The rapid roll-out of generative artificial intelligence (GenAI) has fundamentally changed the enterprise technology landscape, with businesses using Large Language Models (LLMs) to streamline processes like drafting legal documents and forecasting financial data. However, the massive influx of algorithm-generated text has created uncertainty, leading technology leaders to recognize the need to humanize AI outputs to preserve stakeholder trust and prevent digital fatigue. Sophisticated detection algorithms have been integrated into major platforms as countermeasures, auditing content through two metrics: burstiness (variation in sentence length and structure) and perplexity (predictability of word choice). Standard generative models produce outputs with low perplexity and low burstiness, which detection algorithms flag as machine-generated. These detection mechanisms have inadvertently triggered a systemic issue in corporate environments: a high rate of false positives, as technical documentation, compliance reports, and legal contracts inherently require rigid, predictable language.