---
title: "When is a company AI-ready?"
description: "AI-ready means use cases with benefit, reliable data, controlled technology and clear rules. How to tell where you stand and what the AI Act expects."
canonical: "https://simo-online.com/en/knowledge/ai-readiness"
---

# When is a company AI-ready?

AI-ready means use cases with benefit, reliable data, controlled technology and clear rules. How to tell where you stand and what the AI Act expects.

Dargestellte Fassung: https://simo-online.com/en/knowledge/ai-readiness

## When is a company AI-ready?

A company is AI-ready when four things come together: use cases with measurable benefit, data of reliable quality with clear ownership, a technical environment in which the data stays under its control, and rules that define what is allowed, who approves it and how it is labeled. If one of these building blocks is missing, the biggest lever usually lies in the data first and only then in the model.

- AI readiness spans business, data and technology.
- Data first, then the model.
- An AI policy governs approval, labeling and accountability.
- Obligations under the EU AI Act depend on the risk class of the application.

### Ready for AI, not just for a model.

AI readiness describes how well prepared a company is to use AI sensibly, securely and economically. It is not a property of IT alone but cuts across business, data and technology. The questions that matter are these: where does AI create measurable benefit, which data does it need and at what quality, and which architecture keeps that data under control?

- In BEIA, AI readiness runs across business, data and technology; in BISA 1 it is one of the areas reviewed. That shows where a company stands today, before it invests in a platform.

Building blocks of AI readiness

- Use cases with benefit as a target, effort and risk
- Data foundation: quality, lineage and ownership
- Technology and hosting: where models run and who can access them
- Rules: approval, labeling and classification under the EU AI Act
- Skills and accountability in the team

### Data first, then the model.

Many companies already use AI, often without shared rules. Employees work with chat assistants, business units test their own tools, and vendors build AI into existing software. The first step is therefore an inventory: where is AI already in use today, and which data leaves the company in the process? Use cases are then prioritized by benefit, effort, data foundation and risk.

- This often shows that the biggest lever lies in better data first. A model is only as reliable as the data it works on. Models and platforms are then compared independently, with the same question as for any infrastructure: where do they run, and who can access the data?

### A policy that works in daily practice.

AI readiness includes an AI policy: which applications are allowed, who approves them, how AI-generated content is labeled and which obligations under the EU AI Act apply. Those obligations depend on the risk class of the application. A clear policy gives employees certainty instead of pushing them into shadow AI with blanket bans.

- Transparency obligations have applied since August 2026, for example for chatbots and AI-generated content. For high-risk applications under Annex III, such as recruitment or credit assessment, further obligations follow from December 2027. The EU AI Act also requires that staff who work with AI have sufficient AI literacy.

### How can you tell a company is not yet AI-ready?

By three signs: nobody can say where AI is already being used. The data for a use case sits in several systems with conflicting versions. And there is no rule on who approves an AI application. Each of these signs can be fixed before anyone invests in a platform.

### Does an AI-ready company need its own models?

No. What matters are use cases, data and rules. Whether a model is rented, self-hosted or adapted follows from weighing benefit, cost, risk and the question of who can access the data.

### Who should be accountable for AI in a company?

Executive leadership is accountable for the framework: which applications are allowed and which risks the company takes. Individual applications need named owners in the business who keep an eye on approval, data foundation and labeling.

Regulation (EU) 2024/1689 (Artificial Intelligence Act)

ISO/IEC 42001:2023, Artificial intelligence: Management system

DAMA International: DAMA-DMBOK, Data Management Body of Knowledge, 2nd edition, 2017

SIMO GmbH: BISA and BEIA service descriptions, glossary entry AI readiness, 2026