Wedbush Morning Call - Jun 26 2026 6:54AM

研报英文原文证据摘录

Wedbush Morning Call - Jun 26 2026 6:54AM

Data Quality and Context Represent the Real Bottleneck. Across every conversation, executives continued to emphasize

the importance of data being fed into model capabilities as the main driver of quality, recency, and relevancy for enterprise

outcomes with LLMs expected to be commoditized over time. Many customers struggle to leverage AI given the stale,

incomplete, or poorly structured data which leads to poor performance for agentic deployments given the unprepared data

environments leading to arguably the largest reason that AI deployments fail to scale. Real-time data represents the most

meaningful differentiator in agentic AI deployments where the difference between data that is seconds old versus hours/

days old can represent the largest factor impacting personalized, useful experience and a generic, irrelevant experience. The

organizations that have been investing early in data infrastructure are the ones capitalizing on the agentic AI opportunity to

see tangible returns while the companies that skipped this foundational stage find themselves stuck. As we mentioned in our

data deep dive, the data layer represents both the decisive constraint, and the monetization unlock within the enterprise

AI buildout.

Governance and Compliance No Longer Obligations, Rather Competitive Advantages. With agentic deployments accelerating

at scale, regulatory requirements and security features that were previously seen as friction points are now viewed as

imperative with governance an increasingly important feature of agentic buildouts. In highly regulated industries, the ability

to demonstrate auditability, data residency controls, and transparent decision trails is now a prerequisite to entering the