In a piece published in the Harvard Business Review, Rotman School’s Ajay Agrawal, Joshua Gans, Jeffrey Skoll and Avi Goldfarb wrote that poor data can lead to errors. A lack of human judgment in deployment can result in strategic failures, especially in high-stakes situations.

The article explains that thinking of computers as arithmetic machines is more important than most people intuitively grasp because that understanding is fundamental to using computers effectively, whether for work or entertainment.

“While video game players and photographers may not think about their computer as an arithmetic machine, successfully using a (pre-AI) computer requires an understanding that it strictly follows instructions. Imprecise instructions lead to incorrect results. Playing and winning at early computer games required an understanding of the game's underlying logic."

The authors suggest that AI's evolution mirrors this trajectory. Early applications focused on well-established prediction tasks. Recently, AI has reframed many applications as predictions, from predicting loan defaults and machine breakdowns to functions such as writing and drawing.

The report said that quality data and human insight remain crucial in successfully deploying these advanced tools, which businesses must be aware of before deploying.