EU AI Act in Practice: A 2026 Readiness Checklist

The EU AI Act became enforceable in 2024, and we're now in the period where organizations are either getting ahead of compliance or running headlong toward violations. I've seen both approaches. The ones getting ahead are spending time now understanding what they actually need to do. The ones who aren't will find enforcement much more expensive and disruptive.

This is a practical guide to EU AI Act readiness in 2026. Not theoretical. Not what you might do someday. What you need to do now.

First: Does the EU AI Act Apply to You?

This question seems simple but trips up a lot of organizations. The EU AI Act applies if you: operate in the EU market (sell to EU customers, have EU users, or provide services to EU organizations), are based in the EU, or have any AI system in your stack that touches EU users or data.

If any of that applies to you, you need to think about EU AI Act compliance. The Act applies to AI systems, which broadly means software that uses machine learning or algorithmic decision-making. This includes chatbots, recommendation engines, hiring tools, content moderation systems, credit scoring systems, and more.

Second: Classify Your AI Systems by Risk

The EU AI Act has a tiered approach. Not all AI systems require the same level of compliance. Risk classification determines your obligations:

Prohibited AI

Some AI uses are flat-out banned under the Act. This includes subliminal AI manipulation, social credit scores, and real-time facial recognition in public spaces (with very limited exceptions). If you're using any of these, you need to stop.

High-Risk AI

High-risk systems are those that could cause serious harm to fundamental rights. This includes AI used in hiring, education, creditworthiness assessment, and law enforcement. These systems require extensive compliance: detailed documentation, bias testing, human oversight, data quality requirements, and more.

Limited-Risk AI

Systems that interact directly with people (like chatbots) fall into this category and require transparency obligations. You need to tell people they're interacting with AI and explain what it can and can't do.

Minimal or No Risk

Most AI systems that don't directly impact rights or safety fall into this category. No specific compliance obligations, though best practices still matter.

The first practical step: classify every AI system you have. Most organizations have more AI systems than they realize. Tools, plugins, third-party APIs that use AI—it all counts. Make a list. Then classify each one by risk level.

Third: Build Your AI Inventory and Impact Assessment

You can't manage what you don't know you have. Organizations often discover hidden AI systems when they start compliance work. A recommendation algorithm somewhere. A chatbot a team stood up. An API they're using that claims to use AI. An internal tool built with machine learning.

Your inventory should include: name and purpose of the AI system, risk classification, where it's deployed and who uses it, what training data it uses, whether it impacts EU users or data, key performance metrics and known limitations, and who's accountable for it.

For high-risk systems, you also need an impact assessment. This documents how the system could harm people, what safeguards you have in place, and how you'll monitor for problems.

Fourth: Documentation, Documentation, Documentation

EU AI Act compliance is heavily documentation-focused. Regulators want to see evidence that you've thought through risks and taken action. For high-risk systems, you need:

Technical Documentation

How the system was built, what data it uses, what architecture and algorithms power it, how it was trained and tested. This needs to be detailed enough that someone else could understand the system.

Training Data Documentation

What data was used to train the system, how much, where it came from, whether it includes personal data, what biases or quality issues it has. This is critical for demonstrating that your system isn't discriminatory.

Testing and Performance Records

How you tested the system for bias, accuracy, robustness, and adversarial attacks. What tests did you run? What results did you get? Where does the system underperform?

Monitoring and Maintenance Plans

How you'll keep monitoring the system in production. What metrics will you track? What triggers require action? How will you handle drift or degradation?

Human Oversight Procedures

For high-risk systems, humans need to stay in the loop. Document how your team will review system outputs, override decisions when needed, and escalate concerns.

The pattern here is clear: if you can document it, you can demonstrate compliance. If you can't document it, you have a problem.

Key Things to Get Right Now

Identify Prohibited AI

If you're using real-time facial recognition in public spaces, subliminal manipulation, or social credit scoring, you need to stop now. These are banned, and there's no compliance path. Full stop.

Document Training Data

This is where a lot of organizations stumble. You need to be able to say exactly what data trained your system. If you can't, that's a serious compliance gap. If your data includes personal information from EU residents without proper legal basis, that compounds the problem.

Test for Bias

For high-risk systems, you need evidence that you've tested for bias, particularly in protected characteristics. Use established bias testing frameworks. Document what you found and what you did about it. If you found bias and ignored it, that's a major liability.

Plan for Human Oversight

High-risk systems need human involvement. This doesn't mean hiring an army of people to review every decision. It means your system is designed so humans can understand and override AI decisions when needed, and have procedures to do so.

Be Transparent with Users

If your system interacts with people, they need to know they're interacting with AI. Be clear about what it can do and its limitations. This builds trust and ensures informed consent.

A Word on Penalties

The EU AI Act comes with significant penalties. For high-risk AI violations, organizations can face fines up to 6% of global annual revenue. For other violations, it's 3%. These penalties increase over time as enforcement escalates. Organizations that start compliance now will be in a much better position than those that wait.

There's also reputational risk. Enforcement actions are public. Getting caught violating the Act damages trust with customers, partners, and employees.

The Bottom Line

EU AI Act compliance isn't optional. It's also not impossible if you start now. The organizations that will struggle are the ones that wait until they're under enforcement action or facing a legal challenge.

Your immediate next steps: audit your AI systems, classify them by risk, build your inventory, and start documenting. For high-risk systems, invest in bias testing, impact assessments, and governance structures. The work is substantial, but it's also foundational to responsible AI development. And it's what your regulators expect to see.

Get ahead of this now. Your future self will be grateful.