Strategic Generative AI Course for Leaders

Generative AI for Leaders is designed to equip industry professionals with the foundational knowledge and practical skills needed to leverage Generative AI for strategic business advantages.

Course Overview

Generative AI for Leaders is designed to equip industry professionals with the foundational knowledge and practical skills needed to leverage Generative AI for strategic business advantages. This course dives into the applications, tools, and ethical considerations of Generative AI, empowering leaders to make informed decisions, drive innovation, and enhance productivity within their organizations.

Introduction to Generative AI for Leaders

Key Topics:

  • Defining Generative AI and its business implications
  • Difference between Generative and Discriminative AI
  • Key concepts of Large Language Models (LLMs) and Transformer Models

Virtual Lab Activity:

  • Activity 1: Strategic AI Mapping
    – Identify potential areas in your organization where Generative AI could drive value

AI Landscape and Market Trends

Key Topics:

  • Analyzing AI trends across sectors (Finance, Healthcare, Retail, Telecom)
  • Competitor landscape and emerging AI startups
  • Strategic advantages and risks of adopting Generative AI

Virtual Lab Activity:

  • Activity 2: Industry Case Analysis
    – Discuss case studies where Generative AI transformed industry operations, identify success factors and challenges

Applications of Generative AI in Business

Key Topics:

  • Exploring applications like customer support, content generation, R&D, and personalization
  • Examining real-world implementations and enterprise use cases
  • Understanding productivity and operational impacts

Virtual Lab Activity:

  • Activity 3: Use Case Identification
    – Identify high-impact areas in your industry where Generative AI can create business value

Understanding AI Agents and Automation

Key Topics:

  • Overview of AI agents, from simple task automation to advanced assistants
  • Generative AI agents in customer service, decision support, and creative applications
  • Tools and platforms supporting AI agent development

Virtual Lab Activity:

  • Activity 4: AI Agent Roleplay
    – Simulate an AI agent’s role in an organizational setting and discuss potential improvements and use cases

Introduction to Prompt Engineering for Leaders

Key Topics:

  • Concept of prompt engineering and its business relevance
  • Prompt strategies for business leaders to guide AI systems effectively
  • Practical examples in content generation and customer interactions

Virtual Lab Activity:

  • Activity 5: Crafting Effective Prompts
    – Experiment with prompts to achieve desired outputs for business scenarios, analyzing the impact of prompt modifications

Strategic Decision-Making with AI

Key Topics:

  • How Generative AI supports data-driven decisions
  • Enhancing customer insights, market research, and forecasting with AI
  • Cognitive bias mitigation with AI-driven decision frameworks

Virtual Lab Activity:

  • Activity 6: Decision-Making Simulation
    – Scenario-based activity where AI insights are used to make a strategic decision, discussing benefits and potential pitfalls

Exploring Multimodal AI Capabilities

Key Topics

  • Overview of multimodal AI (text, image, video generation) and use cases
  • Generative AI applications in content creation, advertising, and multimedia
  • Industry-specific examples from media, e-commerce, and healthcare

Virtual Lab Activity:

  • Activity 7: Brainstorming Multimodal Applications
    – Group activity to ideate multimodal AI applications within participants’ industries

Evaluating and Selecting AI Models for Business

Key Topics:

  • Key considerations when selecting AI models (vendor comparison, model efficiency)
  • Understanding model metrics (accuracy, relevance, consistency)
  • Enterprise AI tools: Hugging Face, OpenAI, Google Cloud AI

Virtual Lab Activity:

  • Activity 8: Model Evaluation Exercise
    – Participants compare sample outputs from different AI models to understand quality metrics and model selection factors

Governance and Ethics in AI Implementation

Key Topics:

  • Understanding AI governance, ethical considerations, and regulations
  • Addressing privacy, transparency, and accountability in AI use
  • Creating an ethical AI framework within the organization

Virtual Lab Activity:

  • Activity 9: Ethics Workshop
    – Discuss scenarios to identify and address ethical considerations in Generative AI applications

Data Strategy for AI Leaders

Key Topics:

  • Developing a data-centric strategy for AI implementation
  • Managing data quality, sourcing, and privacy compliance
  • Aligning data strategy with organizational goals

Virtual Lab Activity:

  • Activity 10: Data Strategy Mapping
    – Map out a data strategy that aligns with participants’ business goals and consider data sourcing and quality challenges

Enterprise Integrations for AI

Key Topics:

  • Integrating Generative AI with existing enterprise systems (CRM, ERP, etc.)
  • Leveraging APIs and tools for seamless integration
  • Change management strategies for AI adoption

Virtual Lab Activity:

