Commercial contracts are changing rapidly as companies embed artificial intelligence into their products, workflows, and decision systems. Vendors want flexibility to innovate and iterate, while customers want predictability, risk allocation, and meaningful recourse when AI creates unexpected outcomes. These dual pressures mean that AI terms in commercial contracts are now central to negotiations across technology, data services, manufacturing, professional services, e commerce, logistics, and countless other sectors.

Indemnity provisions are also evolving as organizations strive to manage new forms of operational and legal exposure tied to algorithmic outputs, training data, third party rights, cybersecurity, and the potential misuse of AI tools. Parties increasingly view indemnity language as a primary safeguard for allocating risk where insurance and technological controls may not be sufficient.

This article provides a comprehensive, practical, and legally grounded analysis of how AI terms and indemnity clauses intersect in modern commercial contracting. It describes why the issue matters now, outlines common risk categories, uncovers misconceptions, and offers guidance on when tailored legal counsel is especially valuable. Because AI contracting is still a developing area and rules can vary across jurisdictions, the discussion remains general and avoids references to specific statutes or regulatory authorities that do not appear in the context provided.

Key Takeaways

• Companies are incorporating AI at scale, which increases the importance of contract terms that govern training data, output reliability, ownership, confidentiality, and compliance obligations.
• Indemnity provisions tied to AI require careful drafting because many AI related risks do not fit neatly into traditional categories such as intellectual property infringement or personal injury.
• Parties must understand how AI systems operate and the limits of vendor control in order to allocate risk realistically and avoid overpromising on performance.
• Common misconceptions involve assumptions that AI vendors can guarantee error free outputs or that customers bear all responsibility for misuse.
• Legal counsel is particularly helpful when negotiating AI related indemnities, defining roles and responsibilities, or aligning contract terms with operational realities and emerging best practices.

Why AI Terms and Indemnity Provisions Matter Now

Businesses of every size are deploying AI tools to accelerate data analysis, automate customer service, optimize supply chains, or support creative and technical production. These tools generate significant productivity gains, yet they also introduce uncertainty about output accuracy, data integrity, privacy exposure, and the reliability of underlying models.

Commercial contracts drafted even a few years ago often did not contemplate the ways machine learning models may evolve after deployment, how training data affects outputs, or how generative AI tools may create content that resembles protected works. As AI systems improve and scale, contractual gaps directly increase risk for both vendors and customers.

Indemnity provisions are an essential tool for allocating these emerging risks. Historically, indemnity clauses focused on intellectual property infringement, bodily injury, or damage to tangible property. In AI related agreements, potential loss categories may include:

• Harm caused by inaccurate or biased model outputs
• Claims tied to data use or data rights in training or inference
• Reputational harm where AI generated content is misleading or inappropriate
• Contractual breaches involving model performance or security obligations
• Allegations that outputs resemble or replicate protected content

Each category presents unique challenges because AI technologies evolve autonomously in ways that traditional software does not. This evolution increases the importance of having clear AI terms that define responsibilities before issues arise.

Understanding the Legal Landscape for AI in Commercial Contracting

The legal environment for AI in commercial contracting is still developing. AI specific statutory frameworks exist in some jurisdictions, but many rules remain general and rely on established principles of contract law, tort law, intellectual property, consumer protection, and privacy law. Because jurisdictional variation is significant, companies should assume that the legal treatment of AI may differ based on location, industry sector, or the nature of the AI tool.

In the United States, general legal concepts guide negotiations where AI plays a central role. For example:

• Contract law principles govern the enforceability of representations, warranties, and disclaimers related to AI performance.
• Intellectual property doctrines shape the allocation of ownership rights in training data, model improvements, and AI generated outputs.
• Privacy and cybersecurity obligations influence how AI systems may collect, store, and process personal or sensitive information.
• Tort concepts may apply if algorithmic outputs cause harm to third parties, although the contours of liability are still evolving.

Because few jurisdictions have developed comprehensive AI statutory regimes, commercial contracts remain the primary way that companies define expectations and manage risk. This environment places additional pressure on indemnity language, which must anticipate legal uncertainty while remaining practical enough to enforce and implement.

Core AI Terms That Require Careful Drafting

AI terms in commercial contracts vary widely depending on the nature of the product or service. Still, several categories of terms appear consistently and warrant close attention.

