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  "documentTitle": "2023 Scaling Generative AI",
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      "text": "Inconsistent processes can lead to risks and inefficiencies, while consistent governance processes help standardize workflows for data collection, solution engineering, output validation, and performance monitoring. Common delivery frameworks (e.g., LLMOps) bring together GenAI development and deployment into a unified, governed lifecycle that is secure and compliant. A common misconception is that strong processes can hinder speed and creativity. Our experience suggests the opposite. By understanding how work needs to be done and the accompanying guardrails, teams are empowered to explore ways to generate value without fear that they may be making a mistake. Clear boundaries allow freedom for bold action and innovation, while a lack of clarity may lead to more conservative approaches. Governance includes documented roles and responsibilities that drive stakeholder accountability in decision-making across the AI lifecycle, and inform the controls for risk identification and mitigation. Governance also standardizes how stakeholders identify, prioritize, and approve GenAI applications. As processes are amended, organizations need to be mindful about disrupting existing automated or manual controls and take steps to establish assurance in those amended processes. Even as the regulatory landscape is in flux, organizations should proactively establish governance processes that meet existing or likely regulatory requirements.",
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