Artificial Intelligence (AI) is no longer just a futuristic concept – it has quickly become part of our everyday lives and business operations. From customer support agents to advanced data analytics and decision-making tools, AI is transforming the way companies operate, deliver services, and stay competitive.
However, besides exciting opportunities, this shift also brings new legal and operational challenges, especially for IT companies. As AI becomes integrated into software products, cloud services, and IT solutions, it changes the way contracts need to be structured. Traditional IT contracts often overlook the unique characteristics of AI, such as its reliance on data, ability to learn and evolve, and the unpredictability of its outputs.
For those reasons, negotiating IT contracts in the AI era requires extra caution and understanding, and it is no longer enough to focus only on standard software licensing or service levels. Companies must ensure that their contracts clearly address issues like AI intellectual property ownership, data usage rights, transparency obligations, and compliance with swiftly evolving AI regulations.
In this blog, we will explore what makes AI-related IT contracts different, which clauses need special attention, and how IT companies can protect themselves and their clients when negotiating deals that include AI components.
The AI Factor in IT Contracts
What Makes AI-Specific Provisions Unique?
For starters, AI brings a set of challenges and considerations that traditional IT contracts were not designed to handle. Unlike standard software, it is typical for AI systems to:
- Rely heavily on data – AI needs large datasets to function and improve. Consequently, contracts must address who provides the data, who owns it, and how it can be used.
- Learn and evolve over time – As they process more data, it is not unusual for AI models to change. As a result, questions arise about responsibility for outputs, updates, and maintenance.
- Deliver unpredictable outputs – AI decisions are often based on complex algorithms and large volumes of data. With that in mind, it is often difficult to guarantee accurate results or outcomes.
- Raise ethical and regulatory considerations – From data privacy compliance to bias and transparency requirements, AI introduces an entirely new regulatory layer that must be considered in any agreement as a part of AI Governance.
These unique factors mean that traditional “boilerplate” software clauses are often insufficient. Companies that develop or use AI need tailored provisions to address the risks and obligations that come with this technology.
Typical AI Use Cases in IT Agreements
AI is finding its way into IT contracts in a variety of ways. Some common examples include:
a) SaaS solutions with AI components. Many software-as-a-service products now comprise AI features, such as recommendation systems, automated data insights, or natural language processing tools.
b) AI development and integration contracts. Companies engage AI specialists to build, train, or integrate AI models into their existing systems to analyze data, optimize operations, or enhance user experiences.
c) AI data analysis services. In addition to engaging engineers for the development of AI, businesses often outsource data analysis to external providers to gain insights from customer data, operational metrics, or market trends. While this business move offers significant benefits, it also raises complex questions around data ownership and confidentiality.
Understanding these use cases is the first step towards negotiating IT contracts that both safeguard your business and maximize the benefits of AI.
Key Clauses to Watch Out For
When negotiating IT contracts involving AI, several essential clauses require special attention. Why? Simply, those provisions ensure that your company is protected, compliant, and set to fully benefit from the benefits AI offers.
- Intellectual Property Ownership
One of the most crucial issues in relation to AI contracts is determining who owns the AI models, data outputs, and any improvements made during the engagement.
For example, if your company provides data to train an AI model, will you have rights to the improved version of that model?
Or, if the AI system generates outputs or insights based on your proprietary data, can those outputs be used by the provider for other clients?
Both of these questions can be resolved through clear and precise clauses defining:
- Ownership of the original AI model,
- Rights to modifications, enhancements, or retrained versions,
- Ownership and usage rights over AI-generated outputs and results.
- Data Usage and Protection
AI thrives on data, so data usage clauses must ensure:
- Compliance with data privacy laws, such as GDPR or local data protection regulations,
- The data provided is used only for agreed-upon purposes,
- Adequate security measures are in place to protect personal data.
An additional question that is inevitably raised is what happens to your data after the engagement ends – is it deleted, anonymized, or retained for future AI training by the provider? The answer should be contained in the agreement, as well.
- Liability and Risk Allocation
Unfortunately, AI systems can often produce unexpected or incorrect results. To prevent the damage arising out of such lapses, contracts need to address who bears the risk if something goes wrong. Questions to consider in this regard include:
- Will the provider be liable for errors in AI outputs?
