You may have noticed that I only referred to AI, specifically generative AI, in paragraph three, even though it appears the law states that all columns written in 2023 must begin by this subject. But I couldn’t avoid it forever, because generative AI is at the peak of its hype cycle and dominating the technological landscape. Furthermore, it is a technological advancement that has a direct impact on the world of knowledge management. Technology vendors selling into the KM market are almost rabid in their enthusiasm to embrace generative AI and, overall, it’s fair to say, with good reason. But to use an expression from my native country, it’s “horses for courses.” In other words, what works well in one place may not work as well in another.
A 100% accuracy rate is a laudable goal for knowledge management, but it is not realistic. Experts who manually curate specialized knowledge-the base will make mistakes, not intentionally, but it is not and will never be possible to validate every data point in the information sources. Generative AI cannot and will never provide 100% accurate results. Likewise, it is simply not possible to validate every data point before processing it or every output before passing it to the user. Errors will happen; that’s how it happens. But the severity and weight of these errors vary enormously.