Many pundits, academics, and economists advise business executives on how artificial intelligence (AI) will augment human performance in the workplace. Some conclude that human-machine interactions will involve machines providing scale and speed with humans offering insights and training data.

Despite its broad appeal, the assessment that human-machine interactions are, and will continue to be, exclusively about augmenting humans or teams of humans and machines is shortsighted and underestimates the transformative potential of AI.

Some machines are already beginning to learn in virtualized (at least partially) environments with neither human training nor data input from the real world. This process, known as hyperlearning, allows systems to learn at machine speed and develop novel solutions in specific settings, frequently involving unsupervised learning and reinforcement learning algorithms. Often these systems use adversarial or complementary AI engines that play off against each other, generating virtual training data in the process. Companies in different industries are already creating the environment for such hyperlearning systems, raising the question: What should executives expect from human-machine interactions in the coming years?