Why AI Contracts Are Different from Traditional IT Contracts
Imagine this: your project manager uses ChatGPT to draft a requirements document containing confidential client data. Three months later, similar wording pops up in a competitor’s proposal.
Coincidence? Highly unlikely.
AI contracts are fundamentally different from classic software licenses. With CRM software, you know exactly what the system can and cannot do. AI models, however, operate in a gray area of probabilities and ongoing learning.
The EU AI Act, which has been coming into force gradually since 2024, raises the legal bar even further. Companies must now assess risk categories and implement appropriate safeguards.
This creates three key challenge areas:
Data Flow Transparency: Where does your input end up? Is it used for training? Standard terms and conditions rarely answer these questions.
Result Unpredictability: AI can hallucinate, discriminate, or simply deliver incorrect outputs. Who is liable for consequential damages?
Vendor Lock-in: Custom-trained models can’t simply be migrated. Your data and adaptations stay with the provider.
The good news: These risks can be significantly reduced with the right contractual clauses.
The Seven Critical Clauses at a Glance
Not every AI implementation needs a 50-page contract. But you should always cover these seven core areas:
- Data Use & Protection: Clear rules for input data, training, and storage
- Risk Allocation: Who bears the risks for faulty AI outputs?
- Intellectual Property: Legal status of AI-generated content
- Service Level Agreements: Measurable standards for quality and availability
- Compliance & Auditing: Proof of regulatory compliance
- Termination Terms: Data return and deletion upon contract end
- Change Management: Handling model updates and feature changes
These points sound technical, but the business impact is direct. A missing data return process can cut you off from your most vital AI tool for months.
Especially with large-scale implementations—like an enterprise-wide chatbot or RAG system—all aspects must be watertight.
The reason is simple: AI projects are more likely to fail because of unclear responsibilities than because of technology issues.
Data Protection & Compliance: The Core of Every AI Agreement
This is where things get concrete: Your employees enter sensitive information into AI tools every day. Customer data, strategy documents, calculations.
The GDPR requires a legal basis for every data processing activity. With AI applications, this gets complicated because it’s often unclear what exactly happens to the data.
Define the Processing Purpose: Get written confirmation of how your data is used. “Service provision” isn’t enough—demand details. Is it used for training? Is profiling involved?
Control Data Processing: If the AI provider processes personal data, you need a data processing agreement (DPA) under Art. 28 GDPR. Many US vendors offer standard contracts.
Consider Data Localization: Where are your data processed and stored? With European providers, this is often straightforward. With US services, standard clauses or the Data Privacy Framework must apply.
Agree on Deletion Deadlines: Specify when and how your data is deleted. “After contract termination” is too vague. Better: “30 days after written termination with a deletion confirmation.”
Practical example: A machine builder uses AI for quote generation. Customer master data, prices, and technical specs flow into the system.
Without clear data use rules, the company risks GDPR fines up to 4% of annual turnover. For a midsize business with €50 million in revenue, that’s €2 million.
The solution: A detailed data flow plan in the contract. Each type of data has its own rules for storage, processing, and deletion.
Secure Audit Rights: Negotiate the right to perform compliance audits. Larger providers often supply SOC 2 reports.
Liability & Risk Allocation: Who Shoulders What?
AI makes mistakes. That’s reality, not a malfunction.
Large language models hallucinate in a certain percentage of cases, depending on the task. Computer vision can misclassify objects. Predictive analytics occasionally churn out absurd forecasts.
The question: Who pays for damages caused by faulty AI outputs?
Understand Standard Exclusions: Most AI providers exclude liability for indirect damages. That means if an AI-generated quote loses you a deal, the provider won’t pay.
Negotiate Liability Caps: Demand realistic liability limits. For critical use cases, these should match potential damages—not just license fees.
Set Documentation Requirements: The EU AI Act requires extensive documentation for high-risk AI systems. Clarify who provides this evidence.
Real-world example: A staffing company uses AI for pre-screening candidates. The system systematically discriminates against applicants over 50.
Result: Anti-discrimination lawsuit, reputational damage, recruiting freeze.
If liability clauses are vague, the company absorbs all costs. Better: a clause making algorithmic fairness an explicit provider duty.
Check Insurance Coverage: Standard liability insurance rarely covers AI risks. Special cyber policies or AI add-ons are increasingly essential.
Define Incident Response: What happens when there’s an AI error? Set clear reporting lines, response times, and remediation steps.
Here, realism beats perfection. No provider will assume unlimited liability for AI outputs—but you can enforce minimum standards and fair risk-sharing.
Intellectual Property: Who Owns AI-Generated Content?
