Several factors contribute to this high failure rate, including poor data quality, lack of relevant data, and insufficient understanding of AI’s capabilities and requirements.
Let's take a look:
A Lack of Data in a Time of Data Overload?
In the age of unparalleled data volume, velocity and variety, it seems unlikely that a “lack of data” would be the reason for AI projects to fail. But it’s true.
Data observability refers to the ability to monitor and understand the state of data systems. It involves tracking data quality, lineage, and performance across data pipelines.
Organizations can ensure the success of an AI project by monitoring the data’s freshness, volume, distribution, schema, and lineage.
Many organizations enter AI projects with inflated expectations, believing AI can solve complex problems with minimal effort or that it's a magic bullet for every business challenge.
Unclear Goals and Objectives
Projects often lack clearly defined goals and measurable outcomes, leading to a lack of focus and direction.