Abstract
Enterprise AI assistants have reached a curious stage of evolution. They are fluent, well-aligned, and impressively cautious. They can explain the landscape, enumerate the options, and politely step back at the exact moment a decision is required. On paper, this looks like responsible design. In production, it often looks like an assistant that knows the answer but would rather not be quoted. This paper formalizes this behavior as the Decisiveness Gap (DG), a measurable condition in which AI systems systematically under-commit despite having sufficient contextual evidence to support a clear recommendation. The proposed Recommendation Clarity Score (RCS) quantifies the mismatch between contextual decision readiness and observed recommendation strength. The goal is not reckless assertiveness. The goal is proportional courage. Across controlled enterprise-style workflows, elevated DG signals consistently correlate with human hesitation, prompt repetition, and manual override behavior. The implication is uncomfortable but increasingly difficult to ignore: an assistant that explains everything but recommends nothing may be safe, compliant, and quietly expensive to operate. As organizations push AI deeper into decision-critical roles, the ability to detect and correct the Decisiveness Gap will separate systems that merely sound intelligent from those that actually move work forward.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "The Decisiveness Gap in Enterprise AI Assistants: (When Systems Know Enough but Still Won’t Say It)", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9444