eGain Reviews History of AI and Knowledge Management

The evolution of artificial intelligence (AI) and knowledge management (KM) has seen recurring cycles where substantial investments and ambitious visions have encountered the realities of implementation challenges. In a blog post, eGain Corporation analyzes historical lessons pertinent to the current AI boom.

During the 1980s, corporations invested heavily in AI, particularly expert systems designed to capture and scale specialized knowledge. Notable examples include GE's DELTA system for locomotive diagnostics and Digital Equipment Corporation's XCON for configuring computer systems. Case-Based Reasoning (CBR) also emerged as a method for solving new problems by adapting solutions from similar past cases.

Later, in the late 1980s and 1990s, Knowledge Management (KM) emerged with broader goals to capture all organizational knowledge, encompassing documents, processes, and tacit knowledge. The objective was to enhance knowledge sharing and utilization within enterprises. Platforms were developed for knowledge repositories and collaboration, alongside an emphasis on cultural shifts to foster knowledge-sharing environments.

Despite these ambitions, many AI and KM initiatives faced hurdles. Technical limitations, such as the brittleness of expert systems and retrieval challenges in CBR systems, coupled with exorbitant development costs and difficulties in proving return on investment (ROI), impeded progress. Overly optimistic sales pitches also led to disillusionment when systems failed to deliver transformative outcomes. Within KM, tacit knowledge proved particularly difficult to capture, and knowledge updates and search efficiency remained inadequate.

These historical experiences provide valuable insights for the ongoing AI development. eGain Corporation suggests that understanding both the promises and the implementation challenges of past technologies is crucial to avoid similar pitfalls and to build sustainable, value-generating solutions.