AI's next big opportunity lies in capturing organizational "vibes," which refers to the emotional context surrounding decisions. Traditional systems like Salesforce and Workday focus on structured data, but they miss the subtleties that affect outcomes. For example, a deal closed at a significant discount might be linked to the approving VP's mood after a two-martini lunch, or a customer's all-caps email might indicate urgency. This lack of insight creates a "vibe gap," costing enterprises significantly by not accounting for the nuanced emotional landscape that influences decisions. The proposed solution is the "vibe graph," a system designed to track and analyze these ambient sentiments. It would capture a range of vibe signals—from the timing of email responses to punctuation choices in messages—transforming them into structured data. This graph would not only connect entities and decisions but also contextualize them, offering insights like the emotional energy of sales reps or customers at the time of a contract signing. For instance, knowing a sales rep was under quota pressure or that a customer felt skeptical due to lengthy vendor evaluations would provide valuable context that structured data alone cannot offer. Building such a vibe graph requires a new architecture, including layers for vibe signal ingestion, resolution, and the graph itself. Existing companies are unlikely to succeed in this space because their systems are designed for transactional data, not for capturing the ambient emotional signals that occur in real-time. Startups that create infrastructure specifically for vibe tracking can potentially dominate this emerging market by offering insights that established players cannot replicate. The ability to reconstruct the emotional context around decisions could become a key differentiator in the enterprise software landscape.