Definitions of AI Systems, Data, and Outputs

Contracts should define key concepts to avoid disagreements about scope. Terms such as model, training data, input data, output, prompt, or system behavior may seem intuitive but can have different meanings depending on technical context. Clear definitions reduce ambiguity and help parties identify which risks the indemnity provisions should cover.

Data Rights and Training Data Use

One of the most sensitive issues involves the right to use data for training, fine tuning, or improving the vendor’s AI models. Customers often want assurances that their proprietary or confidential information will not be used to train systems accessible to others. Vendors may seek broad rights to use data to enhance the underlying technology.

Negotiations often center on:

• Whether customer data will be used for model improvement
• Whether data will be aggregated or anonymized
• Whether outputs could reveal underlying training data
• Whether the vendor must delete or return data at the end of the contract term

Each of these issues intersects with indemnity, because breaches of data rights can give rise to substantial third party claims.

Output Reliability and Limitations

AI systems can deliver remarkable results but are inherently probabilistic. Their outputs can change as models evolve or as new data is introduced. Vendors typically include disclaimers stating that outputs may be inaccurate and that the customer must supervise the system or validate results. Customers, especially in regulated industries, may require affirmative commitments that outputs meet certain standards of quality or accuracy.

Parties should consider:

• Whether there will be minimum performance standards
• How revisions to the AI model will affect performance commitments
• Whether the customer must implement human review
• Whether the vendor will provide audit logs, documentation, or explainability information

These terms often influence the scope of indemnity by clarifying which party is responsible when outputs lead to operational or legal issues. Vendors should be aware that broad disclaimers of output accuracy may face scrutiny under the Uniform Commercial Code’s implied warranties of merchantability and fitness for particular purpose, particularly when vendors market AI tools for specific use cases or industries.  To manage this tension, vendors should ensure that contractual disclaimers are appropriately tailored to the nature of the AI system, clearly identify the limitations inherent in probabilistic outputs, and align marketing materials and performance representations with the actual capabilities documented in the contract.  Where vendors cannot disclaim implied warranties entirely, contracts should specify that the vendor’s sole obligation for breach of warranty is to use commercially reasonable efforts to correct reported defects or, if correction is not feasible, to terminate the agreement and refund prepaid fees on a pro rata basis.

Ownership and Licensing of Outputs

Commercial agreements often need to specify who owns the rights to AI generated outputs. Customers may expect broad license rights, but complex questions arise where outputs are similar to training data or where model behavior might incorporate patterns from third party sources.

Ownership terms must align with the indemnity clause. If a customer expects the vendor to indemnify for intellectual property claims involving outputs, both parties must understand the limits of the vendor’s control over generative processes.

Confidentiality and Security Requirements

AI systems may process sensitive personal data, trade secrets, or regulated information. Contracts typically impose confidentiality obligations on both parties, and customers often require the vendor to implement reasonable administrative, technical, and physical safeguards. Breaches of confidentiality or security can trigger indemnity obligations, making it critical to describe requirements precisely.

Change Management and Model Updates

AI models evolve regularly. Contracts should address how updates will be communicated, whether performance will be reevaluated after updates, and whether the customer may opt out of certain changes. These provisions clarify operational expectations and help prevent disputes that may escalate into indemnity claims.

How Indemnity Clauses Function in AI Related Contracts

Indemnity provisions allow one party to compensate the other for specified losses arising from third party claims. In agreements involving AI, indemnity clauses can become highly nuanced because AI introduces uncertainty in ways traditional software does not.

Traditional Indemnity Categories

Most technology contracts include indemnification for:

• Intellectual property infringement
• Bodily injury or property damage
• Breaches of confidentiality
• Violations of law

These categories remain relevant for AI, but may not capture all scenarios involving algorithmic behavior.

AI Specific Risk Areas for Indemnity

AI related indemnity often focuses on risks such as:

Output Based Claims

If an AI tool generates content or decisions that cause harm, third parties may assert claims based on reliance or defamation, or on negligence related to output quality. Parties must decide whether the vendor, customer, or both bear responsibility for output supervision.

Data Rights and Unauthorized Use

If training data incorporates third party protected content without authorization, a customer may face claims based on their use of the AI system. Vendors may resist indemnifying against such claims if they do not have full control over training data origins.

