Powering technology lifecycle innovation with AWS services & Amazon Q (AIM124)

Here is a detailed summary of the video transcription in markdown format, broken down into sections for better readability:

Introduction

• Kik from Accenture, leading global asset engineering
• Over 20 years of experience, specializing in AI and Automation in software development and platform management
• Filed over 115 patents in this space
• Collaborating with Luke Higgins, the Chief Arch Tech responsible for Accenture's Global Asset and Automation Deployment

Approach to Generative AI

• 2 years ago, the team started exploring the potential of generative AI, especially with the launch of ChatGPT
• Worked with Stanford to create a scientific model on applying generative AI across the technology delivery lifecycle
• Analyzed over 50,000 projects to understand where generative AI can complement traditional automation
• Collaborated with AWS to understand their capabilities around foundation models (e.g., CodeWhisperer)
• Conducted 70+ controlled pilots to learn and create playbooks
• Currently scaling the approach across 1,200+ projects

Key Capabilities Enabled by Generative AI

1. Digitizing Knowledge:

   • Extracting explicit and implicit knowledge from systems, documents, and people
   • Reverse engineering code to document functionality (e.g., 50% accuracy on Assembler code)

2. Managing Applications and Platforms:

   • Automating ticket processing and root cause analysis
   • Generating insights from voice calls and other unstructured data

3. Software Development Lifecycle:

   • Generating new features and user stories faster
   • Creating architecture as code and generating UI code
   • Reverse engineering platform customizations and configurations

4. Rationalization, Modernization, and Data Engineering:

   • Comparing and finding commonalities across different applications
   • Rewriting software from one technology stack to another
   • Automating data engineering tasks

Platform Architecture

• Built on top of AWS services, utilizing foundation models like CodeWhisperer
• Consists of a data fabric, AI layer, and various use case-specific capabilities
• Provides user experiences through integration with tools like Jira and ServiceNow, as well as custom interfaces

Demonstration

1. Knowledge Digitization:

   • Reverse engineering a C-based application using a multi-step process involving different foundation models
   • Generating functional descriptions, user stories, and test cases from the reverse-engineered knowledge

2. Application Documentation:

   • Digitizing knowledge from recorded sessions and extracting standard operating procedures
   • Automating the creation of workflows for ticket resolution

3. Software Development:

   • Generating new features and user stories based on existing functionality
   • Creating functional and technical architecture artifacts as code
   • Generating code using tools like AWS CodeWhisperer

4. Application and Platform Management:

   • Enriching the CMDB with reverse-engineered and process-mapped information
   • Generating recommendations for application rationalization and modernization

Live Demonstration

• Showcasing the platform's capabilities in incident management, event ops, and workflow automation
• Demonstrating the reverse engineering of a large-scale Java application with millions of lines of code