Discussing the critical gap between AI model capability and AI readiness, and arguing that AI failures often stem from poor data rather than flawed models. Using an example of an incorrect Microsoft Teams user count, explaining that AI requires trusted, current, accurate, complete, and contextual data to be useful. The presentation defines the four pillars of trusted data and emphasizes that contextual data—comparing internal metrics against industry benchmarks—is a key differentiator. Introducing VOSS as a solution that integrates across identity, collaboration, and communications systems to provide a holistic, reconciled view of an organization's operational estate. The content concludes by noting that while AI can provide recommendations, the ultimate question of execution will be addressed in a future installment of the series.