AI’s potential business value is now widely recognized. However, true enterprise transformation remains limited by unclear objectives as to what to use AI for and an underestimation of required change management. More immediate technical challenges relate to growing inference costs, fragmented data environments, and weak governance frameworks. Organizations that want to succeed with AI will need to focus on measurable ROI-driven use cases while building secure, flexible, and policy-driven AI architectures. In this context, trust is emerging as a decisive factor. Trust is based on AI transparency, data lineage, explainability, and strong AI governance. By building AI solutions on trusted data and delivering measurable outcomes, organizations can move beyond experimentation and transform AI into a core operational capability. This Expert View outlines the first steps to operationalize AI.
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