4 contract clauses that protect your business from vendor AI failures

Your vendor’s artificial intelligence systems could become your organization’s biggest blind spot. As AI adoption accelerates across business partnerships, the risks hiding in your supply chain are multiplying faster than most companies can track them.

Consider this reality check: McKinsey, a global consulting firm, reports that 78% of organizations now use AI in at least one business function. However, your organization’s direct AI usage represents just the tip of the iceberg. The larger concern lies beneath the surface—in the AI systems your vendors, partners, and service providers are quietly embedding into their operations, often without your knowledge or oversight.

When a vendor’s chatbot mishandles your sensitive customer data, when an algorithm produces discriminatory hiring recommendations, or when a partner trains its AI models using your proprietary information, the consequences don’t stay contained within their organization. Regulatory penalties, compliance violations, and reputational damage have a way of flowing upstream to the companies that contracted these services, regardless of where the AI failure originated.

The legal landscape makes this risk transfer even more concerning. Courts and regulators typically focus their attention on the organization using the AI tool—not necessarily the vendor that built it. This means your company could find itself liable for AI-driven mistakes that happened entirely outside your direct control.

Smart organizations are getting ahead of this challenge by building AI accountability directly into their vendor contracts. Here are four essential contract clauses that can help shield your business from hidden AI liability while maintaining the benefits of AI-enhanced partnerships.

1. Mandatory disclosure of AI usage

The fundamental challenge in managing vendor AI risk is visibility—you cannot govern what you cannot see. Many organizations discover their vendors’ AI usage only after something goes wrong, creating a compliance nightmare that could have been prevented with proper disclosure requirements.

This visibility gap runs deeper than most executives realize. While nearly four out of five organizations report using AI, McKinsey’s research reveals that only 21% have fully mapped and documented their AI use cases. If companies struggle to track their own AI implementations, imagine how difficult it becomes to monitor the “shadow AI” proliferating across vendor relationships.

Shadow AI refers to artificial intelligence tools and systems that operate within your business ecosystem without formal documentation or oversight. This might include AI-powered features embedded in productivity software, automated analytics running in customer service platforms, or machine learning algorithms optimizing supply chain decisions. Each of these represents a potential point of failure that could impact your operations, compliance status, or customer relationships.

The disclosure requirement becomes even more critical as regulatory frameworks evolve. The European Union’s Artificial Intelligence Act, which began phasing in requirements throughout 2024 and 2025, mandates transparency when AI systems are used in customer-facing roles. Organizations operating across multiple jurisdictions need comprehensive visibility into vendor AI usage to ensure compliance with varying regulatory standards.

Action to take: Structure your contracts to require proactive, ongoing disclosure rather than disclosure only upon request. Vendors should provide detailed documentation of all AI systems involved in service delivery, including obvious applications like chatbots and hidden implementations like automated decision-making algorithms. Specify that this disclosure must be updated whenever new AI tools are introduced or existing systems are modified. For international operations, ensure disclosure requirements align with the most stringent applicable regulations, such as the EU AI Act’s transparency mandates.

2. Strict data usage limitations

Your organizational data represents one of your most valuable assets, yet many companies have limited visibility into how vendors use this information once it leaves their direct control. The rise of AI has created new risks around data usage that traditional contracts often fail to address adequately.

Many AI vendors view client data as a valuable resource for training and improving their machine learning models. Without explicit restrictions, your sensitive information could end up incorporated into AI systems that serve your competitors, embedded in vendor products you never agreed to support, or used to develop capabilities that directly compete with your business.

This data repurposing often happens without malicious intent. Vendors may view client data aggregation as a standard practice for improving service quality across their customer base. However, the implications for your organization can be severe, particularly if proprietary information, customer data, or strategic insights become accessible to competitors through AI models trained on your data.

The regulatory landscape adds another layer of complexity. Privacy laws like the General Data Protection Regulation (GDPR) in Europe, the Health Insurance Portability and Accountability Act (HIPAA) for healthcare data in the United States, and the California Consumer Privacy Act (CCPA) impose strict requirements on how personal and sensitive data can be processed and shared. When vendors use your data for AI training without proper safeguards, they may inadvertently create compliance violations that extend back to your organization.

Action to take: Include explicit contractual language prohibiting vendors from using your data to train external AI models, incorporating your information into their commercial offerings, or sharing your data with other clients. Require comprehensive compliance with all applicable privacy laws, including GDPR, HIPAA, CCPA, and any industry-specific regulations relevant to your business. Specify that these data protection obligations survive contract termination, ensuring your information remains protected even after the business relationship ends. Consider requiring vendors to provide regular attestations confirming compliance with these data usage restrictions.

3. Human oversight requirements for AI decisions

Artificial intelligence can dramatically improve efficiency and reduce operational costs, but it also introduces risks that require human judgment to manage effectively. Automated systems excel at processing large volumes of data quickly, but they can miss contextual nuances, perpetuate hidden biases, or make decisions that seem logical to an algorithm but prove problematic in real-world applications.