  • Activity 11: Integration Blueprint
    – Outline an integration plan for introducing Generative AI into a specific enterprise function

Retrieval Augmented Generation (RAG) Overview for Business

Key Topics:

  • Integrating Generative AI with existing enterprise systems (CRM, ERP, etc.)
  • Leveraging APIs and tools for seamless integration
  • Change management strategies for AI adoption

Virtual Lab Activity:

  • Activity 11: Integration Blueprint
    – Outline an integration plan for introducing Generative AI into a specific enterprise function

Measuring ROI for AI Projects

Key Topics:

  • Defining and measuring ROI for Generative AI projects
  • KPI selection and performance monitoring for AI solutions
  • Aligning AI initiatives with financial and operational goals

Virtual Lab Activity:

  • Activity 13: ROI Assessment Exercise
    – Participants work through an ROI assessment model for a hypothetical Generative AI project

Risk Management in AI Projects

Key Topics:

  • Identifying and mitigating risks in AI projects
  • Building resilience and contingency plans for AI systems
  • Case studies on AI risk management

Virtual Lab Activity:

  • Activity 14: Risk Management Scenario
    – Discuss potential risks in an AI implementation scenario and draft mitigation strategies

Deployment and Scalability of Generative AI Solutions

Key Topics:

  • Best practices for deploying and scaling AI in business settings
  • Monitoring and optimization for ongoing performance
  • Transitioning from pilot projects to enterprise-level deployments

Virtual Lab Activity:

  • Activity 15: Scalability Planning
    – Outline a plan for scaling an AI project, covering resources, timelines, and potential challenges

Capstone Project

Key Topics:

  • Consolidating knowledge through a capstone project
  • Building a strategic roadmap for Generative AI adoption in a specific domain
  • Final presentations and feedback

Virtual Lab Activity:

  • Capstone Activity: AI Adoption Roadmap
    – Participants create a strategic AI adoption plan for their industry or business unit, covering goals, risks, ethical considerations, and expected outcomes

Fees & Enrollment

  • Course Fee: INR 49,999

Career Services

  • 100% Job Placement Support
  • Interview Practice and Preparation
  • 1 on 1 Career Mentoring Sessions
  • Career Development Workshops
  • Resume and LinkedIn Profile Enhancement

Learning Outcomes

  • Identify and assess opportunities to apply Generative AI within their organization.
  • Understand the business implications, applications, and strategic uses of Generative AI.
  • Develop a data-driven decision-making framework utilizing AI insights.
  • Manage risks, ensure compliance, and address ethical concerns in Generative AI deployment.
  • Build and evaluate an AI adoption roadmap aligned with organizational goals.

Strategic Generative AI Course for Leaders

  • Duration:

    Duration: 8 weeks

  • Course Format:

    Course Format: Online Interactive Sessions with Virtual Lab Components

  • Weekly Schedule:

    Weekly Schedule: 2-hour sessions, held twice on weekdays or weekends.

  • Certification:

    Certification: Globally Recognized Generative AI for Developers Certificate

  • Tools Used:

    Tools Used: Google Vertex AI, OpenAI, Azure OpenAI, Langflow

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You’ll get hands-on experience with industry-leading tools like Google Vertex AI, OpenAI, Azure OpenAI, and Langflow. These platforms are commonly used in Generative AI applications and will provide practical experience with real-world tools.

This course is specifically designed for business leaders and decision-makers. It focuses on the strategic applications of Generative AI, with an emphasis on real-world use cases, business impact, and ethical considerations, making it ideal for professionals looking to leverage AI within an organizational setting.

No

The course is highly interactive, with virtual lab sessions, case study discussions, group activities, and simulations. You’ll work on real-world scenarios and practical applications that encourage collaborative learning and hands-on experience.

Generative AI can drive value by improving customer experience, automating repetitive tasks, enhancing content generation, and enabling data-driven decision-making. This course will help you identify specific applications within your industry, assess the potential impact, and implement AI solutions effectively.

The course covers a variety of industries, including finance, insurance, healthcare, retail, telecom, e-commerce, and media. These case studies provide insights into how Generative AI is transforming different sectors, giving you ideas and examples to apply in your own field.

You’ll have access to a dedicated course facilitator and a support team for any questions. We also provide a peer discussion board where you can connect with fellow participants, share insights, and get feedback.

Yes, there will be regular assignments, lab activities, and a capstone project. These assessments are designed to reinforce learning and ensure that you can apply the knowledge gained in practical scenarios.

One of the modules focuses specifically on AI governance, ethics, and compliance. You’ll learn about data privacy, transparency, accountability, and ethical decision-making frameworks, which are essential for responsible AI implementation in any organization.

What career benefits can I expect after completing this course?
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