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The continual desire to increase automation of repetitive tasks and to glean new insights from large, often diverse sets of information has fueled a surge of development around artificial intelligence (AI) across industries and disciplines. As a result, savvy marketeers have taken this opportunity to label practically any decision made by machines as “AI,” making it difficult for business professionals to discern the difference between true AI innovation and clever re-labeling of existing capabilities. In addition, many pseudo-AI solutions require massive investments of time and money behind the scenes to simulate an AI-like experience. Given these factors, it’s no wonder “AI” has become too generalized and is often confusing for those seeking solutions to real business problems.
Dictionary.com defines intelligence as “the ability to acquire and apply knowledge and skills.” Inherent in this definition are several criteria that should be considered when determining the degree to which any given solution is truly artificial intelligence versus plain machine automation. In the Learning & Development (L&D) space specifically, true AI for Learning needs to assess learners and training content virtually on its own. It should then be able to adapt the presentation of content according to that knowledge, not just navigate a pre-defined, static pathing structure. Finally, AI for Learning should deliver this personalization autonomously i.e. it shouldn’t require massive investments of human time to deliver this personalized learning experience. Instead of customers doing significant extra work to provide an AI-like experience, true AI should do the work for them.
Download our whitepaper for insights from over 15 industry reports and further information about leveraging AI to:
- improve training efficacy
- optimize training content
- personalize learner experience
- measure learner engagement
- analyze and predict outcomes based upon learner behavior
- model social learning
- and earn L&D teams a seat at the executive table