# Assessment considerations and prerequisites
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## Start with clear use cases
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Identify specific business problems or opportunities that generative AI can address. Focus on use cases that align with strategic business goals and offer measurable benefits. Prioritize use cases that target commonly faced challenges within the organization to ensure that the solution architecture can serve as a pattern for multiple scenarios.

Initiating the assessment process with a general understanding of potential generative AI applications is beneficial but not mandatory. The [questionnaire](questionnaire.md) that's included with this guide accommodates various levels of preparedness, from organizations that have well-defined use cases to those that have only broad ideas. The assessment process serves to:
+ Refine and clarify these initial use case ideas.
+ Identify new potential use cases.
+ Develop specific, measurable goals for each use case.
+ Assess the feasibility and potential impact of each use case.

Let's consider a hypothetical example: A financial services company decides to explore generative AI modernization. They start with a broad idea of improving their customer service and fraud detection processes.
+ **Initial assessment**: The questionnaire helps them evaluate their current systems, data quality, and organizational readiness for generative AI adoption.
+ **Use case refinement**: Through the assessment process, they refine their initial ideas into two specific use cases:
  + Implementing a generative AI-powered chatbot for customer inquiries
  + Using generative AI for real-time transaction fraud detection
+ **Goal setting**: For each use case, they define specific goals:
  + Reduce customer service response time by 40 percent within 6 months
  + Improve fraud detection accuracy by 20 percent and reduce false positives by 15 percent
+ **Stretch goals**: They also set these ambitious targets:
  + Achieve 80 percent customer satisfaction with AI-assisted responses
  + Develop a predictive fraud detection model that identifies new fraud patterns
+ **MVP definition**: The questionnaire helps them determine an MVP for each use case, focusing on essential features that deliver immediate value.
+ **Target architecture**: Finally, they develop a target architecture that supports one or both use cases, and ensures scalability and integration with existing systems.

## Ensure business alignment
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Align generative AI initiatives with overall business strategy and objectives. For each use case, develop a clear value proposition that demonstrates how generative AI contributes to business growth, efficiency, or innovation. Establish metrics to measure the impact of generative AI implementations on key performance indicators (KPIs).

## Implement governance and oversight
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Create a cross-functional steering committee to oversee generative AI initiatives. Develop policies and guidelines for responsible AI use, addressing ethical considerations and potential biases. Establish a review process for generative AI projects to ensure compliance with organizational standards and regulatory requirements.

## Address data and technical prerequisites
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Assess and improve data quality, and implement data governance practices to ensure reliable inputs for generative AI models. Develop a data strategy that addresses data collection, storage, and management that are specific to generative AI needs. Evaluate and enhance data infrastructure to support the volume and velocity of data required for generative AI workloads.

## Consider compute resource requirements
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Assess current IT infrastructure and identify gaps in computational capacity for generative AI workloads. Plan for scalable compute resources, considering options such as cloud services or on-premises high-performance computing clusters. Optimize resource allocation to balance performance and cost-effectiveness for both training and inference workloads.

## Address privacy and security implications
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Implement robust security measures to protect sensitive data used in generative AI training and operations. Ensure compliance with data protection regulations such as General Data Protection Regulation (GDPR) or California Consumer Privacy Act (CCPA) when handling personal information. Develop protocols for secure model deployment and monitoring to prevent unauthorized access or misuse of generative AI capabilities.

## Engage stakeholders early
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Involve key stakeholders from the beginning to gain leadership buy-in and support. Clearly communicate the benefits and potential impact of modernization initiatives, specifically for generative AI workloads. Provide training and resources to help stakeholders understand generative AI technologies and their implications.

## Iterate and learn
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Adopt an incremental approach that lets you refine target solutions. Use feedback loops to continuously improve workload architecture and processes. Regularly assess the performance and impact of generative AI implementations, and adjust strategies as needed based on real-world results and evolving business needs.