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

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