Bias, Discrimination, or Improper Decision Support

AI outputs can reflect or amplify bias. Customers may face regulatory scrutiny or third party allegations if decisions about employment, housing, credit, or other sensitive matters rely on flawed AI outputs. Contracts should discuss how liability is allocated when bias related claims arise.

Security and Model Integrity

If a vendor fails to secure the AI system properly and a breach exposes customer data or model behavior, indemnity provisions may be triggered. Conversely, customers may be responsible if their misuse of the tool creates vulnerabilities.

Structuring Indemnity Obligations

Well drafted indemnity clauses describe:

• The specific claims covered
• The scope of losses, including legal fees, judgments, or settlements
• The excluded categories of claims, such as those arising from customer misuse
• The conditions for invoking indemnity, including prompt notice and control of defense
• Any caps or limitations on liability

In AI contracts, exclusions often focus on customer misuse, failure to review outputs, or deviation from usage guidelines. Caps on liability may be heavily negotiated because AI related risk is still uncertain and can involve reputational components that are difficult to quantify.

Allocation of Regulatory Fines and Penalties

AI-specific regulations increasingly impose direct penalties on companies that deploy non-compliant systems. The EU AI Act, for example, authorizes fines up to €35 million or 7% of global annual turnover for prohibited practices. Colorado’s AI Act subjects violations to penalties up to $20,000 per violation under the state’s consumer protection framework. These regulatory exposures create new categories of potential liability that traditional indemnity provisions may not adequately address.

Vendors should negotiate contract terms that clearly delineate responsibility for regulatory compliance and associated penalties. Where vendors provide AI tools according to documented specifications and usage guidelines, regulatory penalties arising from customer deployment decisions, data inputs, or use cases outside approved parameters should remain the customer’s responsibility. Conversely, penalties arising from defects in the AI system itself, failure to meet disclosed performance standards, or vendor violations of data handling requirements may warrant vendor responsibility.

In AI contracts, exclusions often focus on customer misuse, failure to review outputs, or deviation from usage guidelines. Caps on liability may be heavily negotiated because AI related risk is still uncertain and can involve reputational components that are difficult to quantify.  A common market practice is to carve IP indemnities out of general liability caps, meaning the indemnifying party’s exposure for IP claims remains unlimited even when other liabilities are capped.  Vendors should consider whether AI-specific IP indemnities, particularly for training data infringement claims, warrant the same uncapped treatment or whether the novel and uncertain nature of AI-related IP risk justifies negotiating for caps that apply across all indemnity categories.  Vendors may argue that because AI outputs are probabilistic and training practices are evolving, blanket exceptions for IP indemnity create disproportionate exposure that customers should share through capped indemnity structures or negotiated liability sharing arrangements.

Operational Implications of AI Indemnity Terms

Indemnity clauses do more than allocate legal risk. They influence how companies operate and govern their AI systems.

Vendor Operational Impacts

Vendors may need to:

• Implement quality assurance processes to validate model behavior
• Maintain documentation about training data sources
• Monitor model updates for unintended consequences
• Provide customer support to address output issues
• Purchase additional insurance products tailored to AI risks

These requirements encourage vendors to adopt responsible development practices while acknowledging that risk cannot be eliminated fully.

Customer Operational Impacts

Customers integrating AI into workflows may need to:

• Implement human oversight of AI generated outputs
• Train employees on appropriate AI use
• Maintain internal review processes for accuracy and bias
• Observe data sharing limitations
• Update compliance programs as AI evolves

These practices help customers avoid triggering indemnity exclusions and reduce the likelihood of third party claims.

Common Misconceptions in AI Contracting

AI contracting remains a developing field, leading to several misunderstandings that can complicate negotiations.

Misconception 1: Vendors Can Guarantee AI Accuracy

AI is probabilistic. Vendors usually cannot guarantee perfect accuracy or predict all outputs. Overly broad commitments can lead to unsustainable liability exposure. Contracts should reflect the inherent limitations of AI systems.

Misconception 2: Customers Bear All Responsibility for Misuse

Although customers play a role in supervising outputs and ensuring appropriate use, vendors also influence system design, training data, and default configurations. Fair allocation of responsibility requires recognizing each party’s sphere of control.

Misconception 3: AI Indemnity Should Mirror Traditional Software Indemnity

AI introduces novel risks, so traditional indemnity frameworks may be insufficient. New categories of exposure related to training data, bias, or generative outputs may require tailored treatment rather than reliance on historical templates.

