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Yes, a startup outside the European Union can still have obligations under the EU AI Act. The trigger is your role and what your AI system does—not your headcount or incorporation country. A company can be in scope when it places an AI system or general-purpose AI (GPAI) model on the EU market, puts it into service in the EU, or uses AI professionally in an EU-facing operation. The practical starting point is an auditable inventory, a documented risk classification, role-specific AI-literacy training and a calendar for the staggered deadlines.
Who must comply, including companies outside the EU?
The Act follows the activity and impact of an AI system. A small company may be a provider (developing or placing a system on the market), deployer (using a system under its authority), importer, distributor or another operator. One company can hold several roles at once—for example, deploying a third-party model internally while also providing a customer-facing application built on it.
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Professional economic activity is within scope according to the European Commission’s Article 50 FAQ. That means a US, UK or other non-EU startup should not assume that having no EU subsidiary removes its obligations. Ask instead:
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- Are we putting it into service for people, customers or workers in the EU?
- Are we using AI professionally in an EU-facing business activity?
- Could our system’s outputs affect people in the EU even when development occurs elsewhere?
Company size does not automatically create an exemption. The amount of work depends on the system’s category and on whether you are a provider, deployer or another operator.
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Start with an AI inventory and role map
Create one controlled register covering every AI capability, including features hidden inside ordinary software. Record the product or feature name, supplier, model version, business owner, users, data types, affected people, deployment location, intended purpose and your company’s legal role.
Systems to include
- Hosted models and APIs used by engineering, support, sales or operations.
- Chatbots, copilots, content generators and automated summarisation tools.
- Hiring, ranking, screening, credit or eligibility decision tools.
- Biometric identification or categorisation features.
- AI embedded in a product you sell or distribute.
- Vendor software with an AI feature enabled by default.
- Spam filters, recommendation features, computer-vision functions and AI-enabled games, even when they appear low impact.
Minimum fields for an auditable register
- System and model identifier, supplier, version and release date.
- Purpose, users, affected groups and geographic markets.
- Provider, deployer, importer or distributor role.
- Input and output data categories, retention and access controls.
- Risk category, the legal basis for that decision and an evidence owner.
- Applicable transparency notices, human-oversight controls and incident contacts.
- Links to contracts, technical documentation, tests, training records and change approvals.
Version the register. When a vendor changes a model, your team changes the purpose or you enter a new market, preserve the old classification and record the new decision rather than overwriting history.
Classify each system by risk
Use four practical buckets, then verify the legal category against the Act and current Commission guidance.
1. Prohibited practices
Stop uses that fall into unacceptable-risk categories before deployment. The Commission identifies manipulation, exploitation of vulnerabilities, unfair social scoring and specified predictive-policing uses among the prohibited practices. A procurement or design review should reject these uses, not merely add a warning or human approval.
2. High-risk systems
High-risk duties apply to systems in the Act’s listed use cases, including relevant Annex III systems, and to certain AI functions embedded in regulated products. Hiring and worker-management tools, credit or access-to-essential-services decisions, and some biometric applications can be high-risk, but the answer depends on the exact purpose, affected decision and legal context. Do not classify an entire vendor or model in the abstract; classify the concrete system and use.
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High-risk work requires a management system covering risk management, data governance, technical documentation, automatic logging, human oversight, accuracy, robustness and cybersecurity. Depending on the category, you may also need conformity assessment and registration steps. Keep evidence showing why the system is in scope, what controls operate and who reviews them.
3. Transparency-covered systems
Some systems are not high-risk but still require users or affected people to be told that they are interacting with AI. Covered obligations also include labelling deepfakes and applying machine-readable marking or detection controls for synthetic content. A general customer-service chatbot is usually a transparency question first; it becomes a different compliance problem if it is also used in a high-risk decision or as a safety component of a regulated product.
4. Minimal-risk systems
Many ordinary uses, such as most spam filters and AI-enabled video games, are considered minimal risk in the Commission’s entry-into-force notice and carry no mandatory AI Act duties. Keep the inventory and classification note anyway: documenting why a system is minimal risk lets you answer a customer, auditor or market-surveillance authority consistently.
Deadlines to put on your compliance calendar
The Commission describes the Act as entering into force on 1 August 2024, with obligations applying in stages. The dates below are the milestones a small business should track.
| Date | What applies | Who should act |
|---|---|---|
| 2 February 2025 | Prohibited-practice rules and AI-literacy obligations apply. | Every provider and deployer with personnel using or overseeing AI. |
| 2 August 2025 | GPAI obligations and related governance provisions apply. | Providers of GPAI models, including those placing a model on the EU market. |
| 2 August 2026 | Main transparency requirements and related enforcement powers begin; the Commission’s policy page describes the Act as generally applicable from this date. | Providers and deployers of systems covered by Article 50 and other applicable provisions. |
| 2 December 2026 | Transition deadline for Article 50(2) marking and detection requirements for certain systems already placed on the market. | Operators with qualifying pre-existing systems. |
| 2 December 2027 | Rules for Annex III high-risk systems apply. | Providers and deployers of in-scope Annex III systems. |
| 2 August 2028 | High-risk rules for AI systems embedded in regulated products apply. | Organizations placing or using those product-embedded systems. |
Dates can interact with the system’s market-placement date, product legislation and later guidance. Recheck the Commission, AI Office, AI Board and national-authority materials whenever you launch, materially modify or repurpose a system.
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Build the required evidence file
AI-literacy record
AI-literacy obligations have applied since 2 February 2025. Train people according to their role rather than giving everyone the same generic slide deck. A developer, recruiter using a ranking tool, customer-support agent supervising a chatbot and executive approving deployment need different instruction.
