AI-Powered Value Proposition: Revolutionizing Customer Engagement
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F25%3A101870" target="_blank" >RIV/60460709:41110/25:101870 - isvavai.cz</a>
Result on the web
<a href="https://uni.uhk.cz/hed/site/assets/files/1093/proceedings_2025_1-1.pdf" target="_blank" >https://uni.uhk.cz/hed/site/assets/files/1093/proceedings_2025_1-1.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.36689/uhk/hed/2025-01-019" target="_blank" >10.36689/uhk/hed/2025-01-019</a>
Alternative languages
Result language
angličtina
Original language name
AI-Powered Value Proposition: Revolutionizing Customer Engagement
Original language description
This study explores the integration of artificial intelligence (AI) into the Value Proposition Canvas (VPC) model, emphasizing its impact on business processes, customer need identification, and company performance. It examines AI applications across different VPC stages, focusing on technologies such as natural language processing (NLP), machine learning (ML), and generative AI. These technologies enhance customer insights, allowing businesses to develop highly personalized value propositions (VPs) and improve market responsiveness. A case study of a luxury resort illustrates how AI-driven analytics refine decision-making, optimize customer engagement, and increase the accuracy of VPs. The findings suggest that AI integration leads to improved personalization, greater strategic adaptability, and higher operational efficiency. Furthermore, AI adoption strengthens automation, streamlines data-driven optimization, and enhances customer-centric approaches, ultimately driving competitive advantage. By leveraging AI, businesses can improve targeting, enhance strategic planning, and create more efficient and responsive VPs. The study provides practical recommendations for companies seeking to integrate AI into their VP strategies, ensuring long-term success in an increasingly digital and competitive marketplace.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
50204 - Business and management
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Hradec Economic Days
ISBN
978-80-7435-855-5
ISSN
2464-6059
e-ISSN
2464-6067
Number of pages
333
Pages from-to
213-224
Publisher name
University of Hradec Králové
Place of publication
Hradec Králové
Event location
Hradec Králové
Event date
Jan 1, 2025
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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