Human oversight serves as a critical safeguard, ensuring that AI-generated outputs are reviewed for accuracy, interpreted within appropriate context, and corrected when systems produce flawed recommendations. Without this oversight layer, organizations risk over-relying on AI efficiency while overlooking significant blind spots that could lead to discriminatory practices, regulatory violations, or operational failures.

Regulatory frameworks increasingly recognize this need for human involvement. The EU AI Act requires documented human oversight mechanisms for high-risk AI systems, reflecting a broader regulatory trend toward ensuring human accountability in automated decision-making processes.

The real-world consequences of inadequate oversight are already visible in the marketplace. Workday, a major provider of human resources software, faces an ongoing lawsuit from the U.S. Equal Employment Opportunity Commission (EEOC) alleging that its AI-powered recruiting tools discriminated against job applicants based on race, age, and disability status. The case, which remained unresolved as of late 2024, illustrates a crucial lesson about vendor AI liability.

Even though the alleged discriminatory bias originated within Workday’s AI system, the EEOC lawsuit targets not just the software provider but also the employers who used these tools in their hiring processes. This legal approach reflects how regulators and courts view AI accountability—they look beyond the technology vendor to examine how organizations implement and rely on AI systems in their operations.

Action to take: Define specific oversight requirements in vendor contracts, particularly for high-stakes decisions like hiring, lending, or customer service interactions. For example, require that qualified human reviewers evaluate AI-driven hiring recommendations before final decisions are made. Establish internal processes to ensure these reviews actually occur and are properly documented. Create audit trails that demonstrate human involvement in AI-assisted decisions, as this documentation may prove crucial if regulatory questions arise. Consider requiring vendors to provide training for your staff on how to effectively review and interpret AI-generated recommendations.

4. Clear liability assignment for AI errors and bias

When AI systems produce incorrect, biased, or harmful outputs, the financial and reputational costs can be substantial. However, the question of who bears responsibility for these failures often remains unclear until disputes arise, potentially leaving your organization exposed to liability for problems that originated entirely within vendor systems.

Many AI vendors actively work to limit their own exposure to AI-related damages. Industry research indicates that approximately 88% of AI technology providers include liability caps in their contracts, often limiting their maximum responsibility to amounts as small as a single month’s subscription fee. While this data specifically relates to AI software contracts, it illustrates a broader pattern across the technology industry—vendors typically seek to minimize their liability exposure while shifting risk to their clients.

This risk allocation creates a dangerous mismatch. The organization using the AI tool faces the full potential impact of AI failures—including regulatory fines, discrimination lawsuits, operational disruptions, and reputational damage—while the vendor that created and maintains the AI system accepts only minimal financial responsibility for problems their technology might cause.

The legal system often reinforces this imbalanced risk distribution. When AI-driven decisions lead to regulatory violations or discriminatory outcomes, authorities typically focus their enforcement actions on the organization that implemented the AI system rather than the vendor that developed it. This approach reflects the principle that organizations remain responsible for their business decisions and operational practices, regardless of whether they rely on third-party technology to support those activities.

Action to take: Negotiate liability provisions that specifically address AI-related issues, including discriminatory outputs, regulatory violations, and errors in automated recommendations. Avoid generic indemnity language that may not cover AI-specific scenarios. Instead, create dedicated contract sections addressing AI liability, with financial remedies that scale appropriately to the potential impact of AI failures. Consider requiring vendors to maintain professional liability insurance that specifically covers AI-related claims. For high-risk applications, negotiate liability caps that reflect the true potential cost of AI failures rather than accepting minimal vendor exposure limits. Ensure liability provisions cover both direct damages and consequential costs, such as regulatory fines, legal defense expenses, and reputational remediation efforts.

Building comprehensive AI governance through contracts

As vendors integrate AI more deeply into their service offerings, the boundary between their AI risks and your organizational exposure continues to blur. The contract clauses outlined here provide essential protection, but they represent just the foundation of a comprehensive AI risk management strategy.

Effective AI governance requires these contractual safeguards to work in coordination with robust internal oversight capabilities. This includes maintaining a comprehensive inventory of all AI systems operating within your business ecosystem, providing appropriate training for employees who interact with AI tools, and establishing clear policies for responsible AI usage across your organization.

The regulatory environment surrounding AI continues to evolve rapidly, with new requirements emerging across multiple jurisdictions. Legal precedents are still developing as courts grapple with questions of AI accountability and liability. Meanwhile, vendors will likely continue pushing to limit their own exposure while maximizing the AI capabilities they offer to clients.

Organizations that successfully navigate this landscape will be those that treat vendor contracts as integral components of their broader AI risk framework rather than afterthoughts to be handled by procurement teams. By embedding disclosure requirements, data protections, oversight mandates, and appropriate liability allocation into vendor agreements today, you create protective guardrails that will serve your business regardless of how AI technology continues to evolve.

The stakes are too high, and the risks too complex, to leave AI governance to chance. Smart contracting practices today can prevent tomorrow’s AI-related crises from becoming your organization’s problem.