AI systems do not evaluate brands the way humans do. They do not assess ambition, vision, or momentum. They evaluate stability.
A brand is treated as real by an AI system when it appears repeatedly, consistently, and independently across trusted sources over time. This behavior is well documented in research on large language models.
Work from Stanford’s Center for Research on Foundation Models shows that language models rely heavily on frequency, consistency, and cross-source agreement when generating confident outputs. When information is fragmented or contradictory, models either generalize or omit it entirely.
This is where narrative consistency becomes measurable. We use the term narrative consistency score to describe how consistently a brand is explained across independent, trusted sources. The score is not driven by volume of mentions. It is driven by alignment. When multiple sources describe a company in similar language, with similar positioning and proof points, AI systems gain confidence that the explanation is stable.
Brands with a high narrative consistency score are easier for AI systems to reuse. Their descriptions require less inference. Their positioning appears settled rather than contested. As a result, they are more likely to be named, summarized, and recommended in AI-generated answers.
Brands with a low narrative consistency score experience the opposite. One article frames them one way. Another reframes them differently. From a marketing perspective, this may feel like nuance. From an AI perspective, it signals uncertainty. Research from OpenAI on reinforcement learning from human feedback explains why models avoid uncertain outputs. Reusing stable, widely corroborated explanations reduces the risk of hallucination.
Narrative consistency also compounds over time. AI systems place greater trust in explanations that persist across months and years. When PR activity stops or becomes inconsistent, narrative consistency erodes gradually. The brand does not disappear overnight. It fades as newer, more stable explanations replace it.
This is why pausing PR often leads to long-term visibility decay in AI search, even when business performance remains strong. AI systems do not track quarterly results. They track the public record.
For CMOs, this reframes PR entirely. The objective is no longer attention or coverage volume. The objective is maintaining a high narrative consistency score across the open web. That score determines whether AI systems treat the brand as real, credible, and safe to reference.
In AI search, credibility is not asserted. It is inferred. Narrative consistency is the signal that enables that inference.