In AI, There Are No Magical Unicorns

It takes a village to get artificial intelligence projects to the finish line. Prepare accordingly

The Essentials

The data scientist and the manager/translator are key players in successful AI initiatives, says  Stephen Thomas, an adjunct faculty member at Smith School of Business, Queen’s University. Thomas is director of the Smith Master of Management Analytics and Smith Master of Management in Artificial Intelligence  programs. In this video, he discusses what organizations can do to avoid being swamped by disruption caused by artificial intelligence.

Video Highlights

0:18     Teams working on AI-based initiatives require members with varying skills, from technical architects and data engineers to machine learning engineers and product managers.

0:38    The manager acts as the crucial lynchpin. She works closely with the client and business team to understand needs and then translates those needs into terms that the technical team can act upon. “Without this manager to translate, we’d have a technical team and an executive team talking past each other. They don’t speak the same language.”

1:38    While it is tempting to rely on data scientists to carry most, if not all, of the AI load, these projects are too complex and multi-faceted. “A data scientist is not a magical unicorn that can do this all on their own.”

2:27     Managers who are denying the importance of AI “will put themselves and their companies at a huge disadvantage. Companies that are embracing this, training themselves, building these teams, will be the leaders, and they will put the other ones out of business.”