Optimizing Procurement Processes through AI: An Analysis of Benefits and Challenges

Thesis title: Optimizing Procurement Processes through AI: An Analysis of Benefits and Challenges
Author: Abilova, Laman
Thesis type: Diploma thesis
Supervisor: Vinš, Marek
Opponents: Jirsák, Petr
Thesis language: English
Abstract:
Despite the claims of artificial intelligence (AI) offering transformative benefits for procurement, only 6% of companies are automating their procurement processes. This study investigates the implementation of GEP SMART, an AI-powered procurement platform, across three different industries- electronics, manufacturing, and pharmaceuticals- to evaluate its effects on cost savings, efficiency, and explore the extent to which it brings challenges to organizations. Based on semi-structured interviews with 12 procurement experts that were analyzed with MAXQDA, the study reveals that the implementation of GEP SMART, an AI platform, increases real-time efficiency with reduced manual errors, automates reports, increases stakeholder management strategies, and captures all procurement steps within one centralized platform. However, the study brings to light that organizations face some struggles during the implementation, such as resistance from employees, new platform complications, training challenges, and stakeholder management. The research validates three hypotheses: (1) AI integration is associated with demonstrable efficiencies; (2) Inadequate digital infrastructure complicates the automation process; (3) success depends on change management and tailored training programs. By bridging the gap in cross-sector evidence, this study provides actionable guidance for procurement leaders in their adoption of AI, arguing that phased implementation roadmaps and cultural adaptation are key to maximizing return on their investment (ROI).
Keywords: AI; Procurement Process; Automation; GEP SMART
Thesis title: Optimizing Procurement Processes through AI: An Analysis of Benefits and Challenges
Author: Abilova, Laman
Thesis type: Diplomová práce
Supervisor: Vinš, Marek
Opponents: Jirsák, Petr
Thesis language: English
Abstract:
Despite the claims of artificial intelligence (AI) offering transformative benefits for procurement, only 6% of companies are automating their procurement processes. This study investigates the implementation of GEP SMART, an AI-powered procurement platform, across three different industries- electronics, manufacturing, and pharmaceuticals- to evaluate its effects on cost savings, efficiency, and explore the extent to which it brings challenges to organizations. Based on semi-structured interviews with 12 procurement experts that were analyzed with MAXQDA, the study reveals that the implementation of GEP SMART, an AI platform, increases real-time efficiency with reduced manual errors, automates reports, increases stakeholder management strategies, and captures all procurement steps within one centralized platform. However, the study brings to light that organizations face some struggles during the implementation, such as resistance from employees, new platform complications, training challenges, and stakeholder management. The research validates three hypotheses: (1) AI integration is associated with demonstrable efficiencies; (2) Inadequate digital infrastructure complicates the automation process; (3) success depends on change management and tailored training programs. By bridging the gap in cross-sector evidence, this study provides actionable guidance for procurement leaders in their adoption of AI, arguing that phased implementation roadmaps and cultural adaptation are key to maximizing return on their investment (ROI).
Keywords: AI; GEP SMART; Automation; Procurement Process

Information about study

Study programme: Management
Type of study programme: Magisterský studijní program
Assigned degree: Ing.
Institutions assigning academic degree: Vysoká škola ekonomická v Praze
Faculty: Faculty of Business Administration
Department: Department of Logistics

Information on submission and defense

Date of assignment: 25. 11. 2024
Date of submission: 14. 5. 2025
Date of defense: 18. 6. 2025
Identifier in the InSIS system: https://insis.vse.cz/zp/90506/podrobnosti

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