Umjetna inteligencija u funkciji prediktivne analitike

master's thesis

• Title (english): Artificial intellignece in the function of predictive analytics
• Author: Leon David Kocurek
• Mentor: Ena Pecina (mentor)
• Committee member: Zrinka Lovretin Golubić (predsjednik povjerenstva)
• Committee member: Andrija Sabol (član povjerenstva)
• Committee member: Ena Pecina (član povjerenstva)
• Granter: University of Zagreb; Faculty of Economics and Business; (Department of Managerial Economics); Zagreb
• Defense date and country: 2024-09-19, Croatia
• Scientific / art field, discipline and subdiscipline: SOCIAL SCIENCES; Economics; Organization and Management

Abstract

U suvremenom tehnološkom okruženju, primjena umjetne inteligencije (UI) u prediktivnoj analitici predstavlja revolucionarni pristup za anticipiranje budućih trendova i ponašanja u različitim industrijskim sektorima. Ovaj rad istražuje kako poduzeća implementiraju UI tehnologije za poboljšanje prediktivne analitike, s ciljem optimizacije poslovnih operacija i donošenja informiranih odluka. Unatoč brzom razvoju UI i njezinoj širokoj primjeni, izazovi u integraciji i korištenju UI za prediktivnu analitiku ostaju značajni. Istraživanje identificira ključne faktore koji utječu na usvajanje UI, analizira postojeće pristupe i strategije te predlaže preporuke za efikasniju integraciju UI u poslovne procese. Kroz anketu među menadžerima, rad pruža uvid u stvarne doživljaje i stavove prema korištenju UI, doprinoseći boljem razumijevanju potencijala i izazova umjetne inteligencije u prediktivnoj analitici.

Abstract (english)

In today's rapidly advancing technological landscape, the application of Artificial Intelligence (AI) in predictive analytics marks a revolutionary approach to anticipating future trends and behaviors across various industrial sectors. This paper explores how enterprises implement AI technologies to enhance predictive analytics, aiming to optimize business operations and make informed decisions. Despite the swift development of AI and its widespread application, challenges in AI integration and utilization for predictive analytics remain significant. The research identifies key factors influencing the adoption of AI, analyzes existing approaches and strategies, and suggests recommendations for more efficient AI integration into business processes. Through a survey among managers, the paper provides insights into real experiences and attitudes towards using AI, contributing to a better understanding of the potential and challenges of artificial intelligence in predictive analytics

• Keywords: umjetna inteligencija; prediktivna analitika; donošenje odluka; izazovi UI
• Keywords (english): artificial intelligence; predictive analytics; decision-making; AI challenges
• Language: croatian
• URN:NBN: urn:nbn:hr:148:983860
• Study programme: Title: Business economics; Study programme type: university; Study level: integrated undergraduate and graduate; Academic / professional title: sveučilišni magistar ekonomije/sveučilišna magistra ekonomije (sveučilišni magistar ekonomije/sveučilišna magistra ekonomije)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Access restricted to students and staff of home institution
• Created on: 2024-09-22 17:42:57