Technology Acceleration
The purpose of this workshop is to educate the Business Analysts, Developers, Testers and deployment teams to adopt AI within
each stage of the SDLC to improve velocity of development whilst ensuring safety & security. We align our workshops’ content
with your team’s current AI adoption maturity to ensure maximum impact
Build-as-you-Learn : Lab-based learning
Role-Tailored : Modular Micro-Sessions
Deep Learning : Live Co-Creation
Multi-tool exposure & cross-role collaboration
Delivery Format : Online or Onsite or Hybrid (your choice)
Duration: 1 day (can be split across multiple days as preferred)
AI-Boosted Business Analysis & Req Engineering
AI can dramatically speed up how business requirements are collected, interpreted, and converted into usable technical documentation. By using AI as a co-analyst, teams reduce
ambiguity and produce clearer, more complete specifications.
Faster discovery = faster delivery.
Sub-Topics:
•Using AI to convert business conversations into structured requirements
• Generating user stories, acceptance criteria, and workflow descriptions
• Creating AS-IS and TO-BE process maps with AI tools
• AI-assisted impact analysis and requirement gap detection
• Automating documentation and requirements traceability
AI-Assisted Solution & System Design
AI tools help architects and
developers create cleaner,
well-structured designs in a
fraction of the time. From
diagrams to design reviews, AI
ensures consistency and reduces
architectural blind spots.
Sub-Topics:
• Generating architecture
diagrams, ERDs, and sequence
diagrams using AI
• Using AI to explore alternative
design patterns and
architectural options
• Converting requirements into
API specifications and data
models
• Automated NFR analysis
(security, performance,
scalability)
• AI-powered reviews to detect
design gaps early
Accelerated Development with AI Coding Assistants
Modern IDE-integrated AI tools
unlock massive productivity by
generating, refactoring, and
reviewing code. Developers can
move faster while maintaining
quality and security. AI becomes
the ultimate coding partner.
Sub-Topics:
• AI-powered code generation
for backend, frontend, and IaC
• Refactoring legacy code with
structured AI prompts
• Debugging and performance
optimization using AI
suggestions
• Creating mocks, stubs, and
sample data automatically
• Secure coding practices with
AI-driven code reviews
Next-Gen Testing & Release Automation Using AI
AI accelerates testing and
DevOps by automating repetitive
tasks and improving coverage.
Teams can deliver faster while
increasing reliability. AI closes the
gap between development and
production.
Sub-Topics:
• Generating unit, integration,
API, and UI test cases from
requirements
• Using AI to create automated
test scripts (Playwright,
Selenium, Cypress)
• AI-assisted analysis of test
failures and build logs
• Auto-generating CI/CD YAML
pipelines (GitHub Actions, Azure
DevOps, GitLab)
• AI-guided deployment
strategies and environment
configuration
Duration: 1.5 days (can be split across multiple days as preferred)
Code Architecture for AI Systems
AI can dramatically speed up how business requirements are collected, interpreted, and converted into usable technical documentation. By using AI as a co-analyst, teams reduce
ambiguity and produce clearer, more complete specifications.
Faster discovery = faster delivery.
Sub-Topics:
•Using AI to convert business conversations into structured requirements
• Generating user stories, acceptance criteria, and workflow descriptions
• Creating AS-IS and TO-BE process maps with AI tools
• AI-assisted impact analysis and requirement gap detection
• Automating documentation and requirements traceability
Cost Optimization & Token Economics
AI tools help architects and
developers create cleaner,
well-structured designs in a
fraction of the time. From
diagrams to design reviews, AI
ensures consistency and reduces
architectural blind spots.
Sub-Topics:
• Generating architecture
diagrams, ERDs, and sequence
diagrams using AI
• Using AI to explore alternative
design patterns and
architectural options
• Converting requirements into
API specifications and data
models
• Automated NFR analysis
(security, performance,
scalability)
• AI-powered reviews to detect
design gaps early
Prompt Engineering/ Using Prompts Effectively
Modern IDE-integrated AI tools
unlock massive productivity by
generating, refactoring, and
reviewing code. Developers can
move faster while maintaining
quality and security. AI becomes
the ultimate coding partner.
Sub-Topics:
• AI-powered code generation
for backend, frontend, and IaC
• Refactoring legacy code with
structured AI prompts
• Debugging and performance
optimization using AI
suggestions
• Creating mocks, stubs, and
sample data automatically
• Secure coding practices with
AI-driven code reviews
Security Awareness for Developers
AI accelerates testing and
DevOps by automating repetitive
tasks and improving coverage.
Teams can deliver faster while
increasing reliability. AI closes the
gap between development and
production.
Sub-Topics:
• Generating unit, integration,
API, and UI test cases from
requirements
• Using AI to create automated
test scripts (Playwright,
Selenium, Cypress)
• AI-assisted analysis of test
failures and build logs
• Auto-generating CI/CD YAML
pipelines (GitHub Actions, Azure
DevOps, GitLab)
• AI-guided deployment
strategies and environment
configuration
Agentic AI & Tool Calling
AI can dramatically speed up how business requirements are collected, interpreted, and converted into usable technical documentation. By using AI as a co-analyst, teams reduce
ambiguity and produce clearer, more complete specifications.
Faster discovery = faster delivery.
Sub-Topics:
•Using AI to convert business conversations into structured requirements
• Generating user stories, acceptance criteria, and workflow descriptions
• Creating AS-IS and TO-BE process maps with AI tools
• AI-assisted impact analysis and requirement gap detection
• Automating documentation and requirements traceability
AI-Assisted Solution & System Design
AI tools help architects and
developers create cleaner,
well-structured designs in a
fraction of the time. From
diagrams to design reviews, AI
ensures consistency and reduces
architectural blind spots.
Sub-Topics:
• Generating architecture
diagrams, ERDs, and sequence
diagrams using AI
• Using AI to explore alternative
design patterns and
architectural options
• Converting requirements into
API specifications and data
models
• Automated NFR analysis
(security, performance,
scalability)
• AI-powered reviews to detect
design gaps early
Accelerated Development with AI Coding Assistants
Modern IDE-integrated AI tools
unlock massive productivity by
generating, refactoring, and
reviewing code. Developers can
move faster while maintaining
quality and security. AI becomes
the ultimate coding partner.
Sub-Topics:
• AI-powered code generation
for backend, frontend, and IaC
• Refactoring legacy code with
structured AI prompts
• Debugging and performance
optimization using AI
suggestions
• Creating mocks, stubs, and
sample data automatically
• Secure coding practices with
AI-driven code reviews
Next-Gen Testing & Release Automation Using AI
AI accelerates testing and
DevOps by automating repetitive
tasks and improving coverage.
Teams can deliver faster while
increasing reliability. AI closes the
gap between development and
production.
Sub-Topics:
• Generating unit, integration,
API, and UI test cases from
requirements
• Using AI to create automated
test scripts (Playwright,
Selenium, Cypress)
• AI-assisted analysis of test
failures and build logs
• Auto-generating CI/CD YAML
pipelines (GitHub Actions, Azure
DevOps, GitLab)
• AI-guided deployment
strategies and environment
configuration