Generative AI workload assessment - AWS Prescriptive Guidance
Purpose of this guideTarget audience and benefitsScope

Generative AI workload assessment

Tabby Ward and Deepak Dixit, Amazon Web Services (AWS)

November 2024 (document history)

Generative AI workload assessment is a strategic method aimed at evaluating and improving an organization's preparedness to create or update its generative AI workloads. This assessment is important because incorporating generative AI into business operations can greatly change how things work, and can provide new efficiencies and capabilities. However, to adopt generative AI successfully, it's essential to thoroughly understand current systems and have a clear plan for the future.

Generative AI workloads refer to computational tasks that involve the use of artificial intelligence models that can create new content, such as text, images, code, or other data types. These workloads typically require substantial computing power, specialized hardware such as GPUs, and large datasets for training and inference. Integrating generative AI workloads into operations presents several challenges:

  • Infrastructure requirements: Provisioning the significant computational resources and specialized hardware that generative AI models require.

  • Data management: Ensuring data quality, privacy, and compliance while handling large datasets.

  • Skills gap: Lack of expertise in AI technologies and model deployment.

  • Ethical considerations: Addressing bias, fairness, and transparency in AI-generated content.

  • Integration complexity: Seamlessly incorporating generative AI into existing workflows and legacy systems.

  • Cost management: Balancing the potential benefits with the high costs of implementation and operation.

Overcoming these challenges requires careful planning, investment in infrastructure and talent, and a strategic approach to implementation.

Purpose of this guide

Generative AI is rapidly becoming a critical component across many industries. It provides transformative opportunities but also pose challenges in terms of integration, compliance, and scalability. Many organizations struggle to fully leverage AI due to weak technological foundations, resistance to change, and data quality issues. The generative AI workload assessment addresses these challenges by identifying the requirements for modernization, defining the scope of implementation, and challenging legacy systems and thinking. It also aids in determining minimum viable products (MVPs) and helps you develop a target solution architecture, ensuring a structured and strategic approach to AI adoption.

This guide serves as a structured approach to help organizations navigate the complexities of adopting generative AI technologies. Instead of clearly defining requirements from the outset, the guide assists in:

  • Identifying potential use cases for generative AI within your organization.

  • Assessing your organization's readiness for generative AI adoption.

  • Defining and refining use case goals and stretch goals.

  • Determining the scope and requirements for generative AI implementation.

  • Developing a target solution architecture.

Target audience and benefits

This assessment is specifically designed for solutions architects, enterprise architects, and application architects who want to evaluate the technical aspects of generative AI workload modernization. It is also valuable for program and people managers who want to gauge their team's overall readiness, resource allocation, and enablement requirements. Industry best practices emphasize the importance of a comprehensive assessment to ensure readiness for AI adoption. This includes evaluating architecture, storage, compliance, integration, testing, deployment, and automation.

Scope

The following topics are in-scope for the generative AI workload assessment method:

  • Current generative AI technologies and models (for example, large language models, image generation models)

  • Narrow AI applications that use generative techniques

  • Integration of generative AI with existing systems and workflows

  • Data strategies for training and fine-tuning generative AI models

  • Ethical considerations and responsible AI practices for current generative AI applications

  • Testing and deployment strategies for generative AI in production environments

  • Security and privacy considerations for generative AI implementations

  • Performance optimization and scalability of generative AI workloads

  • Use cases and applications of generative AI in various industries

  • Evaluation of generative AI outputs and quality assurance processes

The following topics are out of scope:

  • Artificial general intelligence (AGI) and artificial superintelligence (ASI) scenarios

  • Speculative future advancements in AI beyond current generative models

  • Quantum computing applications in AI

  • Neuromorphic computing and brain-computer interfaces

  • Consciousness and self-awareness in AI systems

  • Long-term societal impacts of advanced AI beyond current generative AI applications

  • Regulatory frameworks for hypothetical future AI technologies

  • Philosophical debates on the nature of intelligence and consciousness in machines

  • Extreme edge cases or highly speculative use cases of AI

  • Detailed technical specifications of proprietary AI models or architectures