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For each session, retain the date, attendees, systems covered, learning objectives, trainer or material owner and the way competence was checked. Record refresher training when a model, workflow or risk classification changes. The file should show that personnel understand limitations, foreseeable misuse, escalation routes and the human decisions they remain responsible for.
Transparency evidence
For each covered system, save the user-facing notice, where it appears, the version of the interface and test results showing that the notice is delivered in the relevant interaction. For synthetic text, image, audio or video, document the marking or detection control, the formats covered, failure handling and the date of each test. Plan for the 2 August 2026 enforcement milestone and the 2 December 2026 transition for qualifying systems already on the market.
Vendor due diligence
Obtain the supplier’s model name and version, intended use, known limitations, security information, incident contact, data-use terms and available conformity or technical documentation. Contractually require notice of material model changes and cooperation with investigations. A vendor statement is evidence, not a substitute for your own classification of how your company uses the system.
Incident and change management
Name an owner for incident intake, severity assessment, regulator contact and corrective action. Define what counts as a harmful output, security event, transparency failure or loss of human oversight. Keep the prompt, input context, output, model version, user, timestamp and decision taken where lawful and proportionate. Link every production change to an approval and updated risk assessment.
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If you provide a GPAI model
GPAI providers have additional duties from 2 August 2025. Prepare a provider file containing:
- Technical documentation describing the model and its capabilities, limitations and evaluation approach.
- A copyright-compliance policy for training material.
- A public summary of the content used for training.
Providers of GPAI models with systemic risk face extra controls: notification, a systemic-risk assessment, mitigation measures, incident reporting and cybersecurity. If your startup fine-tunes or distributes a model, establish whether you are providing a model or merely deploying a third-party service; that role determines which file you must maintain.
High-risk implementation checklist
For an in-scope high-risk system, assign accountable owners and work through these controls before release:
- Define the intended purpose. Write the decision the system supports, who can rely on it and which people may be affected.
- Run risk management. Identify foreseeable misuse, discriminatory outcomes, safety hazards and residual risk; document mitigations and review intervals.
- Govern data. Record provenance, relevance, representativeness, quality checks, preprocessing and controls for sensitive data.
- Prepare technical documentation. Describe architecture, training or configuration, performance, limitations, monitoring and change control.
- Enable logging. Capture events needed to reconstruct operation, investigate incidents and demonstrate compliance.
- Set human oversight. Give named people authority, information and time to interpret outputs, override decisions and stop the system.
- Test accuracy, robustness and cybersecurity. Test realistic conditions, edge cases, attacks and degradation after updates.
- Complete conformity and registration steps. Determine the applicable assessment route and registration obligation for the specific category before placing the system on the market or putting it into service.
Hiring, credit and biometric projects deserve an early legal and technical review because a wrong classification can force redesign after deployment. A chatbot used only for general information may not be high-risk, while a tool that ranks applicants or influences access to essential services can trigger the high-risk framework.
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You can build a lightweight program internally, use a compliance platform, or combine internal ownership with specialist training and advice. Compare options against the work your register shows is necessary:
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- Inventory and risk register: Can it cover every model, API, embedded feature and role, with rationale and approvals?
- Evidence and version control: Can you export dated training records, tests, technical files and change history?
- Article 50 monitoring: Does it track transparency notices, synthetic-content marking and detection tests through the 2026 milestones?
- Vendor integrations: Can supplier changes, contracts and incident contacts be attached to each system?
- GPAI and high-risk support: Does it handle provider documentation, copyright policy, training summaries, risk management and conformity evidence?
- EU data residency and exportability: Where is compliance data stored, and can you retrieve it for a market-surveillance request or a regulator?
- Total cost and ownership: Who maintains classifications and updates the program when guidance or a model changes?
For training providers, check EU-specific currency, role-based content, completion records and coverage of deployer versus provider duties. For advisers, evaluate jurisdiction, high-risk conformity experience, sector knowledge, independence and the ability to update work as guidance changes. Treat any commercial service as implementation help, not as a promise of certification or a legal outcome.
A practical 30-day startup plan
- Days 1–5: appoint owners. Name one accountable person and backups for inventory, classification, vendor diligence, transparency, incidents, training and regulatory updates.
- Days 1–10: build the register. Interview engineering, HR, finance, support, marketing and procurement; include dormant pilots and embedded vendor features.
- Days 6–15: classify and stop. Screen prohibited uses first, then assess high-risk, transparency, GPAI and minimal-risk categories. Pause ambiguous launches.
- Days 11–20: close urgent gaps. Deliver role-based AI-literacy training, add notices, test synthetic-content controls and obtain missing vendor documents.
- Days 16–25: design evidence. Create templates for risk decisions, logs, incidents, technical documentation, approvals and model changes.
- Days 26–30: rehearse accountability. Run a mock market-surveillance request: retrieve the inventory entry, classification rationale, training record, transparency test, vendor file and incident procedure for one production system.
Repeat the review whenever a system, vendor, market, user group or purpose changes. Keep the evidence set versioned so you can show what was true at the time a decision was made.
What non-compliance can cost
The Commission’s Article 50 FAQ cites a maximum penalty of €15 million or 3% of worldwide annual turnover for relevant transparency infringements. The applicable amount depends on the infringement and enforcement decision; the figure is not a blanket fee for every startup. A defensible inventory, prompt remediation and complete records reduce the chance that a preventable documentation failure becomes an enforcement problem.
The Bottom Line
For a small business, EU AI Act compliance is an evidence discipline: map every AI use and role, stop prohibited practices, train people, classify each system, implement the right transparency or high-risk controls, and preserve versioned records against the 2025–2028 deadlines. Start now even if your company is outside the EU or most of your tools look low risk.
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