AI vendors and enterprise buyers struggle to get on the same page about the return on investment (ROI) of AI solutions.
Misinformed or uneducated buyers look for quick turn-around and measurable results within an unrealistic, bounded timeline.
Eager vendors are often quick to agree to a buyer's unrealistic expectations if it means winning a pilot deal - but this eagerness often turns to regret as overpromised results ruin the client relationship, and prevent the project from becoming a real deployment.
Smart AI vendors (i.e. those with talented data science staff and real experience implementing AI with enterprise clients) know that this dynamic is bound to lead to frustration and failed projects.
In this article, we'll discuss some of the challenges to measuring AI's ROI, and we'll present our Trinity model for estimating and presenting AI ROI to leadership. We'll also link to a variety of related Emerj articles that explore the themes of Measurable, Strategic, and Capabil...
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