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AI Agents for Business: Automate Work with Control
New
11 students

AI Agents for Business: Automate Work with Control

Design AI agent workflows with human approvals, reliable measurement, and responsible business automation
Created byDr. Law Payne
Last updated 10/2026
English

What you'll learn

  • Explain the strategic and operational foundations of AI agents for business.
  • Apply a five-part decision framework to a realistic business problem.
  • Evaluate evidence, assumptions, quality, risk, and human accountability.
  • Design an implementation plan with ownership, controls, and measurable outcomes.

Course content

2 sections • 12 lectures • 1h 50m total length

  • The Business Case for AI Agents10:25

    Learning objective: Explain and apply agent versus assistant, task economics, workflow boundaries, value hypothesis, executive ownership in a realistic professional or business context.


    Lecture description:


    This lecture explains agent versus assistant, task economics, workflow boundaries, value hypothesis, executive ownership. Students apply the ideas to a realistic business decision, examine common failure modes, and complete a practical exercise they can use immediately.

  • Design the Agent Workflow10:30

    Learning objective: Design and apply trigger and objective, context and memory, tool access, decision logic, completion criteria in a realistic professional or business context.


    Lecture description:


    This lecture explains trigger and objective, context and memory, tool access, decision logic, completion criteria. Students apply the ideas to a realistic business decision, examine common failure modes, and complete a practical exercise they can use immediately.

  • Human Approval and Control10:09

    Learning objective: Evaluate and apply risk tiers, approval gates, exception handling, audit trail, shutdown authority in a realistic professional or business context.


    Lecture description:


    This lecture explains risk tiers, approval gates, exception handling, audit trail, shutdown authority. Students apply the ideas to a realistic business decision, examine common failure modes, and complete a practical exercise they can use immediately.

  • Measure Reliability Cost and Value10:20

    Learning objective: Analyze and apply quality metrics, latency and cost, business outcome, failure analysis, continuous improvement in a realistic professional or business context.


    Lecture description:


    This lecture explains quality metrics, latency and cost, business outcome, failure analysis, continuous improvement. Students apply the ideas to a realistic business decision, examine common failure modes, and complete a practical exercise they can use immediately.

  • Deploy Scale and Govern Agentic Work10:31

    Learning objective: Evaluate and apply pilot design, change adoption, portfolio governance, vendor decisions, ninety-day roadmap in a realistic professional or business context.


    Lecture description:


    This lecture explains pilot design, change adoption, portfolio governance, vendor decisions, ninety-day roadmap. Students apply the ideas to a realistic business decision, examine common failure modes, and complete a practical exercise they can use immediately.

  • Decompose the Goal into Observable Agent Steps6:35

    Break a business briefing goal into observable steps, inputs and acceptance conditions. Resolve client identity before combining retrieval results, preserve missing evidence and stop before unauthorized external communication. Complete the agent-step worked case.

Requirements

  • No coding experience is required. Access to common productivity software is helpful but not required.
  • - A willingness to apply the frameworks to a real or realistic business situation.
  • - Access to common productivity software is helpful but not required.

Description

This course contains the use of artificial intelligence.


AI Agents for Business: Automate Work with Control gives professionals a structured system to identify valuable agent opportunities and implement controlled automation that improves work without hiding accountability.


The course is designed for executives, managers, business owners, consultants, operations leaders, and transformation professionals. It moves beyond disconnected tips and tool demonstrations. Students learn how to define the business decision, organize evidence, design a practical workflow, evaluate quality, preserve human accountability, and connect activity to measurable results.


The continuing case follows a professional services company where client intake, research, proposal development, delivery tracking, and follow-up depend on disconnected manual work. Every lecture includes a management framework, application guidance, quality controls, and an assignment learners can use in an organization, client engagement, career, or business.


Dr. Law Payne combines doctoral-level leadership education with substantial entrepreneurial, marketing, and teaching experience. He holds a Doctor of Business Administration specializing in Organizational Leadership and Development, a master’s degree in Educational Psychology, a master’s degree in Business, and a bachelor’s degree in Business Administration and Marketing. His professional experience includes nine-figure entrepreneurship, published work reaching more than one million copies, and growth leadership involving more than one billion dollars in managed sales.

Who this course is for:

  • Executives, managers, business owners, consultants, operations leaders, and transformation professionals seeking responsible AI automation.
  • - Professionals who want business value and responsible implementation rather than surface-level hype.
  • - Professionals who want a structured, evidence-based approach instead of disconnected tips or tool demonstrations.
  • - Leaders and learners who want to preserve human accountability while using artificial intelligence.

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