The enterprise floor was thick with context confusion. Ask for "net revenue" one day, then "ARR" the next, and the results—reliable as river ice in springtime—came back inconsistent. The same question, a scattershot of numbers. It was a failure of definition, that low hum of unreliability which kept the technology squarely in the experimental zone.
The current trajectory is toward agentic analytics. Not just asking, but executing. Autonomous AI systems capable of complex, end-to-end operations: analyze data, make a choice, take action. This required a transformation built deeper than surface prompt engineering. AtScale, long centered on semantic layer architecture, recognized that moving past unreliable chatbots demands an actual foundation of business understanding.
The semantic layer is the necessary structure, mapping the established definitions—what "revenue" precisely means, right now—across every disparate data store. It eliminates the existential crisis of a chatbot querying mismatched data.
This approach transforms the AI agent from a tentative tool into something enterprise-ready. Bedrock. McKinsey identified this move toward agentic AI as one of the fundamental innovations predicted to drive the next wave of business impact.
It's about ▩▧▦ speed. It's about certainty, about creating systems that can slice cleanly through human bottlenecks to accelerate the decision cycles previously hampered by inconsistent reporting.
AI agents are reshaping enterprise analytics, and AtScale, a leader in semantic layer solutions, is helping organizations navigate this shift from ...Here's one of the sources related to this article: Visit website
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