw material. In the era of generative AI, this dividing line is blurring. Language models increasingly operate based on the knowledge they retrieve and reason over to generate responses. Consequently, knowledge has effectively become an instruction layer, akin to code, dictating the system's actions.
eGain emphasizes that the quality of the knowledge base directly impacts the accuracy of AI model outputs. Research indicates that simply incorporating high-quality, curated knowledge can significantly improve AI model response accuracy without requiring model retraining. This underscores the necessity of investing in engineering discipline within knowledge management to ensure AI systems perform reliably in practical applications.