The AI Paradigm Shift: A Comparative Review of Applications and Challenges in Healthcare, Finance, E-commerce, Automotive, and Manufacturing

Abstract

The widespread integration of Artificial Intelligence (AI) is reshaping industrial sectors on a global scale, with forecasts suggesting the market could grow to nearly $900 billion by 2029, reflecting a CAGR of around 32.1%. Moving beyond descriptive overviews, this study provides a systematic critique and cross-domain synthesis of 108 distinct studies to map the operational DNA of AI across five strategic sectors: Healthcare, Finance, E-commerce, Automotive, and Manufacturing. Our analysis reveals a distinct dichotomy in technological maturity: while sectors like E-commerce have successfully operationalized generative models for hyper-personalization, high-stakes domains face critical bottlenecks. Specifically, while AI integration has demonstrated exceptional capabilities such as achieving 99.08% accuracy in equipment failure prediction and improving financial forecasting precision by 30%, it remains constrained by the "black box" interpretability crisis and data heterogeneity. This paper delineates a comprehensive taxonomy of these disparities, identifying systemic barriers such as algorithmic bias, legacy infrastructure incompatibility, and the scarcity of rare-event data in autonomous systems. Ultimately, we propose a strategic roadmap shifting the focus from purely performance-driven metrics to Explainable AI (XAI) and neuro-symbolic architectures, arguing that the transition from experimental pilots to resilient industrial ecosystems requires solving the trade-off between model complexity and trustworthy governance.

Keywords

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Explainable AI

Yapay Zeka Paradigma Değişimi: Sağlık, Finans, E-ticaret, Otomotiv ve Üretim Sektörlerindeki Uygulamalar ve Zorlukların Karşılaştırmalı Bir İncelemesi

Abstract

Yapay Zekanın (YZ) yaygın entegrasyonu, küresel ölçekte endüstriyel sektörleri yeniden şekillendiriyor ve tahminler, pazarın 2029 yılına kadar yaklaşık 900 milyar dolara ulaşabileceğini ve yaklaşık %32,1'lik bir bileşik yıllık büyüme oranına (CAGR) sahip olacağını gösteriyor. Tanımlayıcı genel bakışların ötesine geçen bu çalışma, beş stratejik sektörde (Sağlık, Finans, E-ticaret, Otomotiv ve Üretim) YZ'nin operasyonel DNA'sını haritalamak için 108 farklı çalışmanın sistematik bir eleştirisini ve alanlar arası sentezini sunmaktadır. Analizimiz, teknolojik olgunlukta belirgin bir ikilik ortaya koymaktadır: E-ticaret gibi sektörler hiper kişiselleştirme için üretken modelleri başarıyla uygulamaya koyarken, yüksek riskli alanlar kritik darboğazlarla karşı karşıyadır. Özellikle, YZ entegrasyonu, ekipman arızası tahmininde %99,08 doğruluk elde etmek ve finansal tahmin doğruluğunu %30 artırmak gibi olağanüstü yetenekler göstermiş olsa da, "kara kutu" yorumlanabilirlik krizi ve veri heterojenliği ile sınırlı kalmaktadır. Bu makale, algoritmik önyargı, eski altyapı uyumsuzluğu ve otonom sistemlerde nadir olay verilerinin azlığı gibi sistemik engelleri belirleyerek bu farklılıkların kapsamlı bir sınıflandırmasını ortaya koymaktadır. Sonuç olarak, deneysel pilot projelerden dayanıklı endüstriyel ekosistemlere geçişin, model karmaşıklığı ve güvenilir yönetişim arasındaki dengeyi çözmeyi gerektirdiğini savunarak, odağı tamamen performansa dayalı ölçütlerden Açıklanabilir Yapay Zekaya (XAI) ve nöro-sembolik mimarilere kaydıran stratejik bir yol haritası öneriyoruz.

Keywords

• Yapay Zeka
• Makine Öğrenimi
• Derin Öğrenme
• Açıklanabilir Yapay Zeka

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