This title slide is titled "Democratizing Beverage Field Service." It highlights leveraging Agentic AI to enhance performance and customer experience.

Leveraging Agentic AI to Enhance Performance and Customer Experience

Source: DBA Research Proposal by Tarek Boussaidi (2025)

This agenda slide outlines a presentation structure starting with Chapter 1 (Introduction to the problem, objectives, and frameworks like SECI, TAM, KBV), Chapter 2 (Literature review on AI in field service, knowledge democratization, and gaps), and Chapter 3 (Mixed-methods research methodology evaluating Agentic AI's impact). It concludes with key frameworks/contributions (efficiency, customer experience, implications) and a Conclusion (summary, limitations, future directions).

Presentation Agenda

1. Chapter 1: Introduction

Overview of problem, objectives, and theoretical foundations (SECI, TAM, KBV)

2. Chapter 2: Literature Review

Synthesis of AI in field service, knowledge democratization, and industry gaps

3. Chapter 3: Research Methodology

Mixed-methods design for evaluating Agentic AI impact on performance

4. Key Frameworks & Contributions

Expected outcomes in efficiency, customer experience, and practical implications

5. Conclusion

Summary, limitations, and future research directions Source: DBA Research Proposal: Democratizing After-Sales Field Service in the Beverage Industry

This slide is a section header for Chapter 1: Introduction, marked as section 01. It features the subtitle "AI-Powered Field Service Transformation in Beverage Industry."

Chapter 1: Introduction

01

Chapter 1: Introduction

AI-Powered Field Service Transformation in Beverage Industry

Source: DBA Research Proposal by Tarek Boussaidi (2025)

The slide addresses after-sales field service challenges in the beverage industry and how agentic AI enables knowledge democratization for technicians. Its objectives are to boost performance and customer experience, while posing research questions on AI integration effectiveness.

Key Concepts & Objectives

• After-sales field service challenges in beverage industry
• Agentic AI enables knowledge democratization for technicians
• Objectives: Boost performance and customer experience
• Research questions on AI integration effectiveness

Source: DBA Research Proposal by Tarek Boussaidi (2025)

This slide introduces Chapter 2: Literature Review, focusing on key frameworks in an AI-driven field service knowledge sharing context. It specifically explores SECI, TAM, and KBV models.

Chapter 2: Literature Review

02

Literature Review

Exploring SECI, TAM, KBV in AI-Driven Field Service Knowledge Sharing

Source: DBA Research Proposal by Tarek Boussaidi (2025)

The SECI Model by Nonaka drives knowledge spirals through socialization, externalization, combination, and internalization, key for democratizing expertise via AI in field services. TAM predicts AI adoption via perceived usefulness and ease-of-use, while KBV views knowledge as a core asset, with agentic AI enhancing field service performance and customer experience.

Theoretical Frameworks

Source: DBA Research Proposal by Tarek Boussaidi (2025)

Chapter 3 introduces the Research Methodology section. It features the subtitle "Mixed Methods Workflow for AI-Driven Field Service Enhancement."

Chapter 3: Research Methodology

Chapter 3

Research Methodology

Mixed Methods Workflow for AI-Driven Field Service Enhancement

Source: DBA Research Proposal by Tarek Boussaidi (2025)

The research design employs mixed methods, combining quantitative surveys and qualitative interviews with beverage industry case studies for validation. Key contributions include AI-enhanced frameworks for field service optimization, practical guidelines to democratize after-sales knowledge, and advances to SECI, TAM, KBV theories alongside AI-driven performance and customer experience improvements.

Research Design & Contributions

• Mixed methods: Quantitative surveys + qualitative interviews
• Beverage industry case studies for real-world validation
• AI-enhanced frameworks for field service optimization
• Practical guidelines to democratize after-sales knowledge
• Key contributions to SECI, TAM, KBV theory
• Advances in AI-driven performance and customer experience

Source: DBA Research Proposal by Tarek Boussaidi (2025)