Identifying use cases to integrate GenAI into existing application architectures

Integrating Generative AI (GenAI) into your existing business applications can unlock new levels of efficiency and innovation for cloud architects and engineers. However, identifying the most suitable applications and use cases can be challenging. This guide will explore various steps to identify potential candidates for GenAI integration within your cloud environment. We’ll then delve into specific problem types GenAI services can address across AWS, Azure, and GCP.

Steps to find potential cloud applications to integrate GenAI services

Analyze Existing Workflows:

1. Process Mapping: Visually map your cloud applications and business processes. Look for tasks that are

• • Repetitive: Manual data entry, report generation, content creation. GenAI can automate these tasks with increased speed and accuracy.
  • Error-prone: Tasks requiring human judgment or interpretation of large datasets. GenAI can assist with data analysis and anomaly detection.
  • Time-consuming: Steps that slow down overall workflow. GenAI can streamline processes and free up human resources for higher-level tasks.

Read: How GenAI Cloud services are revolutionizing Application Architectures
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2. Data Analysis: Dive deep into the data flowing through your applications. Look for patterns and trends that GenAI can leverage to

• • Personalize Customer Interactions: Generate targeted marketing content or product recommendations based on customer data.
  • Automate Content Creation: Produce summaries of reports, social media posts, or marketing materials.
  • Generate New Ideas: Explore design concepts, product variations, or content themes through text generation or image manipulation.

Match GenAI Capabilities to Needs:

• • Explore Cloud Provider Services: Familiarize yourself with the specific GenAI services offered by your cloud platform (AWS, Azure, GCP). These services can include:
  • Text Generation: Create realistic and creative text formats, from marketing copy to code snippets. For example, If your help desk struggles with repetitive ticket responses, integrate a text generation service to automate responses to frequently asked questions.
  • Image Manipulation: Generate new images, edit existing ones, or perform tasks like object removal or style transfer.
  • Data Augmentation: Enrich existing datasets by creating synthetic data for training AI models.
  • Align Capabilities with Opportunities: Match the identified pain points and opportunities with relevant GenAI functionalities.
  • Proof of Concept (POC): Develop a small-scale POC to test the feasibility and impact of integrating GenAI into a specific process. Analyze the results to assess potential benefits and identify challenges before full-scale deployment.

Common problems that can be addressed by Cloud GenAI services

Below are some of the examples illustrate how GenAI can be leveraged to solve specific business challenges and streamline processes across various cloud platforms using their Cloud GenAI services.

Read: Skills required to develop GenAI apps in Cloud

AWS:

• Problem: Developers needed a way to rapidly develop and deploy generative AI applications.
• Solution: AWS introduced the Generative AI Application Builder, which accelerates development by allowing users to ingest business-specific data, evaluate large language models (LLMs), and deploy applications with an enterprise-grade architecture. For instance, Amazon CodeWhisperer helps developers by generating code suggestions in real-time.

Azure:

• Problem: Businesses required a scalable way to incorporate generative AI into their operations.
• Solution: Azure Machine Learning and Azure OpenAI Service provide tools for prompt engineering and building language model-based apps. Azure’s Generative AI services enable businesses to automate content generation and enhance content quality, which can be used in marketing and branding to create personalized campaigns. For example, a popular retailer company leveraged Azure OpenAI solution and enabled the company to produce personalized marketing campaigns at a scale previously unattainable. This resulted in more effective marketing efforts, better customer engagement, and an overall increase in sales.

GCP:

• Problem: Organizations found it challenging to access and customize generative AI for their needs.
• Solution: Google Cloud introduced support for generative AI in Vertex AI, giving data science teams access to foundation models. The Generative AI App Builder allows developers to create new experiences like bots and digital assistants quickly. This has enabled businesses and governments to generate text, images, code, and more from simple natural language prompts.

Real-World GenAI integration success stories:

Here are some of the success stories of various types of companies who successfully integrated GenAI services into their existing application landscapes and business process.

• Netflix uses GenAI to generate personalized video previews for each user, increasing engagement and watch time.
• Forbes employs a large language model (LLM) to generate summaries of financial reports, allowing users to quickly grasp key insights.
• Autodesk leverages GenAI for image manipulation, allowing designers to explore variations of product concepts through automated style transfer.
• Boehringer Ingelheim utilizes GenAI for drug discovery, generating new molecule structures for potential drug candidates.

The implementation of Generative AI (GenAI) has had a significant impact on business revenue, with companies experiencing broad productivity gains and efficiency improvements. Here are some key insights:

• Deployment of GenAI tools has led to productivity gains of 10% to 20% across enterprises, with the potential to reshape processes and functions to deliver 30% to 50% gains in efficiency and effectiveness.
• BCG’s survey of over 2,000 CEOs and C-suite executives revealed that GenAI is changing the way companies do business, with 54% of leaders expecting AI and GenAI to deliver cost savings in 2024.
• IDC forecasts that by 2025, 35% of global enterprises embracing GenAI to co-develop digital solutions could potentially double revenue growth compared to competitors.
• A McKinsey study estimates that GenAI-enhanced productivity and innovation could add between $2.6 and $4.4 trillion to the global economy annually.

In terms of cloud workloads, the 2024 Cloud Report by Cockroach Labs provides an unbiased analysis of instance types across AWS, Azure, and GCP, helping businesses find the best options for their workloads. Additionally, the Flexera report reveals that nearly 25% of AWS and Azure customers are spending between $500,000 to over $5 million each month, highlighting the significant investment and adoption rates of cloud services.

Read: GenAI Basics and Fundamentals

These reports and insights demonstrate the transformative effect of GenAI and cloud services on business growth and revenue, with CEOs and business professionals recognizing the value and opportunities presented by these technologies.

Several companies, like the ones mentioned in the e-commerce and customer service examples, have successfully identified GenAI use cases that align with their technical and business needs. Others are still exploring the potential of this technology within their organizations.

By following these steps and by staying updated on emerging GenAI capabilities, cloud architects and engineers can identify and implement innovative GenAI solutions that can significantly improve efficiency, unlock new possibilities, and create a competitive edge for their businesses.