What is Business Use Case: Sample GenAI Business Use Case on AWS

What is Business Use Case

A business use case is a structured narrative that describes how a system or process should behave from the perspective of an actor or stakeholder. It helps to communicate the functional requirements of a system or process, providing clarity on the interactions, outcomes, and conditions under which the system or process operates.

Parts of a Business Use Case

A well-defined business use case typically consists of the following parts:

• Use Case Name
• Brief Description
• Actors
• Preconditions
• Basic Flow (Main Success Scenario)
• Alternative Flows (Extensions)
• Postconditions
• Business Rules
• Nonfunctional Requirements
• Assumptions
• Notes or Additional Information
• Key Metrics

Sample GenAI Business Use Case on AWS:

Use Case Name:

Personalized Content Generation for E-commerce

Brief Description:

This use case outlines the implementation of a Generative AI-driven system on AWS to automatically generate personalized product descriptions and marketing content for an e-commerce platform. By leveraging customer data and AI models, the system enhances engagement and automates the content creation process, improving the platform's efficiency and customer experience.

Actors:

1. Customer – End users interacting with the e-commerce platform.
2. Marketing Team – Responsible for overseeing the quality and relevancy of generated content.
3. E-commerce Platform – The platform that hosts product listings and showcases the AI-generated content.
4. AWS Services – Systems that generate, store, and serve the content, including Amazon SageMaker, Amazon Personalize, AWS Lambda, and Amazon S3.

Preconditions:

1. Available User Data: Customer interaction data (browsing history, past purchases) must be stored in a data warehouse (e.g., Amazon Redshift).
2. Trained AI Model: The Generative AI model must be deployed on Amazon SageMaker for generating personalized product descriptions.
3. AWS Lambda Functions Configured: Lambda functions are configured to trigger content generation upon customer actions (e.g., product views).
4. Product Catalog: The e-commerce platform’s product catalog must be up to date in S3 or a database like DynamoDB.

Basic Flow (Main Success Scenario):

1. Customer Browses Website: A customer lands on the platform and begins browsing products.
2. Trigger Content Generation: An AWS Lambda function is invoked when a customer views a product or enters a search query.
3. Generate Personalized Content: The trained GenAI model on Amazon SageMaker generates personalized product descriptions and recommendations based on the user’s interaction data.
4. Display Generated Content: The personalized content is served to the e-commerce platform via API calls to Amazon Personalize and Amazon SageMaker.
5. Customer Views Content: The customer sees the personalized product descriptions and recommendations, improving engagement.
6. Feedback Loop: Customer interactions with the generated content (e.g., clicks or purchases) are captured to retrain the model for future improvements.

Alternative Flows (Extensions):

1. Content Generation Failure:

Condition: AI model fails to generate content due to an error or data limitations.

Action: Default product descriptions are used instead, and an error is logged for troubleshooting.

2. Insufficient User Data:

Condition: There is insufficient customer interaction data to personalize content.

Action: The system provides generic recommendations using Amazon Personalize’s collaborative filtering based on popular products.

Postconditions:

1. Content Delivered: Personalized product descriptions and recommendations are successfully displayed on the platform.
2. Updated Training Data: Customer interaction data (e.g., clicks, purchases) is stored and used for model retraining to enhance future content personalization.

Business Rules:

1. Content Accuracy: Generated product descriptions must accurately reflect the product catalog and adhere to the company’s marketing guidelines.
2. Compliance with SEO Standards: All generated content must meet search engine optimization (SEO) best practices to maximize visibility.
3. Data Privacy: Customer data used for personalization must comply with privacy laws (GDPR, CCPA) and be handled securely.

Nonfunctional Requirements:

1. Performance: Content generation and delivery should occur within 500 milliseconds to ensure no impact on user experience.
2. Security: Data in transit between AWS services must be encrypted. Access to the GenAI model and customer data must be restricted based on IAM policies.
3. Scalability: The system should be capable of handling tens of thousands of concurrent requests across multiple regions.
4. Reliability: The system must maintain 99.9% availability for content generation services.

Assumptions:

1. Real-Time Data Availability: Customer data (e.g., interaction history, preferences) is available in near real-time for the GenAI model to generate relevant content.
2. Automated Model Retraining: The Generative AI model is periodically retrained based on new customer interactions to improve personalization accuracy.
3. Cloud-Native Integration: The e-commerce platform is integrated with AWS services for real-time data exchange and API interactions.

Notes or Additional Information:

• The system is designed to be modular and can integrate with additional AI services for more advanced features (e.g., voice-based search using Amazon Lex or personalized email marketing with Amazon SES).
• Continuous improvement through retraining ensures the AI model stays relevant with evolving customer preferences and trends.

Key Metrics:

• Customer Engagement Rate:

Definition: Measure how often customers interact with the personalized content (e.g., clicks on personalized product descriptions or recommendations).

Target Metric: Increase engagement by 15-20% compared to non-personalized content.

• Conversion Rate:

Definition: Track the percentage of customers who make a purchase after interacting with personalized content.

Target Metric: Improve the conversion rate by 10%.

• Content Generation Time:

Definition: The time it takes for the Generative AI model to generate and serve personalized content to the user.

Target Metric: Maintain content generation within 500 milliseconds.

• Model Accuracy:

Definition: Evaluate the accuracy of the personalized content by tracking customer satisfaction (e.g., product reviews) or accuracy in product descriptions.

Target Metric: Achieve 90% accuracy in content relevance.

• Feedback Loop Efficiency:

Definition: Measure how effectively the system learns from customer interactions and improves personalization in subsequent interactions.

Target Metric: Decrease customer churn by 5% due to better personalization over time.

• Scalability:

Definition: Number of simultaneous users supported by the system while maintaining performance targets.

Target Metric: Support at least 100,000 concurrent users without degradation in service.

• Cost Savings:

Definition: Generative AI helps reduce labor costs associated with manual content creation, optimize processes, and improve overall efficiency.

Target Metric: Achieve a 25% reduction in labor costs related to content generation, as well as a 15% decrease in overall content production costs.

• Time Savings:

Definition: Generative AI automates repetitive content creation tasks, leading to faster turnaround times and reduced delays.

Target Metric: Reduce content generation time by 50% compared to manual processes, leading to a faster go-to-market strategy for new products.

• Quality Improvement:

Definition: Generative AI enhances the quality of product descriptions, recommendations, and marketing content. Metrics like coherence, creativity, and accuracy are key.

Target Metric: Improve the quality of content by 20%, with enhanced coherence, creativity, and reduced content-related errors.

• Customer Satisfaction:

Definition: Using personalized content improves customer experience and engagement. Metrics such as customer satisfaction scores, net promoter score (NPS), and sentiment analysis can be used.

Target Metric: Achieve a 10-point improvement in NPS and a 15% increase in positive sentiment analysis scores for AI-generated content.

• Productivity Gains:

Definition: Generative AI augments the capabilities of the marketing and content creation teams, allowing them to handle more volume with fewer errors.

Target Metric: Increase productivity by 30%, with more content generated and reduced error rates in product descriptions by 20%.

This structured narrative clearly defines how AWS services can be used to implement a business use case for personalized content generation, helping stakeholders understand the system’s functional and nonfunctional requirements.

Credits: AWS Developing Generative AI Solution Course on Skill builder : https://explore.skillbuilder.aws/learn/course/19610/developing-generative-artificial-intelligence-solutions;lp=2194

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