This paper explains why financial institutions are rapidly investing in AI while struggling to scale it beyond pilots. It highlights high-impact use cases such as fraud detection, risk analytics, customer personalization, trading optimization, and back-office automation. The core challenge is operationalizing AI across fragmented data, legacy systems, and regulated environments. The paper argues that enterprise orchestration is the missing control layer—coordinating data pipelines, model training, inference, retraining, governance, and compliance. By standardizing and observing workflows end-to-end, institutions can improve reliability, security, and performance while enabling real-time, AI-driven decision-making across the organization.

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