Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation

Abstract

This systematic review examines the transformative potential of Generative Artificial Intelligence (GenAI) across diverse sectors, including information technology, education, manufacturing, creative industries, healthcare, transportation, management, marketing, finance, energy, law, media, agriculture, and e-commerce. By analyzing its applications, the study highlights how GenAI enhances efficiency, fosters innovation, and addresses sector-specific challenges. Key benefits include the automation of complex processes, optimization of resource use, and acceleration of decision-making. However, delayed adoption risks such as workforce displacement and ethical dilemmas are also discussed. The review identifies critical barriers like data privacy concerns, algorithmic bias, and regulatory challenges. Practical strategies for successful GenAI integration are explored, emphasizing infrastructure readiness, workforce upskilling, and ethical governance. This includes leveraging generative models such as Generative Adversarial Networks (GANs), Transformer-based models, Variational Autoencoders (VAEs), and diffusion models to adapt to industry-specific demands. Furthermore, the study underscores the necessity of balancing technological advancements with responsible AI deployment to minimize risks and maximize societal benefits. By synthesizing existing research, this review provides actionable insights for stakeholders aiming to leverage GenAI's transformative capabilities responsibly. It emphasizes the urgency of adopting GenAI technologies to maintain competitiveness and sustainability in rapidly evolving markets. As the study concludes, it advocates for cross-sectoral collaboration to address the complex challenges posed by this paradigm-shifting technology and calls for adaptive policies to align innovation with ethical principles and societal values.

Keywords

• Generative AI
• Industry transformation
• Artificial intelligence strategy.

İş Hayatında Üretken Yapay Zekâ: Dönüşümün Eşiğinde Sistematik Bir İnceleme

Abstract

Bu sistematik inceleme, bilgi teknolojisi, eğitim, üretim, yaratıcı endüstriler, sağlık, ulaşım, yönetim, pazarlama, finans, enerji, hukuk, medya, tarım ve e-ticaret dahil olmak üzere çeşitli sektörlerde Generative Artificial Intelligence'ın (GenAI) dönüştürücü potansiyelini inceler. Çalışma, uygulamalarını analiz ederek GenAI'nın verimliliği nasıl artırdığını, inovasyonu nasıl teşvik ettiğini ve sektöre özgü zorlukları nasıl ele aldığını vurgular. Temel avantajlar arasında karmaşık süreçlerin otomasyonu, kaynak kullanımının optimizasyonu ve karar alma sürecinin hızlandırılması yer alır. Ancak, işgücü yer değiştirmesi ve etik ikilemler gibi gecikmiş benimseme riskleri de tartışılmaktadır. İncelemede veri gizliliği endişeleri, algoritmik önyargı ve düzenleyici zorluklar gibi kritik engeller de tanımlanmaktadır. Başarılı GenAI entegrasyonu için pratik stratejiler araştırılmakta ve altyapı hazırlığı, işgücü becerilerinin geliştirilmesi ve etik yönetişim vurgulanmaktadır. Bu, Generative Adversarial Networks (GAN'lar), Transformer tabanlı modeller, Variational Autoencoders (VAE'ler) ve sektöre özgü taleplere uyum sağlamak için difüzyon modelleri gibi generatif modellerden yararlanmayı içerir. Ayrıca, çalışma, riskleri en aza indirmek ve toplumsal faydaları en üst düzeye çıkarmak için teknolojik ilerlemeleri sorumlu AI dağıtımıyla dengelemenin gerekliliğini vurgular. Mevcut araştırmaları sentezleyerek, bu inceleme, GenAI'nin dönüştürücü yeteneklerinden sorumlu bir şekilde yararlanmayı amaçlayan paydaşlar için eyleme geçirilebilir içgörüler sağlar. Hızla gelişen pazarlarda rekabet gücünü ve sürdürülebilirliği korumak için GenAI teknolojilerini benimsemenin aciliyetini vurgular. Çalışmanın sonucuna göre, bu paradigmayı değiştiren teknolojinin ortaya koyduğu karmaşık zorlukları ele almak için sektörler arası iş birliğini savunur ve inovasyonu etik ilkeler ve toplumsal değerlerle uyumlu hale getirmek için uyarlanabilir politikalar çağrısında bulunur.

Keywords

• Üretken Yapay Zeka
• Endüstri dönüşümü
• Yapay zeka stratejisi.

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