Misconception 4: Indemnity Alone Will Address All AI Risk

Indemnity provisions allocate risk after a claim arises, but they do not prevent the underlying event. Effective risk management requires a combination of contractual, operational, and technical controls.

Misconception 5: AI Outputs Cannot Infringe Intellectual Property

Generative AI tools may create content resembling existing protected works, and this risk is being actively litigated in high-profile cases. Major lawsuits include The New York Times v. OpenAI (seeking billions in damages for alleged training data and output infringement), Getty Images v. Stability AI (alleging infringement of over 12 million photographs), and Disney & Universal v. Midjourney (the first major visual media copyright case against AI image generator.) While outcomes remain uncertain, vendors should ensure indemnity provisions clearly distinguish between claims arising from (a) use of copyrighted training data, (b) allegedly infringing outputs, or (c) both. Vendors may limit indemnity to claims arising solely from their training data practices, while requiring customers to accept responsibility for how they use and rely on outputs. Clear allocation of these distinct categories of risk helps vendors manage exposure while giving customers realistic expectations about protection.

Risk Management Strategies for AI Contracting

Companies can reduce exposure and improve contract outcomes by implementing structured risk management practices.

Conduct a Technical and Legal Assessment

Before integrating AI into workflows or signing a contract, organizations should assess how the tool functions, what data it processes, how outputs are generated, and what limitations exist. This knowledge helps identify which contract terms require careful negotiation.

Align Contract Terms with Actual Use Cases

Contracts should reflect the real world use of the AI system. If the system will support critical business decisions, the level of diligence and oversight required should match the significance of the risk.

Clarify Roles and Responsibilities

Clearly defined roles reduce disputes. If customers must provide accurate input data or follow specific review procedures, the contract should state these obligations plainly.

Use Insurance to Supplement Indemnity

Some companies obtain specialized insurance to cover technology related risks. Insurance does not replace indemnity, but it can provide a financial backstop for unforeseen claims.

Maintain Audit Trails and Documentation

Documentation helps demonstrate compliance with contractual requirements and may support defenses in the event of third party claims.

Anticipated Developments in AI Contracting

The legal landscape for AI is evolving rapidly. Although specifics will vary across jurisdictions and industries, several broad trends are likely.

Increased Regulatory Attention

Governments are actively adopting AI-specific regulations. The EU AI Act entered into force in August 2024, with prohibited AI practices effective February 2025, general-purpose AI model obligations effective August 2025, and high-risk system requirements applying by August 2026, backed by penalties reaching €35 million or 7% of global turnover. In the United States, Colorado’s AI Act (effective June 2026) imposes a duty of reasonable care on developers and deployers of high-risk AI systems to prevent algorithmic discrimination. NYC Local Law 144 (effective July 2023) requires bias audits for automated employment decision tools. Companies should monitor these developments and ensure  contract terms address regulatory compliance obligations, including which party bears responsibility for fines, penalties, and required disclosures.

More Sophisticated Performance and Reliability Metrics

Customers may demand more detailed performance descriptions, including permissible error rates, monitoring requirements, or transparency expectations. Vendors may respond by providing more structured disclosures about model limitations.

Expanded Use of Safe Use Guidelines

Contracts may incorporate detailed usage protocols that define how customers should deploy AI tools, what types of tasks require human supervision, and how to handle errors. These guidelines help allocate responsibility and reduce disputes.

Evolving Norms for Training Data Rights

As businesses become more aware of data provenance issues, contracts may include more detailed representations about data sourcing, permissible use, and model training practices.

Greater Emphasis on Explainability and Accountability

Customers may require access to information that explains how models reach conclusions, particularly in sensitive domains. Indemnity provisions may evolve to reflect how explainability impacts responsibility for decisions.

Because these developments are still emerging, companies should use caution when predicting future obligations and should work with counsel to evaluate how evolving norms may affect contracting strategies.