This is an issue dividing legal experts worldwide: Can AI create copyrightable works? In Germany, the answer is clear: no.
Copyright protects only human creations. AI outputs are essentially public domain—in theory.
Reality is more complicated:
Respect Input Rights: If you use copyrighted texts as AI input, you could trigger violations. Some AI models were trained on protected material.
Clarify Output Usage: Even if AI-generated texts aren’t copyrighted, the provider can limit usage rights via contract. Check the fine print.
Secure Editing Rights: Can you modify and resell AI outputs as you wish? This must be clearly stated.
Case in point: A marketing agency uses DALL-E to create campaign images. The resulting image accidentally resembles an existing artwork.
Consequence: Legal warning, compensation claim, campaign halted.
The solution: Contract clauses requiring the AI provider to check for potential infringements and indemnify you in case of violations.
Protect Trade Secrets: AI-generated content is often based on your confidential data. Ensure it doesn’t end up as training data for other customers.
Take Trademark Rights into Account: AI may inadvertently use third-party trademarks. Clarify who’s liable for trademark infringements.
There’s no one-size-fits-all answer to IP. Every use case needs its own arrangements.
SLA & Performance Guarantees for AI Systems
Traditional software either runs or it doesn’t. AI is more nuanced.
A chatbot might be technically available but deliver useless answers. A translation tool might output text, but with unacceptable quality.
Make Availability Measurable: 99.9% uptime is standard—but define what “available” actually means. Response times over 30 seconds are practically unusable for many applications.
Agree on Quality Metrics: Here’s the tricky part: how do you measure the quality of an AI translation or generated text?
Possible approaches:
- Human evaluation scores on a sample set
- Benchmarks versus established systems
- Customer satisfaction thresholds
- Technical accuracy on standardized test cases
Address Performance Degradation: AI models can worsen after updates. Agree on rollback options to previous versions.
Real-world issue: A company uses AI for document extraction. After a provider update, the recognition rate drops from 94% to 78%.
Without SLA clauses, you have no recourse. With smart contractual agreements, you can demand rollbacks or compensation.
Demand Scaling Guarantees: What happens if your AI capacity needs jump by 500%? In successful implementations, you need to know your capacity limits.
The key: Be realistic with requirements. AI quality naturally fluctuates. But minimum standards can and should be enforced.
Exit Clauses & Data Portability
The worst-case scenario: Your AI vendor is acquired, triples the price, or shuts down the service.
Without an exit strategy, you’re trapped—months or years of data work locked in.
Specify Data Export Formats: In which formats will you get your data back? CSV? JSON? Proprietary formats will not help you.
Agree on Transfer Timelines: How long will the export take? For large datasets, it could take weeks. Plan for adequate transition periods.
Clarify Model Portability: Can you take a custom-trained model with you? Often, this is technically impossible—but training data can usually be exported.
Practical example: An industrial company spends two years training an AI chatbot with company-specific FAQs. The provider doubles the price.
With strong exit clauses: export of training data, 90-day parallel operation, new implementation with a competitor.
Without exit strategy: months without a chatbot, or forced to accept the increase.
Request Deletion Confirmation: Once data is exported, all your data at the old provider must be deleted. Get written confirmation.
Negotiate Migration Support: Larger providers often offer support migrating to competitor products. It sounds odd, but it’s standard for B2B services.
Exit clauses are insurance policies. You hope never to need them—but if you do, they’ll save your project.
Practical Checklist for Your Negotiations
Before entering contract negotiations, prepare these points:
Before Negotiation:
- List data categories: What data flows into the system?
- Conduct risk assessment: What are the worst-case scenarios?
- Budget for legal advice: For contracts over €50,000 annually, specialist advice is worth it
- Evaluate alternative providers: Having a plan B strengthens your position
Must-Have Clauses:
- Detailed data use rules
- GDPR-compliant data processing
- Realistic liability caps
- Measurable SLA standards
- Full data portability
Nice-to-Have Additions:
- Free test periods for updates
- Escrow agreements for critical systems
- Preferred support hours
- Regular compliance reports
Remember: Contracts are negotiable. Standard terms are a starting point, not the final word.
With bigger deals, you have more leverage than with small license purchases. Use it.
Conclusion: Legal Certainty Without Stifling Innovation
AI contracts are more complex than classic software licenses—but they are far from unsolvable.
The key takeaway: Don’t be intimidated by technical complexity. Focus on business-critical risks.
Data protection, liability, and exit strategies are must-haves. The rest is up for negotiation.
Legal hurdles can be overcome—with early planning and realistic expectations.
Your next step: Identify your most critical AI use cases and develop a contract template you can deploy with all providers.
That way, you save time and create consistency in your AI investments.
