The article discusses the critical challenge of trust in AI as enterprises grapple with the seemingly opaque nature of AI-driven decisions. According to theCUBE Research and McKinsey, lack of trust hampers deployments in about 60% of businesses. Scott Hebner and Marc Le Maitre highlight the need for transparency in AI, especially with traditional AI frameworks being perceived as 'black boxes.' Le Maitre introduces causal AI as a solution to enhance explainability and improve stakeholder trust. Their insights underline the urgency to develop more understandable AI systems for effective enterprise integration.
Business leaders already lack trust in AI outcomes, which is slowing down deployments. With AI agents promising to help make business-critical decisions, the trust issue will become even more pronounced.
We hired a really clever data scientist fresh out of college, and we spent months trying to understand the inner workings of these black-box machines.
It quickly became clear that we were never going to get full transparency. That realization spurred me on to find a way to make AI more explainable.
The lack of AI trust has slowed deployments in approximately 60% of enterprises, according to a McKinsey & Company analysis.
Collection
[
|
...
]