AIchemist
CEN 소개
VELANEXA
블로그
문의하기
데모 체험
← 목록으로
Enterprise AI

The New Enterprise Stack: LLMs, Agents, Vision AI, and Spatial Data

Dec 5, 2025

For a short period, it was possible to talk about enterprise AI as primarily a language model deployment problem. Connect an LLM to enterprise knowledge, build appropriate retrieval infrastructure, manage prompting and safety, and realize substantial value from AI-assisted knowledge work. This framing captured the initial wave of enterprise AI value accurately. It no longer captures the full picture.

The enterprise AI stack is expanding. Language models provide the language understanding and generation foundation. Agent frameworks sit above this layer, providing the planning and execution logic that turns language capability into autonomous workflow action. Vision AI adds perception capability, allowing systems to process images, video, and visual observations that represent a large and underutilized category of enterprise information. Spatial data infrastructure grounds all of these layers in geographic and physical context, enabling the spatial reasoning that physical-world decisions require.

Each layer of this stack adds capabilities that the layers below it cannot provide. A language model without agent frameworks can answer questions but cannot execute multi-step workflows. An agent framework without vision AI cannot process visual inputs, limiting its operational scope in environments where physical observation matters. Vision AI without spatial data infrastructure produces perceptions that lack the geographic and asset context needed for operational decision support. The full stack is required for the full range of enterprise AI value.

Understanding this stack architecture changes enterprise AI investment planning. It reveals gaps between current capability investments and full-stack requirements. It enables more systematic prioritization of which stack layers to invest in next, based on which capabilities are currently most constraining for the highest-value use cases. And it provides a framework for evaluating vendors and technologies against the question of which stack layers they address and how well they integrate with other layers in the stack.

블로그 - AI 데이터 인사이트 | AIchemist