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| 1 | +Infrastructure management is a critical component of adopting any IT solution. Poorly managed infrastructure can lead to inefficiencies, higher costs, and operational risks. If the infrastructure for generative AI is over-provisioned or poorly managed, the organization that owns pays the costs of that infrastructure risks having the financial benefits of adopting generative AI diminished by the costs associated with hosting generative AI solutions. |
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| 3 | +## Specifying generative AI best practices |
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| 5 | +When implementing a generative AI CoE, you can ensure that the generative AI CoE is required to determine what best generative AI adoption practices will be followed prior to those solutions are deployed in the organization's infrastructure environment. A properly constituted generative AI CoE can also determine appropriate oversight mechanisms for the deployment and operation of the solution. Not only can oversight mechanisms and the implementation of best practices instituted from generative AI workload conception optimize costs, but they can also help ensure that a generative AI deployment complies with the regulatory requirements relevant to the organization. |
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| 7 | +## Security |
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| 9 | +As organizations integrate generative AI into their workflows, they must account for risks related to data protection, model security, compliance, adversarial attacks, and responsible AI governance. Security isn't a standalone function, but an essential component of every generative AI deployment. |
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| 11 | +While each generative AI solution incorporates security in different ways, a generative AI CoE plays an important role in setting guidelines and supporting teams in secure AI implementation. Rather than replacing dedicated security teams, the CoE needs to be in close and constant collaboration with them, to help embed security considerations into AI-related processes and align them with organizational security policies. |
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| 13 | +## Determining governance requirements |
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| 15 | +Governance is the critical backbone supporting the consistent application of responsible AI principles and practices across an organization. It involves setting company-wide rules, defining processes, and establishing roles for the stakeholders involved in the AI lifecycle. |
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| 17 | +Responsible AI governance bodies need sufficient financial resources, human capital, and authority to enact meaningful changes across the organization, ensuring responsible AI practices are both actionable and sustainable. |
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| 19 | +The generative AI CoE should be an integral part of the governance model, strengthening responsible AI through its practices. This includes skilling, development, deployment, monitoring strategies, and data management frameworks. |
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| 21 | +The CoE plays a critical role in providing AI-specific data standards and best practices while collaborating with specialized teams responsible for broader data governance and architecture. Additionally, generative AI systems often handle sensitive and regulated data, requiring organizations to comply with standards like GDPR, HIPAA, and CCPA. Implementing robust data security, privacy, and responsible AI practices help mitigate risks while fostering trust in AI-driven decisions. |
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| 23 | +## Cost management |
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| 25 | +Managing the financial impact of generative AI systems requires a structured approach to optimize costs while maintaining performance and scalability. Generative AI solutions often involve high computational and storage expenses, making cost control a critical aspect of operational success. |
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| 27 | +This practice establishes processes and tools to monitor, forecast, and optimize spending, ensuring that resources are efficiently allocated to maximize ROI. A key consideration in cost efficiency is determining whether to develop custom generative AI solutions or leverage off-the-shelf alternatives. Organizations must assess factors such as cost integration, complexity, and adaptability to make informed investment decisions. |
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| 29 | +A generative AI CoE can contribute to cost efficiency by promoting FinOps principles, driving collaboration between finance, engineering, and business teams to ensure that financial considerations are integrated into every stage of generative AI solution development and deployment. |
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| 31 | +## Monitoring and optimization |
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| 33 | +Effective measurement is crucial for selecting and optimizing generative AI models. Accurately interpreting metrics and benchmarks ensures meaningful AI evaluation, helping organizations choose the right models based on factors like accuracy, efficiency, and relevance to their use cases. |
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| 35 | +Selecting the appropriate model from the generative AI catalog requires understanding how different benchmarks align with real-world application needs. |
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| 37 | +This includes using metrics to assess and mitigate biases in the models, ensuring fairness and ethical outcomes. These metrics can help organizations identify models that aren't only effective but also aligned with responsible AI principles. Organizations should establish a system for continuous monitoring and improvement, using metrics to set baselines, track progress, and identify areas for refinement. |
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