Operating models
Ownership, human oversight and the redesign of workflows around AI systems.
AI/02
Intelligence domain · AI/02
Enterprise AI
The operating models, infrastructure and governance behind useful artificial intelligence.
“The competitive edge is shifting from model access to workflow design, trusted data and the ability to redesign how decisions are made.”
01
Pillar guide
Enterprise AI beyond the model
Enterprise AI is the organizational and technical system that turns models into reliable work. It includes data access, workflow design, evaluation, security, governance, adoption and the economics of operating at scale.
We track where AI changes a real decision or process, how companies control that change and which capabilities remain valuable as models become more widely available.
Coverage mapAI/02
Data & infrastructure
The retrieval, compute and integration layers that determine production performance.
Governance & evaluation
Risk-based controls, testing, monitoring and recovery across the AI lifecycle.
02Latest in AI
5 current intelligence briefs
Enterprise AI · analysis
The enterprise AI agent evaluation scorecard
A practical control model for testing task completion, evidence quality, permissions, failure handling and operating cost before an agent reaches production.
Enterprise AI · analysis
Enterprise AI agents are becoming an operating model
The strategic question is no longer whether a company can deploy an agent. It is whether the organization can redesign ownership, controls and work around it.
Enterprise AI · analysis
Europe’s quiet advantage in industrial AI
Europe may not lead the consumer model race, but its industrial base creates a different opportunity: intelligence embedded in machines, energy systems and regulated operations.
Enterprise AI · news
Small-business AI adoption is becoming an infrastructure question
For smaller companies, useful adoption depends on data access, workflow fit and training—not the size of the newest model.
Enterprise AI · analysis
Enterprise AI governance: the control stack leaders actually need
Useful governance sits inside the workflow: clear ownership, approved data, measurable behavior and a recoverable path when an AI system fails.
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Editorial map
Eight investigations across this domain
- 01Agentic workflow architecture
- 02Enterprise model evaluation
- 03AI data and retrieval systems
- 04Human oversight by risk
- 05AI unit economics
- 06Model and vendor portability
- 07AI security and red teaming
- 08Workforce adoption and operating change
Questions this hub answers
Start with the decision.
- What is an enterprise AI operating model?
- How should AI systems be evaluated before deployment?
- Which AI governance controls belong inside the workflow?
Company intelligence
Organizations in this system
SiemensIndustrial AI, digital twins and automation →MicrosoftEnterprise AI, cloud and agent infrastructure →Schneider ElectricEnergy management and industrial digitization →ServiceNowWorkflow AI and enterprise service operations →OpenAIFrontier models, enterprise agents and AI platforms →GoogleAI models, cloud platforms and data-centre infrastructure →SalesforceCRM agents, data platforms and enterprise automation →ABBIndustrial automation, electrification and robotics →Mistral AIEuropean foundation models and deployable AI →
Explore all company files →Related French-language expertise
A practical AI stack for smaller companies
Entreprisma's French-language guide connects tools, data, governance and adoption for SMEs.
Read on Entreprisma ↗