This ebook explores how organizations can build trust in AI-driven systems by prioritizing transparency, explainability, and governance. It explains the differences between traditional analytics, deterministic AI, and large language models, highlighting the risks of opaque decision-making. The guide outlines key principles for trustworthy AI, including auditability, reproducibility, bias mitigation, and human oversight. It discusses how enterprise AI agents can be designed to augment decision-making rather than replace it, ensuring accountability and regulatory compliance. Use cases demonstrate how explainable AI improves adoption across regulated industries. The ebook concludes that trust in AI is achieved through clear logic, governance frameworks, and responsible deployment practices th

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