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Course Outline

Fundamentals of Agentic AI in Business Automation

  • Defining agentic AI and its significance in modern automation
  • Surveying the tools and frameworks available for building intelligent agents
  • Enterprise applications in customer service, logistics, and marketing

Discovering Automation Opportunities

  • Analyzing current workflows to identify pain points
  • Assessing the feasibility and ROI of AI-driven automation
  • Establishing success metrics and defining integration requirements

Architecting Agentic Workflows

  • Creating task-specific and orchestration-level agents
  • Structuring prompts and logic for effective automation agents
  • Incorporating decision-making logic and exception handling

Connecting Agents to Business Systems

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

Practical Use Cases

  • Automating customer service interactions and sentiment analysis
  • Enhancing supply chain demand forecasting and vendor coordination
  • Optimizing marketing campaigns through AI-driven insights

Governance, Security, and Oversight

  • Administering access control and data sensitivity protocols
  • Configuring monitoring dashboards and alert systems
  • Reviewing and auditing automated decision processes

Practical Project: Constructing an Integrated AI Workflow

  • Selecting a target process for automation
  • Designing and deploying the AI agent
  • Conducting testing, evaluation, and optimization

Conclusion and Future Directions

Requirements

  • A foundational grasp of business workflows and process automation concepts
  • Proficiency with Python or API-based integration methods
  • Practical experience utilizing productivity and automation tools

Target Audience

  • Product managers aiming to uncover new automation opportunities
  • Automation engineers responsible for deploying AI-driven workflows
  • Business analysts focused on designing data-informed business processes
 21 Hours

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