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Building a Production-Ready AI Service Framework: The New Focus of Enterprise AI Competition

As AI transitions from the experimental stage to large-scale production, corporate competitiveness is shifting from model selection to the serving harness—a suite of engineering systems that encompasses architecture design, memory management, scheduling strategies, and inference optimization. In an article published in MIT Technology Review, EXL points out that the key determinant of AI’s real-world impact is no longer model parameters or benchmark scores, but rather the ability to translate model capabilities into business value with controllable costs, reliable latency, and sustainable scalability.

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Building Context-Aware AI: A New Paradigm for Enterprise Decision Intelligence

Celonis Chief Scientist Devavrat Shah argues that enterprise AI must be embedded in real-world business contexts—such as workflows, role-based permissions, and process constraints—in order to achieve “decision intelligence”; AI divorced from context is prone to errors, resource waste, or ineffective outputs. His technical approach relies on a “context layer” and a digital twin architecture, emphasizing incremental validation, role-based governance, and workforce empowerment. However, all these claims are subjective assertions and have not been independently verified.

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