The success of Generative AI (ChatGPT and other LLM-powered systems), largely depends on the effective management of the vast knowledge they possess. Generative AI models can produce impressive and realistic outputs, but they do so by training on massive amounts of data. The enormous amount of knowledge they possess can be both a strength and a challenge.
It is important to effectively manage this knowledge to ensure that the models generate outputs that are not only realistic but also ethical, fair, and safe. Furthermore, continuous monitoring and updating of the models are necessary to ensure they remain up-to-date and accurate, as well as to prevent the models from generating biased or harmful outputs.
Our KM Assessment for AI with a PoC plan involves evaluating your organization's knowledge management practices related to artificial intelligence AI and developing a plan for a proof of concept to test the feasibility of implementing AI solutions. Earley takes it a step further by analyzing and organizing your business's current data management practices, identifying data gaps, and determining the potential impact of AI on the organization's knowledge management processes.
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Managing content — whether documents, transactional data or digital assets — is about providing content in context. Users can't find what they need for many reasons: (1) information and systems evolve and tend toward a disordered state; (2) in most organizations governance processes around asset management, search, taxonomy and metadata are immature; (3) content is not "selectively managed. In this interview we discuss a number of issues around content management, taxonomy, tagging, metadata and search, and provide some ideas on how to tackle the chaos to create business value.