Studies have shown Canadian companies are more hesitant to adopt AI than companies in peer nations. While some of this can be attributed to financial caution, we’ve detected a deeper cause: a fear of failure that often becomes paralyzing.
The fear isn’t unfounded. AI projects can fail, but the cause isn’t typically due to the technology itself. Rather, it’s more often a rush toward implementation without first answering two fundamental questions: What are we trying to achieve? And how will we know if we’ve succeeded?
These two elements – establishing clear objectives, and measurable success criteria – can determine the difference between whether a project delivers transformative value, or ends up on the cutting room floor. And it makes sense: if you don’t know where you’re going, how will you know when you’ve arrived?
In this paper we focus on the second question – have we succeeded? – and show why this is not as difficult to answer as it may seem. But to be effective, the answers must be defined before the AI project even begins.