ALISMIA AI as a Tool for Digital Empowerment: Redesigning Client Interaction in Beauty Businesses
Abstract
Objectives: This study examines how the ALISMIA AI platform - a management system purpose-built for professional beauty businesses - changes the way salons and studios interact with their clients, and what that change means for trust, satisfaction, and commercial performance. The investigation proceeds from a straightforward empirical observation: beauty businesses that deploy this platform consistently report outcomes that exceed what administrative automation alone should produce, suggesting that the mechanism of impact is relational rather than merely operational.
Methods: A convergent mixed-methods design was adopted. Survey data were collected from 347 clients and 89 business owners across Ukraine, Poland, Germany, France, Italy, and the Netherlands. Quantitative relationships were estimated through structural equation modeling (SEM). Ten business owners who had used the platform for at least six months participated in semi-structured interviews analyzed via reflexive thematic analysis.
Results: Personalization quality predicted client trust (β = 0.61, p < .001) and satisfaction (β = 0.54, p < .001), but the strength of these relationships depended substantially on whether clients felt they retained meaningful influence over how they were served. Business owners reported reductions in appointment non-attendance averaging 38%, increases in repeat bookings of 29%, and improvements in internal satisfaction scores of approximately 25%. Breadth of feature adoption and duration of use were stronger predictors of performance gains than business size or geographic location.
Conclusions: The most consequential effect of ALISMIA AI in the businesses studied is neither scheduling efficiency nor automated marketing - it is the conversion of individually held practitioner knowledge into a shared organizational asset. This transformation addresses what is arguably the deepest structural vulnerability of small independent beauty enterprises: the fragility of client relationships that depend on the memory and continued presence of a single person. The implications extend to debates about service authenticity, the organizational conditions of digital empowerment, and the design of client-facing technology in high-relational service sectors.
Keywords
References
Similar Articles
- Dr. Andika Prasetyo, Siti Rahmawati, M.Sc., Rizky Maulana, Structured Teaching Framework Focused on Beginner-Level Software Development Skills , International Journal of Modern Computer Science and IT Innovations: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Svetlana Petrova, Beyond Hyperscale: The Socio-Technical Adaptation of Site Reliability Engineering for Enhanced Resilience in Critical Infrastructure , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 11 (2025): Volume 02 Issue 11
- Dr. Jonathan Miller, Dr. Emily Carter, A Deep Learning-Based Biometric Authentication Architecture for Banking Fraud Prevention Using Google Teachable Machine and Facial Recognition Analytics , International Journal of Modern Computer Science and IT Innovations: Vol. 3 No. 05 (2026): Volume 03 Issue 05
- John Doe, Transforming Supply Chain Management Through Artificial Intelligence: A Holistic Theoretical Analysis , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 09 (2025): Volume 02 Issue 09
- Prof. Elise Vandermark, INTEGRATING LAKEHOUSE ARCHITECTURES AND CLOUD DATA WAREHOUSING FOR NEXT-GENERATION ENTERPRISE ANALYTICS , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 12 (2025): Volume 02 Issue 12
- Victor E. Halden, Integrating AI-Driven Automation into Modern DevOps: Advancements, Challenges, and Strategic Implications in Software Engineering , International Journal of Modern Computer Science and IT Innovations: Vol. 3 No. 02 (2026): Volume 03 Issue 02
- Jianhong Wei, Aaliyah M. Farouk, MITIGATING CONFIRMATION BIAS IN DEEP LEARNING WITH NOISY LABELS THROUGH COLLABORATIVE NETWORK TRAINING , International Journal of Modern Computer Science and IT Innovations: Vol. 1 No. 01 (2024): Volume 01 Issue 01
- Dr. Alistair Sterling, Architectural Evolution and Decomposition Strategies: A Comprehensive Analysis of Microservice Migration, Performance Optimization, And Machine Learning-Assisted Service Boundary Detection , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 12 (2025): Volume 02 Issue 12
- Dr. Eleanor Whitfield, Architecting Secure and Cost-Optimized Iot-Cloud Ecosystems: Integrating AI-Driven Intrusion Detection, Multi-Path Routing, And Intelligent Workload Scheduling in Distributed Systems , International Journal of Modern Computer Science and IT Innovations: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Dr. Mingyu L. Chen, Muhammad Siddiqui, CODE-SWITCHED RELATION EXTRACTION: A NOVEL DATASET AND TRAINING METHODOLOGY , International Journal of Modern Computer Science and IT Innovations: Vol. 2 No. 02 (2025): Volume 02 Issue 02
You may also start an advanced similarity search for this article.