- Are there any limitations of liability, and are they appropriate, taking into account the potential impact of AI decisions?
- Should there be indemnities for specific risks, such as data breaches or regulatory fines resulting from AI errors?
- Service Levels and Performance Standards
Unlike traditional software, defining measurable KPIs and service levels for AI tools can be challenging due to the probabilistic nature of AI outputs. This means that AI systems may not always produce consistent results, which further leads to the necessity to set realistic performance expectations in contracts.
In this regard, contracts should:
- Avoid warranting unrealistic accuracy levels,
- Clearly define expected performance metrics,
- Include provisions for retraining or improving AI models if performance standards are not met.
- Transparency and Explainability Requirements
As AI becomes more embedded in business decisions, ensuring transparency has become a critical concern, especially when it comes to high-risk systems and general-purpose AI models (GPAI), which are subject to stricter regulations under the EU AI Act due to their potential impact on fundamental rights, safety, and compliance risks. With such growing demands for AI transparency, both on the regulatory and client level, it is important to address:
- Whether the AI provider must explain how the system works,
- If information on how outputs are generated must be provided,
- What documentation will be available to demonstrate compliance and fairness.
If your company plans to use AI outputs in decision-making processes, it is crucial to address these topics in the contract, especially when such decisions could impact employees or clients.
- Compliance with AI Regulations
Finally, as AI regulation is advancing rapidly, both locally and globally, contracts must include provisions that:
- Ensure compliance with current and future AI laws (such as the EU AI Act),
- Allow for contract updates if regulations change significantly,
- Allocate responsibility for regulatory monitoring and compliance.
Future-proofing your contracts will help avoid unexpected liabilities or operational disruptions as AI legislation develops.
Practical Tips for Negotiating AI-Related IT Contracts
By now, we can agree without doubt that AI contracts require a different mindset compared to traditional IT agreements. The following practical tips are essential for effective negotiations and the protection of the company’s business interests.
Understand What the AI System Actually Does. Before entering into any AI-related contract, make sure you have a clear understanding of what the AI system is designed to do, how it works, and what its limitations are.
For example, does it provide recommendations, automate decisions, or analyze large datasets?
Collecting all information in this regard will help you assess:
- Whether the system’s outputs are critical to your operations,
- The level of accuracy you can and should expect,
- The potential risks associated with relying on its results.
Without this understanding, it will be difficult to negotiate appropriate performance standards, liability clauses, and compliance obligations, whereas having the relevant information upfront gives your company a stronger negotiation advantage, as it allows you to identify risks early and set clear expectations with the other contractual party.
Involve Technical and Compliance Teams Early. AI systems often raise technical, operational, and legal questions that go beyond standard contract review. Involving your technical and compliance teams early in the negotiation process will help:
- Identify hidden technical dependencies or integration challenges,
- Assess data protection, privacy, and ethical implications,
- Ensure the AI system complies with internal policies and external regulations.
In short, cross-functional collaboration from the start can prevent costly oversights later.
Define AI Functionality Clearly. Due to the fact that AI outputs can vary, vague descriptions in contracts can lead to disputes if the system does not perform as expected. To avoid this, it is necessary to:
- Clearly define what the AI system is expected to do,
- Specify the types of data it will process and the outputs it will generate,
- Outline any accuracy thresholds or performance expectations in realistic terms.
Once again, precise drafting reduces misunderstandings and aligns expectations between your company and the provider.
Review Third-Party Components and Open-Source AI Library Risks. Many AI systems rely on third-party components or open-source libraries. While these certainly accelerate development, they can introduce some new risks, such as:
- Licensing restrictions or obligations to share modifications,
- Security vulnerabilities in unverified libraries,
- Lack of clarity on who is responsible for third-party software failures.
To avoid these challenges, ensure the contract requires the provider to disclose all third-party and open-source components, confirm that they are appropriately licensed, and set out who bears the risk for any associated issues.
