Agentic AI в enterprise: переход от теории к внедрению

Agentic work—where AI systems operate autonomously to complete tasks without continuous human oversight—has moved beyond theoretical discussion and is now being adopted across enterprises.

These systems differ from traditional automation by handling end-to-end workflows, making decisions based on dynamic inputs, and adapting their behavior over time. For example, in customer service, agentic AI can resolve multi-step issues—such as identifying a problem, escalating to the right department, and following up—without human intervention.

Organizations adopting agentic work cite improved operational efficiency, reduced costs, and faster cycle times as primary benefits.

However, challenges remain. Data quality is cited as the top barrier to scaling agentic systems, followed by integration with legacy systems and workforce resistance.

Experts recommend starting with high-impact, well-defined use cases—such as invoice processing or basic IT support—and gradually expanding scope. Success depends on strong governance, clear accountability frameworks, and continuous monitoring of AI behavior.

One case study highlighted in the report involves a global financial services firm that deployed an agentic AI for mortgage underwriting. The system now handles initial eligibility screening, document verification, and risk scoring—reducing processing time from 7 days to under 48 hours.

As adoption grows, the distinction between human and AI responsibilities is shifting: humans now focus on oversight, exception handling, and strategic input, while AI manages routine execution and adaptive decision-making.

By 2026, at least 50% of enterprises will use agentic AI in at least one core business function—making it the new enterprise standard.