AI-driven procurement in Finnish enterprises : drivers and challenges for implementation and evolving performance metrics from a management perspective
Fernando, Chrishley (2025)
Diplomityö
2025
School of Engineering Science, Tuotantotalous
Kaikki oikeudet pidätetään.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2025072979781
https://urn.fi/URN:NBN:fi-fe2025072979781
Tiivistelmä
Adoption of Artificial Intelligence within procurement nodes is gaining popularity in many industries as enterprises seek data-driven and intelligent mechanisms to optimize operations, enhance decision-making, and cater to evolving market demands. Despite this popular trend, the strategic and tactical drivers that encourage AI adoption, as well as the practical barriers and limitations faced by organizations, remain underexplored, particularly in the Finnish business landscape. The main target of this research is to fill this gap by analyzing AI project deployments within Finnish enterprises from a management perspective.
A qualitative approach is used in this study, where structured interviews were conducted with sourcing and procurement professionals from various industries in Finland. Thematic coding was the analysis method, and NVivo analytical tool was used to understand the key drivers that drive the AI adoption and barriers which limit the project speeds and the impact, AI has on performance metrices from a management perspective. This research identified technological, organizational and environmental drivers and challengers for AI adoption within procurement domain in Finnish. When it comes to drivers, research has found out main technical drivers to be advanced AI driven demand forecasting, robust and mature ERP system foundations, comprehensive data availability and analytics, automation of repetitive tasks and intelligent risk identification & classification. Organizational drivers centered on cross functional team setup, involvement through pilot projects, supportive but cautious leadership, kaizen and data driven decision making and success stories. External drivers comprised of competitor pressure, customer pressure, sustainability reporting requirements and regulatory scrutiny.
Further the research brought in insights regarding barriers which hold the AI adoption in procurement domain in Finnish enterprises. Technical barriers include data quality and data consistency, system integration and level of user-friendliness of the applications. Mainly highlighted organizational barriers include change resistance and trust, skill gap and insufficient training, skepticism on losing job and project workload. External barriers spanned data privacy and confidentiality concerns, digital maturity of suppliers and vendor driven scope volatility.
In addition, the research shed light on supplementary nature of AI on traditional performance metrices like cycle time for procurement and routine orders, cost savings, quality KPIs, lead time, OTIF etc. and how AI has resulted in emerging performance metrices such as data completeness for critical items, AI recommendation validity rate, risk prediction accuracy and spend classification accuracy.
A qualitative approach is used in this study, where structured interviews were conducted with sourcing and procurement professionals from various industries in Finland. Thematic coding was the analysis method, and NVivo analytical tool was used to understand the key drivers that drive the AI adoption and barriers which limit the project speeds and the impact, AI has on performance metrices from a management perspective. This research identified technological, organizational and environmental drivers and challengers for AI adoption within procurement domain in Finnish. When it comes to drivers, research has found out main technical drivers to be advanced AI driven demand forecasting, robust and mature ERP system foundations, comprehensive data availability and analytics, automation of repetitive tasks and intelligent risk identification & classification. Organizational drivers centered on cross functional team setup, involvement through pilot projects, supportive but cautious leadership, kaizen and data driven decision making and success stories. External drivers comprised of competitor pressure, customer pressure, sustainability reporting requirements and regulatory scrutiny.
Further the research brought in insights regarding barriers which hold the AI adoption in procurement domain in Finnish enterprises. Technical barriers include data quality and data consistency, system integration and level of user-friendliness of the applications. Mainly highlighted organizational barriers include change resistance and trust, skill gap and insufficient training, skepticism on losing job and project workload. External barriers spanned data privacy and confidentiality concerns, digital maturity of suppliers and vendor driven scope volatility.
In addition, the research shed light on supplementary nature of AI on traditional performance metrices like cycle time for procurement and routine orders, cost savings, quality KPIs, lead time, OTIF etc. and how AI has resulted in emerging performance metrices such as data completeness for critical items, AI recommendation validity rate, risk prediction accuracy and spend classification accuracy.