{"id":22458,"date":"2024-11-18T15:00:11","date_gmt":"2024-11-18T20:00:11","guid":{"rendered":"https:\/\/enterprise-knowledge.com\/?p=22458"},"modified":"2025-11-03T16:31:46","modified_gmt":"2025-11-03T21:31:46","slug":"from-artificial-intelligence-to-knowledge-intelligence","status":"publish","type":"post","link":"https:\/\/enterprise-knowledge.com\/from-artificial-intelligence-to-knowledge-intelligence\/","title":{"rendered":"From Artificial Intelligence to Knowledge Intelligence"},"content":{"rendered":"

Much of the dialogue in our industry has been dominated by Artificial Intelligence (AI) and what that means to the fields of Knowledge Management, Data Management, and Content Management. At EK, we\u2019ve been witnessing firsthand the impact of the \u201cAI Revolution,\u201d with markedly greater inquiries from organizations seeking AI services or software advice, as well as clients experimenting with what the market currently has to offer.<\/span><\/p>\n

The buzz is clearly around generative AI, with tools like ChatGPT, DALL-E, and Microsoft Copilot inspiring a great deal of interest and enthusiasm. We\u2019re playing with these tools, and many others, and they demo spectacularly and are a lot of fun to experiment with. Though extremely promising, however, there\u2019s still a major gap between a good demo and a tool that can be trusted at the enterprise level to deliver not just the right answers, but complete answers that fully leverage the knowledge and expertise of the organization and its people.<\/span><\/p>\n

Where generative AI tools fall short is in their prevalence for hallucinations (as in, a rather gentle word for errors). Imagine a situation where the AI of a medical supply company recommends the wrong product, where an airline offers a steeply discounted ticket, or where a leading services firm\u2019s chatbot insults a potential customer. If you\u2019ve followed the news, you know each of these issues (and many more) have already occurred. The risk for errors is steep, and with those errors can come unacceptable risks that can lead to loss of revenues, regulatory fines, brand and reputation issues, or even loss of life.\u00a0<\/span><\/p>\n

Moreover, the current slew of tools are capable of understanding structured content and unstructured content increasingly well, but struggle to deal with conflicting inputs, and outright fail to incorporate an organization\u2019s most valuable assets, its tacit knowledge and human expertise. This results in the classic issues of \u201cgarbage in, garbage out,\u201d and worse yet, fails to connect people and their knowledge in any actionable form.<\/span><\/p>\n

To reframe the conversation, we\u2019ve been asking our clients to consider what they\u2019re really seeking to get out of AI. We find that most organizations are seeking the promise of AI, but require the reliability and trustworthiness of more conventional knowledge assets, and moreover are seeking to incorporate human intelligence into their solutions in a way that captures all of an organization\u2019s knowledge assets, rather than leaving key elements like people, their experiences, and their expertise out of the equation. To that end, we discuss Knowledge Intelligence (KI), rather than AI.<\/span><\/p>\n

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KI integrates institutional knowledge, business context, and expertise<\/b><\/a> to enhance AI capabilities through effective knowledge and data management practices. Put simply, it smashes together the latest in AI technologies with all of an organization\u2019s knowledge assets, delivering on the promises of AI with greater reliability and organizational expertise.<\/span><\/p>\n

The specific components of KI address the following critical gaps for AI:<\/span><\/p>\n