AI systems are smart, but they do not learn after they are deployed. The “learning” part of an AI system happens during training. Once trained, the model is static until you retrain. Retraining can be expensive, time-consuming, and difficult because of the need for new, labeled data.
Many Artificial Intelligence initiatives are doomed from the start – but needn’t be. Here are five things that keep AI projects from achieving success. 1. No Strong Center A successful AI project requires a well-defined center and clear goals. Why are you doing AI? What is the vision for the future? The inability to answer
While hiring smart people and providing a sandbox environment seems an obvious place to begin, real-world AI initiatives gain little from sandbox innovation. An effective AI initiative needs top-down vision, strategy, and tempo. Decide what elements of AI define the future of your industry and link those to strategic capabilities your company will need to
