Agentic AI for Business Leaders
About the Course:
This course is designed for business leaders, executives, and decision-makers who want to understand how Agentic AI can transform organizations. Unlike traditional automation or generative AI chatbots, agentic systems can reason, plan, use tools, and collaborate - making them powerful enablers of productivity and innovation. Through real-world frameworks, business case examples, and a hands-on capstone project, learners will gain the knowledge to spot opportunities, evaluate feasibility, manage risks, and build roadmaps for AI adoption in their organizations - without requiring coding skills.
Course Objectives:
By the end of this course, learners will be able to:
Define and explain Agentic AI and how it differs from chatbots, automation, and standard generative AI.
Assess organizational readiness by evaluating data maturity, culture, and grassroots AI usage.
Identify business problems suitable for agentic systems and avoid over-engineering for trivial or high-risk tasks.
Understand key technical concepts (hallucinations, tokens, RAG, guardrails, memory, etc.) in a business-friendly way.
Evaluate AI solutions and vendors, avoiding “agent washing” and measuring ROI, cost, and reliability.
Build an AI governance and risk framework, ensuring accountability, ethical use, and safety in adoption.
Design a practical roadmap for pilot projects and scaling agentic AI initiatives.
Apply learning in a capstone project by analyzing and enhancing an AI-powered expense reporting system.
Who is the Target Audience?
This course is intended for:
Business leaders & executives looking to align AI strategies with business goals.
Department heads & managers in IT, Operations, Finance, HR, Customer Support, etc., who want to leverage AI in workflows.
Innovation & strategy professionals are responsible for driving digital transformation.
Consultants & advisors working with organizations on AI adoption.
Non-technical leaders who want to “speak the AI language” and engage effectively with technical teams and vendors.
Basic Knowledge:
No coding or technical background required - the course is designed for business decision-makers.
Basic familiarity with AI/automation concepts (chatbots, data-driven tools) is helpful but not mandatory.
Comfort with strategic thinking and business analysis, since the focus is on applying AI to organizational contexts.
An open mindset toward innovation, experimentation, and organizational change.
Module 1: Course Overview & Introduction
- Introduction to the course and objectives
- Framing the gap between AI jargon and business decisions
- What “Agentic AI” is vs. traditional chatbots and automation
- Why organizations need agentic systems now
Module 2: Understanding Agentic AI (TQ: Agentic AI)
- Definition and scope of agentic AI
- Key differences between agentic systems and generative AI / rule-based automation
- Core properties of agents:
- Autonomy
- Decision making
- Tool use
- Planning
- Memory
- Collaboration
Module 3: Is Your Organization Ready?
- Assessing internal maturity (data, culture, experimentation readiness)
- Identifying “hidden AI champions” and grassroots AI usage in teams
- Approaches to budgeting, funding, and piloting small AI experiments
Module 4: Technical Essentials for Leaders (No Coding Required)
- Core technical concepts: hallucinations, tokens, guardrails, memory, context windows
- Basics of prompt design and retrieval-augmented generation (RAG)
- Context management and multi-step reasoning in agents
- Recognizing failure modes: where and why agents go wrong
Module 5: Spotting the Right Problems for Agentic AI
- Business problem types suited for agentic AI:
- Adaptive workflows
- Complex decision making
- Orchestration tasks
- When agentic AI is not the right fit:
- High-stakes or regulated environments
- Trivial repetitive tasks are better handled with simpler automation
- Balancing scope, safety, and risk when selecting pilots
Module 6: Evaluating Solutions & Talking Tech
- How to decode vendor claims and avoid “agent washing”
- Build vs. buy vs. partner decisions for AI adoption
- Business-relevant metrics to track:
- Performance and reliability
- Inference cost and efficiency
- Latency and response times
Module 7: Ethics, Governance & Risk
- Handling failures at scale: graceful degradation and human fallback
- Building accountability frameworks and oversight mechanisms
- Addressing risks:
- Bias and fairness
- Safety and adversarial inputs
- Abuse prevention and audit trails
Module 8: Your Agentic AI Roadmap
- Moving from pilots to scaled adoption
- Planning 90-day wins vs. long monolithic AI roadmaps
- Selecting an initial “boring but impactful” problem
- Structuring governance, stakeholder buy-in, and cross-functional teams
- Resourcing considerations for long-term sustainability
Module 9: Capstone Project - Agentic Expense Reporting System
- Analyze an existing AI-powered expense reporting system with agentic elements
- Identify enhancements:
- New agent insertion points
- Improved integration strategies
- Stronger safeguards
- Define metrics for success
- Plan fallback strategies, risk mitigation, and rollout roadmap