When enterprise document and content management systems emerged, the content these systems helped organize was expected to help in improving and automating processes. Similarly, technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Chatbots were projected to drive digital transformation by utilizing enterprise data. On the contrary, what is happening today is that these new technologies are exposing challenges that should have been overcome years ago.
The challenge, according to Lisa Trager, President of Content in Motion, is in developing content strategies for everything including better digital and customer experiences. Simply put, everything boils down to content, understanding what is content strategy, and having dedicated people for content operations. Furthermore, putting money into AI, ML, and Chatbots and expecting them to deliver enriched user experience, without first investing in taxonomy, could turn out to be a futile exercise.
Lack of taxonomy and a cohesive content strategy is indeed crippling the effectiveness of AI in overcoming the challenge of bringing content into processes.
Seth Earley, CEO of Earley Information Science, perceives Chatbots, a manifestation of AI, as a channel to information and knowledge, and as a retrieval mechanism similar to search. Moreover, constraints to the effectiveness of Chatbots echo those of search namely poorly curated and structured content.
The fact that search and Chatbots face the same set of challenges makes clear that they are part of the information retrievability continuum, and it is imperative that both must be fixed. To put things right, enterprises should devise a framework—knowledge scaffolding—for structuring knowledge scattered across an enterprise. In addition, to harness the many aspirational capabilities of AI, enterprises should organize information; understand the user and their processes and above all, their content. Subsequently, technology can be used to enhance and automate processes.
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