When Legal Counsel Is Especially Valuable

AI contracts require interdisciplinary understanding of law, technology, and operational risk. Companies often benefit from legal counsel when:

• Negotiating indemnity clauses involving AI outputs or training data
  
  
• Drafting performance standards for AI systems
  
  
• Addressing privacy, cybersecurity, or compliance obligations created by AI adoption
  
  
• Reviewing vendor policies related to monitoring, updates, or data governance
  
  
• Integrating AI tools into regulated industry environments
  
  
• Understanding how evolving regulatory expectations may impact future contract modifications
  
  

At Margolis PLLC, we help clients navigate these complexities by aligning contract terms with business objectives while managing risk in a practical, commercially reasonable way.

Frequently Asked Questions

What are the most important AI terms to include in a commercial contract?

Key terms typically include definitions of AI system components, data rights, training data permissions, output responsibilities, confidentiality obligations, performance expectations, change management provisions, and indemnity scope. These terms ensure both parties understand how the AI tool will be used and who bears responsibility for specific risks.

Does a vendor usually indemnify customers for AI generated errors?

Not always. Vendors often limit indemnity to traditional categories such as intellectual property infringement. Output related indemnities are heavily negotiated because vendors cannot fully control how AI models behave, especially when outputs depend on customer provided data or prompts. Whether such indemnity applies depends on the specific contract language.

How can customers reduce risk when adopting AI tools?

Customers can implement human review processes, document decision making procedures, provide accurate input data, monitor outputs for error or bias, and negotiate clear contractual obligations regarding data rights and performance. Operational diligence helps prevent issues that might lead to third party claims.

What happens if AI outputs resemble protected works?

The legal treatment of this issue is still developing. Parties often address ownership and use rights through licensing terms and may negotiate indemnity provisions for intellectual property related claims. Because generative AI can produce content influenced by training data, careful drafting helps clarify responsibility.

How do indemnity exclusions work in AI contracts?

Exclusions specify situations where indemnity does not apply, such as customer misuse, failure to follow usage guidelines, unauthorized modifications, or reliance on outputs without appropriate review. Exclusions help allocate responsibility based on each party’s control over system behavior.

Can companies impose performance guarantees for AI systems?

Parties may negotiate performance commitments, but such guarantees are typically bounded and include explanations of model limitations. Vendors often resist absolute accuracy guarantees because AI systems evolve and may behave unpredictably.

How do confidentiality obligations apply to AI systems?

AI systems often process sensitive data, so confidentiality terms should describe how data is handled, stored, and protected. Breaches of confidentiality can lead to indemnity obligations, especially if third parties suffer harm. The contract should specify both parties’ duties regarding secure data handling.

Should AI contracts address model updates?

Yes. Updates may change how outputs are generated or how well the system performs. Contracts should state how updates will be communicated, whether customers can opt out, and how updates interact with performance and indemnity commitments.

How do training data rights affect indemnity?

If training data contains unauthorized or protected content, customers may face claims related to their use of the AI system. Vendors and customers should negotiate how training data is sourced, what rights exist, and who is responsible for any infringement claims.

Are AI related risks insurable?

Some insurers offer technology liability coverage that may apply to AI related risks, although coverage varies. Insurance can complement indemnity by providing financial protection for certain categories of claims. Companies should evaluate coverage alongside contract terms.

Practical Synthesis for Businesses Adopting AI

AI terms and indemnity provisions are no longer niche considerations. They sit at the center of commercial negotiations because AI systems influence operational processes, reputational risk, and legal exposure. Businesses that adopt AI should understand how responsibilities are divided between vendors and customers and should evaluate risks in light of actual system behavior rather than assumptions. Contract terms should be precise, realistic, and aligned with technical capabilities and business objectives.

Indemnity clauses play a key role in allocating risk, but they cannot eliminate uncertainty. Effective risk management depends on the combination of thorough contract drafting, strong internal governance, transparent communication between contracting parties, and attention to the evolving legal landscape.

By approaching AI contracting with a structured, informed strategy, companies can capture the benefits of AI innovation while minimizing unexpected liabilities.

Disclaimer

This article provides general legal information and does not constitute legal advice. Laws and contractual obligations vary by jurisdiction and specific factual circumstances. Readers should consult an attorney for advice regarding their particular situation.

How Margolis PLLC Can Help

Margolis PLLC advises companies on drafting, negotiating, and updating AI related commercial contracts and indemnity provisions. We help clients balance innovation with risk management by providing tailored, practical guidance grounded in real world business needs. If your organization is integrating AI tools or revisiting contract templates to address AI related risks, we welcome the opportunity to support you.