Knowledge management (KM) and artificial intelligence (AI) have both gone through booms and busts—periods of hype followed by a sobering dose of reality. After an “AI winter,” AI is currently enjoying an “AI spring,” because of a range of new applications driven by availability of training data, progress in algorithm performance, computing power and new funding. There is also a growing understanding that cognitive applications of AI are trained in much the same way as humans. The two work hand in hand. Therefore, the same resources can be applied to preparing that information for AI that will solve problems even if AI is not your primary objective. AI training content for cognitive systems such as knowledge retrieval bots, semantic search, intelligent virtual assistants, etc., should be designed to be reusable across multiple systems and platforms.
A well-integrated knowledge engineering approach solves immediate knowledge access by humans, while laying the foundation for an AI-powered future. Organizations will compete on their knowledge about customers, products, solutions, and technologies embodied in AI tools and systems. Making the investment in the foundation for knowledge management will pay off in the short term, as well as prepare your organization for the future.
Join us to explore these topics and more
- The role of KM in AI
- Understanding Knowledge Engineering (KE) and how it is different from Knowledge Management (KM)
- Ways to design training data and content for both humans and AI
- Approaches for targeting processes that will provide the clearest ROI