Enterprise AI
TLDR
AI applications deployed inside large organizations, with requirements around security, compliance, data governance, and integration.
Definition
Enterprise AI refers to AI products and deployments targeted at large organizations as opposed to consumers or small businesses. The defining characteristic is not the model or the task, it is the surrounding requirements: the enterprise buyer needs data residency controls, audit logs, role-based access, integration with existing identity systems, and compliance with sector-specific regulation.
An enterprise-AI product is often functionally similar to a consumer or SMB product; the difference is in how it is sold, deployed, and supported.
Why it matters
Most of the revenue in the AI market currently flows through enterprise deployments. The defensible position for a Belgian startup in this category usually combines a specific vertical (law, healthcare, industrial, HR) with deep integration into the enterprise stack, rather than competing on pure model capability against foundation-model providers.
Belgian companies operating in this space include TechWolf (Skills Ontology for HR), Collibra (Data & Analytics governance for enterprises embedding AI), and Aikido Security (AI for security operations).
Mechanism
Enterprise-AI sales cycles are long (3-9 months) because the buyer has to involve procurement, security review, legal, and sometimes the works council. The unit economics work because enterprise contracts are large (six to seven figures) and expand over time as new departments adopt the tool.
Related
- Parent: AI-Native - enterprise-AI is an application of the AI-Native pattern
- Sibling: Digital Transformation - the organizational context in which enterprise AI is deployed
- Sibling: Skills Ontology - a specific enterprise-AI data pattern