This title slide, titled "Democratizing After-Sales Field Service," introduces a research proposal summary on leveraging Agentic AI to enhance performance and customer experience in after-sales field service. It is presented by Tarek Boussaidi.

Democratizing After-Sales Field Service

Leveraging Agentic AI to Enhance Performance and Customer Experience

By Tarek Boussaidi

Research Proposal Summary

Source: Research Proposal by Tarek Boussaidi

The presentation agenda outlines five key sections: Introduction & Chapter 1 on overview and challenges, Chapter 2 on literature review covering agentic AI and research gaps, Chapter 3 on methodology including mixed-methods and ethics, visual frameworks like AI democratization and SECI model, and Conclusion & Implications with findings and future directions. This structure provides a clear roadmap from background and analysis to contributions and next steps.

Presentation Agenda

1. Introduction & Chapter 1: Overview and Challenges

Background, problem statement, aims, objectives, and research questions.

2. Chapter 2: Literature Review

Agentic AI, service transformation, adoption factors, and research gaps.

3. Chapter 3: Methodology

Mixed-methods design, data sources, analysis, and ethical considerations.

4. Visual Frameworks

AI democratization, SECI model, and integrated theoretical framework.

5. Conclusion & Implications

Summary of findings, contributions, and future research directions. Source: Research Proposal: Democratizing After-Sales Field Service in the Beverage Industry

This section header slide introduces Chapter 1 titled "Introduction," marked as section 01. It outlines key elements including Overview, Background, Challenges, Problem Statement, Aims & Objectives.

Chapter 1 – Introduction

01

Chapter 1 – Introduction

Overview, Background, Challenges, Problem Statement, Aims & Objectives

Source: Research Proposal: Democratizing After-Sales Field Service in the Beverage Industry

Chapter 1 addresses field service challenges in the beverage industry, particularly knowledge silos and inefficiencies. It aims to democratize knowledge using Agentic AI, outlines research questions and hypotheses, and highlights significance for enhanced performance and customer experience.

Key Elements of Chapter 1

• Field service challenges in beverage industry
• Problem: Knowledge silos and inefficiencies
• Aims: Democratize knowledge via Agentic AI
• Research questions and hypotheses
• Significance: Enhanced performance and CX

Source: Research Proposal Summary

This section header slide introduces Chapter 2 on the Literature Review. It highlights key topics including Agentic AI, After-Sales Transformation, Knowledge Democratization, Adoption Factors, and the SECI-TAM-KBV Frameworks.

Chapter 2 – Literature Review

02

Literature Review

Agentic AI, After-Sales Transformation, Knowledge Democratization, Adoption Factors, SECI-TAM-KBV Frameworks

Source: Research Proposal: Democratizing After-Sales Field Service in the Beverage Industry

Agentic AI transforms after-sales service via the SECI model for knowledge democratization, with TAM and KBV explaining adoption through organizational readiness, infrastructure, and human capabilities. Research gaps include scarce studies on beverage industry field service and unresolved AI challenges like access equity, integration barriers, and performance impacts.

Literature Highlights & Gaps

This section header slide introduces Chapter 3 on Research Methodology. It highlights key elements including mixed-methods design, data sources, sampling, analysis, and ethics.

Chapter 3 – Research Methodology

Chapter 3

Research Methodology

Mixed-Methods Design, Data Sources, Sampling, Analysis, and Ethics

Source: Research Proposal: Democratizing After-Sales Field Service in the Beverage Industry

The workflow outlines a methodology starting with AI Democratization, leveraging Agentic AI for field service and knowledge sharing, followed by SECI Model phases: Socialization (tacit-to-tacit via observations and interactions), Externalization (tacit-to-explicit documentation for AI models), and Combination/Internalization (explicit integration into workflows via AI analytics). It culminates in an Integrated Framework synthesizing TAM/SECI/KBV with mixed-methods validation for a holistic Agentic AI approach.

Methodology & Frameworks

Source: Chapter 3 – Research Methodology

Agentic AI transforms field service, bridging literature gaps and revolutionizing beverage industry operations. The slide thanks the audience, invites Q&A, and calls for discussions on AI implementation opportunities.

Conclusion & Next Steps

• Summary: Agentic AI transforms field service

• Contributions: Bridges gaps in literature
• Implications: Revolutionizes beverage industry operations
• Q&A****

Thank you!

Closing: Empowering field service with AI. Call-to-action: Let's discuss implementation opportunities.

Source: Research Proposal: Democratizing After-Sales Field Service in the Beverage Industry