In the Harvard Business Review article, AI Won’t Change Companies Without Great UX, Michael Schrage asked the question, “As artificial intelligence algorithms infiltrate the enterprise, organizational learning matters as much as machine learning. How should smart management teams maximize the economic value of smarter systems?”:
“Strategically speaking, a brilliant data-driven algorithm typically matters less than thoughtful UX design.”
Thoughtful UX designs can better train machine learning systems to become even smarter. The most effective data scientists learn from use-case and UX-driven insights. For example, a data scientists may discover that users of one of their smart systems informally use a dataset to help prioritize customer responses. That unexpected use case may lead to retraining the original algorithm.
Focusing on clearer, cleaner use cases means better and more productive relationships between AI and its humans. The division of labor becomes a source of design inspiration and exploration. The quest for better outcomes shifts from training smarter algorithms to figuring out how the use case should evolve. That drives machine learning and organizational learning alike.
Business process redesign and better training are important, but better use cases – those real-world tasks and interactions that determine everyday business outcomes – offer the biggest payoffs. Focus on the actual job that needs to get done then develop use cases that inform your models.