Generative AI for IT Executives
Duration
5 Days
Level
Advanced Level
Design and Tailor this course
As per your team needs
Overview
This strategic, hands-on course equips IT executives with a comprehensive understanding of how Generative AI (GenAI) can be leveraged across all phases of the software development lifecycle. Participants will explore AI strategy formulation, use case identification, ROI analysis, feasibility planning, and responsible AI implementation. The course also covers GenAI tooling, open-source/cloud options, and risk governance, enabling leaders to make confident, informed decisions on GenAI adoption and delivery.
Audience
- Senior IT Leaders (CIOs, CTOs, Heads of Engineering, AI Champions)
- Responsible for defining, validating, and leading GenAI initiatives for client organizations
- Not expected to code but must guide technical teams and engage clients on strategy, feasibility, and delivery
Prerequisites
- Familiarity with software development lifecycle (SDLC) and IT project delivery models
- Conceptual understanding of AI/ML (no coding required)
- Experience in leading client-facing solution design or technology initiatives
- Interest in strategic technology adoption and digital transformation
Curriculum
Understand how to align GenAI with client business needs and internal delivery strategy.
- AI landscape: GenAI vs traditional AI
- GenAI opportunities across industries
- Use case identification
- ROI and Feasibility assessment: technical, data, and cost
- Multi-cloud and open-source GenAI tooling overview
- Building an Enterprise Grade GenAI Application
- Hands-on/Demo: Low Code/No Code Platform to build enterprise grade applications
Provide a clear, high-level understanding of GenAI concepts, capabilities, and terminology to align all participants and enable strategic conversations with clients and teams.
- Large Language Models (LLMs), foundation models, and fine-tuning
- Retrieval-Augmented Generation (RAG) explained simply
- Prompt engineering fundamentals: zero-shot, few-shot, and chain-of-thought prompting
- Hallucinations, grounding, context window, token usage – what these mean for project decisions
- Overview of the GenAI tech landscape: OpenAI, Anthropic, Google, HuggingFace, Azure, AWS
- Factors in choosing LLMs
- Demo: Prompt engineering in action – how output quality changes with prompt structure
Visualize and plan GenAI integration across project phases.
- GenAI in Requirements: user stories → test cases
- GenAI in Design: architecture generation, flow diagrams
- GenAI in Development: code generation, pair programming assistants (e.g., GitHub Copilot, Amazon CodeWhisperer)
- GenAI in Testing: self-healing tests, anomaly detection, log analysis
- GenAI in Deployment & Monitoring: prompt safety, change management, LLMOps basics
- Demo-Based Learning: AI-generated test scenarios, self-healing script use case, DevOps + AI agent flow
Build an internal delivery model and evaluate tooling/tech stack readiness
- Project lifecycle for GenAI: from POC to production
- Required team roles: AI product manager, prompt engineers, QA with GenAI, LLMOps
- Tooling comparison: OpenAI, AWS Bedrock, Azure OpenAI, Google GenAI, LangChain, LlamaIndex
- Finetuning vs RAG: which strategy, when and why
- Vendor and model evaluation
- Demo: RAG-based chatbot using LangChain or Azure OpenAI
- Exercise: Role-mapping and tech stack planning for an internal GenAI CoE
Prepare for safe, ethical, and compliant GenAI deployment
- Limitations of LLMs and hallucinations
- Prompt security and data privacy
- Bias detection, auditability, and explainability
- Compliance frameworks (AI Act, GDPR, ISO/IEC 42001, etc.)
- Governance checklist: model evaluation, red teaming, safety testing
- Brainstorming Session: Risk scenarios and mitigation strategies
- Demo: Prompt guardrails using open-source filters or Azure Promptflow
Duration
5 Days
Level
Advanced Level
Design and Tailor this course
As per your team needs
