The digital transformation of business has historically progressed through distinct technological waves, including mechanization, computerization, and automation. The current emergence of generative artificial intelligence (AI) represents a qualitative shift within this trajectory, as it extends automation beyond routine and rule-based tasks into domains traditionally reserved for human cognition, creativity, and judgment. Generative AI systems, such as large language models and multimodal neural networks, are capable of producing original content, synthesizing complex information, and generating strategic insights based on probabilistic reasoning rather than deterministic logic (Goodfellow et al., 2016; Vaswani et al., 2017).

In the context of business development and management, this shift is particularly consequential. Business development encompasses opportunity identification, market expansion, partnership formation, innovation strategy, and long-term value creation. Management, meanwhile, involves planning, organizing, coordinating, controlling, and leading organizational resources. Both domains are knowledge-intensive and decision-driven, making them especially susceptible to disruption by generative AI technologies.

While early discourse around AI in business emphasized automation, cost reduction, and efficiency, generative AI introduces a more nuanced value proposition. It enables augmentation rather than substitution of managerial labor, allowing organizations to explore new strategic possibilities, personalize customer engagement, and accelerate innovation cycles. At the same time, the rapid diffusion of generative AI raises concerns regarding ethical governance, data integrity, workforce displacement, and the erosion of human judgment.