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Course Outline

Introduction to Agentic AI in Business Automation

  • Understanding agentic AI and its significance in automation
  • An overview of tools and frameworks for creating intelligent agents
  • Enterprise applications: customer service, logistics, and marketing

Identifying Automation Opportunities

  • Mapping existing workflows and identifying pain points
  • Assessing feasibility and return on investment for AI-driven automation
  • Defining success metrics and integration requirements

Designing Agentic Workflows

  • Structuring task-specific agents and orchestration-level agents
  • Prompt engineering and logic structuring for automation agents
  • Incorporating decision-making processes and exception handling

Integrating Agents with Business Systems

  • Linking AI agents to CRMs, ERPs, and communication platforms
  • Leveraging Zapier, Make, or Power Automate for orchestration
  • Executing API-based integrations using Python

Applied Use Cases

  • Automating customer service interactions and sentiment analysis
  • Supply chain demand forecasting and vendor coordination
  • Optimising marketing campaigns with AI-driven insights

Governance, Security, and Monitoring

  • Managing access control and data sensitivity
  • Configuring monitoring dashboards and alert systems
  • Evaluating and auditing automated decisions

Hands-on Project: Building an Integrated AI Workflow

  • Selecting a target process for automation
  • Designing and implementing the AI agent
  • Testing, evaluation, and optimisation

Summary and Next Steps

Requirements

  • A fundamental understanding of business workflows and process automation
  • Familiarity with Python or API-based integrations
  • Practical experience with productivity or automation tools

Target Audience

  • Product managers looking to identify automation potential
  • Automation engineers focused on implementing AI-driven workflows
  • Business analysts designing data-informed business processes
 21 Hours

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