--- title: "Enterprise AI & Governance FAQs | DataWeavrs" description: "Answers to the questions buyers ask about governance-first AI and the AI Act." url: "https://dataweavrs.com/faq" lang: "en" --- FAQ # Frequently asked questions Answers to the questions buyers ask about governance-first AI and the AI Act. How do I stop employees pasting company data into public ChatGPT? Give them a sanctioned assistant that answers from company data under access rules, and log what it is asked. WeavrCore puts every assistant, agent and integration behind one layer of visibility and control, with configurable data classification, role-based access and an audit trail by default, so the shadow-AI habit has somewhere safe to go. What does the AI Act require of a company that uses AI with customers, and since when? Since 2 August 2026, Article 50 transparency duties apply: people must be told when they interact with an AI system, and AI-generated content must be disclosed. The heavier high-risk obligations (risk management, documentation, human oversight, logging) apply to Annex III systems from 2 December 2027. Which AI systems count as high-risk under the AI Act, and what is the deadline now? Annex III lists them: employment and workforce decisions, credit and insurance, education, access to essential services, critical infrastructure, law enforcement, migration, justice and biometrics. The Digital Omnibus on AI, in force since 27 July 2026, moved the deadline for standalone high-risk systems to 2 December 2027 and for AI embedded in regulated products to 2 August 2028. Why do AI pilots fail to reach production, and what changes that? MIT's 2025 study of 300 deployments found that only 5% of enterprise AI pilots deliver a return, and that the ones that succeed are workflow-integrated and built with external specialists. What stops most pilots is governance: the security review, the missing audit trail, the architecture left until the end. Doing that work in week one, on a platform that does it by default, is what changes the outcome. What is a sovereign AI deployment, and when do you need one instead of the cloud? A sovereign deployment runs everything on your own infrastructure, with local model inference and zero data egress. You need it when data must stay inside your perimeter in every circumstance: defence, intelligence, parts of healthcare and critical infrastructure. Most regulated organisations choose Hybrid instead: private data stays home and only data approved for external processing reaches an external model. How do you keep an audit trail of what an AI said and why? Log every query, every response, every data access and every model call, append-only, with the sources each answer was grounded in. WeavrCore does this by default in every deployment mode, so if a decision is challenged six months later the reasoning can be reconstructed. How long does it take to put an AI use case into production in a regulated company? Five weeks from kickoff to production for one use case, following a seven-step method that starts with business value and builds governance in during week one. Heavily regulated engagements extend by several weeks, and the production stage depends on the customer's environment. ```json [ { "@context": "https://schema.org", "@type": "Organization", "@id": "https://dataweavrs.com/#organization", "name": "DataWeavrs", "url": "https://dataweavrs.com/", "email": "hello@dataweavrs.com", "logo": "https://dataweavrs.com/dataweavrs-logo.png", "sameAs": [ "https://www.linkedin.com/company/dataweavrs" ], "location": [ { "@type": "Place", "name": "Brussels", "address": { "@type": "PostalAddress", "addressLocality": "Brussels", "addressCountry": "BE" } }, { "@type": "Place", "name": "Madrid", "address": { "@type": "PostalAddress", "addressLocality": "Madrid", "addressCountry": "ES" } } ] }, { "@context": "https://schema.org", "@type": "WebSite", "@id": "https://dataweavrs.com/#website", "url": "https://dataweavrs.com/", "name": "DataWeavrs", "inLanguage": "en", "publisher": { "@id": "https://dataweavrs.com/#organization" } } ] ``` ```json [ { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "How do I stop employees pasting company data into public ChatGPT?", "acceptedAnswer": { "@type": "Answer", "text": "Give them a sanctioned assistant that answers from company data under access rules, and log what it is asked. WeavrCore puts every assistant, agent and integration behind one layer of visibility and control, with configurable data classification, role-based access and an audit trail by default, so the shadow-AI habit has somewhere safe to go." } }, { "@type": "Question", "name": "What does the AI Act require of a company that uses AI with customers, and since when?", "acceptedAnswer": { "@type": "Answer", "text": "Since 2 August 2026, Article 50 transparency duties apply: people must be told when they interact with an AI system, and AI-generated content must be disclosed. The heavier high-risk obligations (risk management, documentation, human oversight, logging) apply to Annex III systems from 2 December 2027." } }, { "@type": "Question", "name": "Which AI systems count as high-risk under the AI Act, and what is the deadline now?", "acceptedAnswer": { "@type": "Answer", "text": "Annex III lists them: employment and workforce decisions, credit and insurance, education, access to essential services, critical infrastructure, law enforcement, migration, justice and biometrics. The Digital Omnibus on AI, in force since 27 July 2026, moved the deadline for standalone high-risk systems to 2 December 2027 and for AI embedded in regulated products to 2 August 2028." } }, { "@type": "Question", "name": "Why do AI pilots fail to reach production, and what changes that?", "acceptedAnswer": { "@type": "Answer", "text": "MIT's 2025 study of 300 deployments found that only 5% of enterprise AI pilots deliver a return, and that the ones that succeed are workflow-integrated and built with external specialists. What stops most pilots is governance: the security review, the missing audit trail, the architecture left until the end. Doing that work in week one, on a platform that does it by default, is what changes the outcome." } }, { "@type": "Question", "name": "What is a sovereign AI deployment, and when do you need one instead of the cloud?", "acceptedAnswer": { "@type": "Answer", "text": "A sovereign deployment runs everything on your own infrastructure, with local model inference and zero data egress. You need it when data must stay inside your perimeter in every circumstance: defence, intelligence, parts of healthcare and critical infrastructure. Most regulated organisations choose Hybrid instead: private data stays home and only data approved for external processing reaches an external model." } }, { "@type": "Question", "name": "How do you keep an audit trail of what an AI said and why?", "acceptedAnswer": { "@type": "Answer", "text": "Log every query, every response, every data access and every model call, append-only, with the sources each answer was grounded in. WeavrCore does this by default in every deployment mode, so if a decision is challenged six months later the reasoning can be reconstructed." } }, { "@type": "Question", "name": "How long does it take to put an AI use case into production in a regulated company?", "acceptedAnswer": { "@type": "Answer", "text": "Five weeks from kickoff to production for one use case, following a seven-step method that starts with business value and builds governance in during week one. Heavily regulated engagements extend by several weeks, and the production stage depends on the customer's environment." } } ] } ] ```