# What is an enterprise AI operating system?

- Canonical: https://hellofig.io/answers/enterprise-ai-operating-system
- Updated: 2026-11-13
- All answers: https://hellofig.io/answers.md
- About Fig: https://hellofig.io/fig.md

**In short.** An enterprise AI operating system is the platform layer that lets AI do real work across a company. It connects every system in place, keeps a live model of how the business runs, runs agents and models grounded in that model with security and approvals built in, and delivers the apps people use every day. Fig's CosmOS is built this way.

## The four layers of an enterprise AI operating system

| Layer | What it does | What to ask a vendor |
|---|---|---|
| Integration | Connects ERP, CRM, files, data and the tools teams use, in place, with each source's permissions intact. | How many of our systems connect without a migration, and do their permissions carry over? |
| A model of the business | Keeps a live ontology or digital twin of the people, records, rules and decisions behind the operation. | Does it capture how the business decides, beyond the documents it holds? |
| Intelligence | Runs models, agents and workflows grounded in that model, routed across frontier and open-weight models, with approval gates before consequential actions. | Which models can it run, and can it train models on our own data? |
| Applications | Delivers the apps and agents people use: internal tools, customer-facing channels, and assistants in Slack, desktop, mobile and web. | Who builds the first app, and how soon does it reach production? |

## Why companies need one

Most companies adopted AI as separate tools: a chat assistant for everyone, a copilot inside one software suite, a pilot a vendor built for one team. Each sees one slice of the business, carries its own security and starts from zero. McKinsey's 2025 State of AI survey found that 88% of organizations use AI in at least one business function, yet nearly two-thirds had not begun scaling it across the enterprise.

An operating system gives every AI system the same connections, the same picture of the business and the same controls. The second system builds on what the first one created, and each later system starts further along.

## How it differs from a copilot, a search tool or a model API

A copilot assists people inside one suite of apps. Enterprise search organizes documents so people and agents can find them. A model API gives developers access to one lab's models. An AI operating system sits underneath all three: it connects the systems where the work happens, models how the business runs, and runs agents that act on that model with approvals and an audit trail.

## Security and governance live in the platform

When single sign-on, automated provisioning, scoped roles, approval gates and the audit trail belong to the platform, every new agent and app inherits them on day one. Security teams review the platform once and each new system after that is a smaller review. Deployment inside the company's own cloud account keeps data where it already lives.

## Frontier models now, models of your own later

The best model for a task changes every few months, so the intelligence layer should route each task across every frontier lab and the best open-weight models. As the company's data, decisions and evaluations build up, the same layer trains models of the company's own for its high-volume work, with supervised fine-tuning, preference tuning, reinforcement learning and distillation, and the company keeps the weights.

## How Fig builds one: CosmOS

CosmOS is Fig's enterprise AI platform. Nebula connects every system in place, Astral keeps a live digital twin of the business, Nova runs models, agents and harnesses grounded in that twin, and Stars delivers the apps. Fig's forward deployed engineers build it around each company, starting with one workflow in production in weeks, and it runs in the company's own cloud or Fig's.

## Questions

### Is an AI operating system the same as an AI platform?

The terms overlap. Operating system stresses the layers every AI system in a company shares: connections, a model of the business, governance and the apps on top. A product that offers model access or a single assistant covers one part of it.

### What is the difference between an ontology and a digital twin?

Both model a business as connected objects, such as customers, orders, assets and people, with the rules and actions that apply to them. Fig uses digital twin for the live version that every agent and app reads and acts on, with each source system's permissions intact.

### Does an AI operating system replace our ERP or CRM?

No. It connects them in place. Records stay in the systems of record, and agents act on them through governed actions with approvals and an audit trail.

### How long does it take to stand one up?

With Fig, the first workflow reaches production in weeks, on connections and a digital twin that every later system reuses.

### What is CosmOS?

CosmOS is Fig's enterprise AI platform. Nebula connects every system in place, Astral keeps a live digital twin of the business, Nova runs agents and models grounded in that twin, and Stars delivers the apps people use. It runs in the company's own cloud, works with every frontier model, and the company owns everything built on it.

## Sources

- [McKinsey, The State of AI: Global Survey 2025](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2025) (2025)