This brief explains how to build a pragmatic AI strategy by understanding the full AI landscape, assessing and prioritizing use cases and balancing rapid adoption with strong governance. Page 1 emphasizes evaluating benefits, risks and costs while preparing data platforms and modern infrastructure such as GPUs and cloud AI services. Page 2 outlines development choices including model selection, RAG, fine-tuning and ongoing QA, along with the need for observability and responsible policies as AI introduces new security risks. Verinext guides organizations in designing and executing high-value AI solutions.

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