Abstract
Generative AI systems are increasingly embedded within modern marketing pipelines, enabling organizations to produce high volumes of promotional copy with minimal human intervention. While these systems significantly improve content velocity and engagement optimization, they also introduce a subtle but material compliance and trust risk. In many real-world deployments, AI-generated marketing language exhibits amplified urgency signals such as artificial scarcity, time pressure framing, and exaggerated limited-availability claims that are not always proportionate to actual campaign constraints. Because these systems are frequently optimized for conversion performance, they may systematically favor phrasing that heightens perceived urgency even when supporting inventory, timing, or promotional conditions are weak, delayed, or partially uncertain. Over time, this optimization dynamic can create a measurable drift toward increasingly aggressive promotional pressure that may not be immediately visible through conventional compliance review processes. This disclosure introduces the concept of the Urgency Inflation Problem, defined as a condition in which AI-generated promotional content expresses pressure cues that materially exceed verifiable campaign context. The proposed detection framework evaluates urgency markers, contextual support signals, and campaign metadata to compute a bounded Urgency Risk Score (URS) representing the likelihood that a given output may create disproportionate consumer pressure. The architecture is intentionally model-agnostic and suitable for real-time integration into email marketing systems, digital advertising platforms, and e-commerce content pipelines. By identifying inflated urgency language prior to publication, the framework enables organizations to maintain regulatory alignment, reduce consumer protection exposure, and preserve long-term brand credibility while continuing to benefit from AI-driven marketing automation at scale.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "The Urgency Inflation Problem in AI-Generated Promotions", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9385