AI Customer Experience: Service, Journeys and Retention

Design AI-assisted journeys, support systems, personalization, service recovery, and customer retention

Created byDr. Law Payne

Last updated 10/2026

English

What you'll learn

- Build a practical AI Customer Experience strategy connected to meaningful business outcomes.
- Diagnose workflows, evidence gaps, assumptions, risks, and decision bottlenecks.
- Use AI for research, analysis, generation, and coordination without delegating accountability.
- Apply human review, verification, privacy, and quality controls at the right moments.
- Measure efficiency, quality, user response, business value, and organizational learning.
- Develop a ninety-day roadmap for implementation, governance, and responsible scale.

Course content

1 section • 5 lectures • 49m total length

The AI-Assisted Customer Experience System9:44
This lecture explains customer outcomes, journey architecture, service economics, AI role, trust. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Journey Research and Voice-of-Customer Intelligence9:50
This lecture explains journey evidence, contact drivers, sentiment limits, pain-point prioritization, customer segments. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Service Automation and Human Escalation9:52
This lecture explains intent design, knowledge quality, response boundaries, escalation logic, quality assurance. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Personalization Service Recovery and Retention9:55
This lecture explains contextual personalization, recovery principles, churn signals, next-best action, customer agency. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Measurement Governance and the CX Roadmap9:39
This lecture explains experience metrics, operational metrics, retention economics, privacy, implementation roadmap. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.

Requirements

- No coding experience is required unless a lecture explicitly demonstrates a technical workflow.
- Access to relevant AI tools is helpful but not required to understand the frameworks.
- Bring a real business, client, team, project, or professional objective for the exercises.
- Be willing to test assumptions and verify AI-generated claims.

Description

This course contains the use of artificial intelligence.

Artificial intelligence creates value when it improves a real decision, workflow, or customer outcome. AI Customer Experience: Service, Journeys and Retention provides a structured system for professionals who want to use AI to improve service speed and consistency while protecting empathy, trust, and customer choice.

This course is designed for customer experience leaders, service managers, marketers, founders, operations professionals, and consultants. It moves beyond disconnected prompts and tool demonstrations. Students learn how to define outcomes, organize evidence, design practical workflows, evaluate quality, preserve human accountability, and connect AI activity to measurable results.

Across five substantial lectures, Dr. Law Payne explains the strategic logic and practical execution of AI Customer Experience. Every lecture includes a decision framework, a continuing business case, quality controls, implementation guidance, and an assignment that learners can apply to an organization, client, career, or business.

The course emphasizes responsible practice. Students learn where AI can accelerate research, analysis, generation, and coordination, as well as where privacy, bias, accuracy, security, or reputation require stronger safeguards. The aim is not maximum automation. The aim is better performance with visible ownership.

Dr. Law Payne is a business educator, growth strategist, nine-figure entrepreneur, published author, and Doctor of Business Administration specializing in Organizational Leadership and Development. His experience includes more than one billion dollars in managed sales and more than fifteen years of teaching, coaching, and mentoring across business, psychology, leadership, fitness, and education.

By the end of the course, students will have a working AI Customer Experience operating system and a ninety-day plan for disciplined implementation.

Who this course is for:

- Customer experience leaders, service managers, marketers, founders, operations professionals, and consultants.
- Professionals who want structured workflows instead of prompt collections.
- Teams seeking practical AI adoption with human accountability.
- Learners who want to connect AI activity to customer, operational, or financial outcomes.