Across contemporary organizations, advances in artificial intelligence (AI) are transforming AI from a discrete technological resource into a systemic organizational capability that actively shapes decision-making, business model innovation, and competitive advantage. Traditionally, AI interfaces have largely been reactive, responding to human prompts and predefined inputs. The emergence of Agentic Artificial Intelligence represents a fundamental shift, as agentic systems are designed to operate with increasing autonomy, enabling goal-driven planning, workflow orchestration, coordination across systems, and machine-initiated action with limited human intervention.
For organizations, this growing autonomy presents both significant opportunities and substantial risks. Agentic AI promises new forms of value creation by enhancing efficiency, scalability, personalization, and decision quality across organizational functions such as human resource management, marketing, customer engagement, knowledge management, and operations. At the same time, the delegation of agency to autonomous systems heightens concerns related to governance, transparency, accountability, and oversight, particularly when organizations have limited visibility into how agentic systems reason, learn, and act. Moreover, misaligned interactions and problematic resource integration may produce unintended negative outcomes, underscoring the coexistence of value creation and value co-destruction in AI-enabled organizational processes.
Despite these unresolved challenges, agentic AI is no longer a speculative phenomenon. Organizations have already begun embedding agentic systems into core practices, including recruitment, onboarding, performance management, customer service, marketing operations, and knowledge-intensive work. This diffusion reflects a broader shift in which AI is increasingly understood as a normalized and enduring component of contemporary organizational and marketing systems rather than a temporary technological trend. In parallel, early implementations in knowledge management demonstrate how agentic systems can unify fragmented knowledge bases, dynamically adapt insights, and support continuous organizational learning.
From an academic standpoint, these developments challenge existing organizational and information management theories. While socio-technical systems theory, agency theory, and organizational learning have traditionally conceptualized AI as a support tool within human-centric systems, they offer limited explanatory power for autonomous, multi-agent systems capable of independent coordination and action. In marketing and customer engagement contexts, interactive value formation is increasingly shaped by emotional and relational dynamics emerging from human–AI interactions, further complicating assumptions about control and responsibility. As such, new theoretical perspectives are needed to capture agency, accountability, and knowledge dynamics in AI-enabled organizations.