Generative AI for Leaders
Duration
3 Days (8 hours per day)
Level
Advanced Level
Design and Tailor this course
As per your team needs
This immersive, strategic course equips delivery leaders with the tools, insights, and frameworks to integrate Generative AI (GenAI) effectively into software delivery organizations. Designed specifically for senior managers and executives, the program focuses on real-world applications of GenAI across the SDLC, ethical adoption, tooling evaluation, team enablement, and roadmap planning.
Participants will:
- Explore enterprise-relevant use cases across the delivery lifecycle
- Evaluate tools and platforms (build vs buy, cloud vs open-source)
- Understand ethical, legal, and compliance risks of GenAI in delivery
- Learn how to scale GenAI responsibly across teams and portfolios
- Leave with a tailored 30/60/90-day roadmap for action
- Senior Delivery Managers
- Portfolio and Program Managers
- Enterprise Delivery Heads and Transformation Leaders
- Client Engagement Managers and Delivery Excellence Teams
- Change Agents leading innovation or AI adoption
Topics Covered:
- Introduction, course objectives, and participant expectations
- The evolution of GenAI: From rule-based bots to LLM-powered agents
- Key technologies: LLMs, prompts, multimodality, retrieval-augmented generation
- GenAI vs traditional AI in delivery environments
- Role transformation: New-age Delivery Manager mindset
- Industry trends: GenAI adoption in enterprise delivery functions
- Case study and Demonstrations of GenAI use cases (e.g., planning assistant)
Interactive Elements:
- Group Activity: “What could GenAI mean for our delivery organization?”
- Discussion: Real-life challenges GenAI could address
Outcome:
Participants gain a foundational understanding of GenAI and its strategic relevance in delivery management.
Topics Covered:
- Overview of the Software Delivery Lifecycle (SDLC) and its pain points
- Mapping GenAI use cases across delivery stages:
- Planning and estimation
- QA and defect triaging
- Documentation and reporting
- Stakeholder communication
- Risk tracking and retrospectives
- Prioritizing use cases: ROI, complexity, feasibility
- Case studies and tool demonstrations
Interactive Elements:
- Team Exercise: Map GenAI to current delivery projects and workflows
Outcome:
Participants identify specific high-impact areas within their delivery functions where GenAI can create value.
Topics Covered:
- AI ethics in delivery environments: Hallucination, bias, explainability
- Working with client and proprietary data: Privacy, IP, and regulatory considerations
- Shadow AI and prompt injection: Risk examples in delivery workflows
- Creating GenAI usage guidelines and policy guardrails
- Red-teaming GenAI in delivery settings
- Audit, logging, and incident response in AI-powered tools
Interactive Elements:
- Exercise: Draft a “Responsible GenAI Use Policy” for delivery teams
- Discussion: Common pitfalls in GenAI governance
Outcome:
Participants develop awareness of the guardrails needed for secure, compliant, and ethical GenAI adoption.
Topics Covered:
-
- Change management for GenAI introduction
- Overcoming resistance and fear of automation
- Delivery team enablement: Upskilling plans, CoE models, and experimentation charters
- Organizational maturity models for GenAI adoption
- Stages: Exploration → Experimentation → Integration → Institutionalization
- Optional Segment: Agentic AI – task orchestration, co-pilots, and autonomous agents in delivery
Interactive Elements:
- Facilitated Workshop: Each participant maps 2–3 GenAI opportunity areas in their team
- Group Reflection: Barriers and accelerators to team-wide adoption
Outcome:
Participants are equipped to begin piloting GenAI tools and drive adoption across their teams.
Topics Covered:
- Mapping delivery pain points to GenAI opportunities
- Aligning GenAI initiatives with client and business strategy
- Tooling discussion: Open-source (LangChain, LlamaIndex) vs cloud tools (Vertex AI, Azure OpenAI)
- Tool selection frameworks: build vs buy, integration strategies
- GenAI Operating Models and pilot project frameworks
- Delivery-centric AI Strategy and Governance Blueprint
- Metrics and KPIs for AI adoption success
- Key challenges and mitigation strategies
Capstone Activity:
- Build a personalized 30/60/90-Day GenAI Roadmap
- Identify 2 pilot projects
- Define success criteria and checkpoints
- Outline team onboarding and enablement plan
Outcome:
Participants walk away with a strategic plan for implementing GenAI across their delivery portfolio.
Topics Covered:
- Overview of low/no-code GenAI platforms (e.g., Microsoft Copilot Studio, OpenAI playground, Zapier AI)
- Building delivery assistants, risk analyzers
Hands-On Simulations:
- GenAI use cases implementation using Dataiku
- GenAI Chatbot implementation using private knowledgebase
Outcome:
Participants gain firsthand experience creating working GenAI prototypes using simple tools – without needing to code.
Topics Covered:
- AI Agents – What & Why?
- AI Agent vs Agentic AI
- Agentic AI Design Patterns
- Key Agentic AI Platforms
Hands-On Simulations:
- Building AI Agents using n8n
- Building AI Agents using Dataiku
- Experience in software delivery management or portfolio leadership
- Basic familiarity with AI/ML concepts is useful but not mandatory
- Interest in technology transformation, change management, and innovation in delivery