Common Mistakes to Avoid
Even experienced companies can sometimes make mistakes when negotiating AI-related IT contracts. Being aware of some common pitfalls can help you protect your business more effectively.
a) Treating AI Like Any Other Software Service
One of the most frequent mistakes is treating AI solutions as if they were traditional software tools. As previously mentioned, unlike standard software, AI systems may not always produce consistent outputs and often rely heavily on data quality and continuous learning.
Overlooking these differences can lead to unrealistic expectations, poorly defined performance terms, and disputes if the AI outputs fail to align with initial assumptions.
b) Insufficient Attention to Data Training Rights and Restrictions
Although AI systems are powered by data, contracts often fail to clearly address:
- Who provides the training data,
- How can the data be used during and after the contract,
- Whether your company keeps the rights to any AI improvements resulting from your data.
Neglecting these issues can result in losing competitive advantages, especially if providers use your proprietary data to enhance their models for other clients.
c) Neglecting Ongoing Monitoring Obligations
Another common mistake is assuming that AI systems do not require regular monitoring or updates. In reality, AI models can change over time, resulting in reduced accuracy or biased outputs if not retrained or updated. To prevent this from happening, contracts should address:
- Obligations for performance monitoring and reporting,
- Responsibilities for model updates or retraining,
- Procedures for addressing drops in AI performance.
Failing to include these provisions may leave your company with underperforming systems and no clear recourse.
Future Trends in IT Contracting from the AI Perspective
Based on everything previously analyzed, it is clear that AI is not only transforming products and services but also reshaping the way IT contracts are structured and negotiated. Looking ahead, several trends are likely to influence IT contracting practices in the AI era:
a) Automated Contracting Tools – As AI-powered legal tech evolves, contract drafting, review, and negotiation processes themselves are becoming automated. Tools used for analyzing risks in AI agreements, suggesting clause improvements, or generating first drafts tailored for AI-related deals will increasingly support legal and procurement teams.
As a result, not only that the negotiation time can be significantly reduced, but these tools may also improve consistency in contracts on the organizational level and help identify hidden risks more efficiently.
b) Smart Contracts Integrating AI Decision-Making – Another emerging trend is the use of smart contracts that integrate AI functionalities, particularly in blockchain-based transactions. For example, AI algorithms can automatically trigger contractual obligations or payments based on real-time data inputs or performance results.
While this offers operational efficiency, it also introduces new risks, such as:
- Errors in the AI decision-making process triggering unintended consequences, or
- Challenges in legally enforcing AI-driven contract executions.
Contracts involving such technologies will require careful drafting to balance automation with accountability.
c) AI Clauses Becoming Standard in Template Agreements – As AI becomes embedded in a wide range of IT solutions, clauses addressing AI-specific issues will become standard features of IT contract templates. For example:
- Mandatory transparency and explainability provisions,
- AI model retraining obligations and performance review schedules,
- Data usage, ownership, and improvement of rights clauses,
- Compliance with AI-specific regulations such as the EU AI Act.
Incorporating these clauses proactively will help companies stay compliant, manage risks, and avoid delays in negotiations as AI adoption accelerates.
d) Increasing Regulatory Influence on Contract Terms – With new regulations such as the EU AI Act and global initiatives on AI governance, regulatory requirements will directly shape contractual obligations. Contracts will need to reflect:
- Classification of AI systems (e.g., high-risk systems or GPAI),
- Mandatory risk assessments and compliance documentation,
- Supplier obligations to assist clients with regulatory reporting.
In short, keeping contract terms aligned with evolving regulatory frameworks will become a competitive necessity.
AI is reshaping the IT landscape at an unprecedented pace, bringing with it both opportunities and challenges. As this technology becomes more integrated into software products, services, and business operations, traditional IT contracts are no longer sufficient to address the complexities it introduces.
Negotiating AI-related IT contracts requires a clear understanding of the technology, its unique risks, and the evolving regulatory environment. From defining intellectual property ownership and data usage rights to ensuring transparency, accountability, and compliance, each clause plays a critical role in protecting your business and ensuring long-term value.
Any company that adjusts its contracting practices to address AI-specific issues proactively, involving technical and compliance teams early, and staying informed about future trends, will be well-positioned to harness the benefits of AI while managing its risks effectively.
