A vendor’s promise that its AI tool will improve productivity is no longer enough. The buyer must know what data enters the system, where it is processed, whether the model can be changed after deployment, and who carries the cost when regulatory scrutiny follows. Emerging regulation for AI contracting is turning those questions into core commercial terms.
For companies buying, developing, or embedding AI, the contract is where legal obligations become operational controls. A generic software agreement, a short data-processing addendum, and a supplier’s standard assurances will rarely provide adequate protection. The commercial objective is clear: secure useful technology without accepting unpriced compliance, data, and liability exposure.
Why AI Regulation Is a Contract Issue
AI regulation is often discussed as a compliance exercise. For business leaders, it is also a risk-allocation exercise. Laws may impose obligations on providers, deployers, importers, distributors, and employers. But regulators will assess actual conduct, while customers, subcontractors, and insurers will look first to the contract when deciding who must act, pay, notify, remediate, or defend a claim.
The EU AI Act is particularly relevant to businesses operating in or selling into the European market. Its obligations are being phased in, with different requirements depending on the system, the parties’ role, and the relevant timeline. Some prohibited AI practices already face restrictions, while governance duties for general-purpose AI models and high-risk systems bring more demanding documentation, oversight, monitoring, and incident-management expectations.
That framework does not replace existing law. GDPR, cybersecurity rules, consumer protection requirements, sector-specific regulation, intellectual property law, procurement rules, employment law, and confidentiality obligations can all apply to an AI transaction. A contract that addresses only the AI Act may still leave a material gap.
The First Question: What Is Being Bought?
An AI contract should begin with a precise description of the solution. “AI-enabled services” is commercially convenient language, but legally thin. The agreement should distinguish between a general-purpose model, a model embedded in a business application, a tool that generates content, a decision-support system, and a system that makes or materially influences decisions about people.
This classification matters because risk depends on use. An internal drafting assistant used with non-sensitive material presents a different profile from a system screening job applicants, evaluating creditworthiness, supporting medical decisions, monitoring workers, or ranking bidders in a procurement process. The same underlying technology can fall into a different risk category when the use case changes.
The contract should define the approved purpose, user groups, deployment environment, input types, and prohibited uses. It should also state whether the buyer may connect the tool to internal systems, use it with personal data, or permit affiliates and subcontractors to access it. These are not technical footnotes. They determine whether the vendor’s representations, security commitments, and indemnities are fit for purpose.
Do Not Contract Against a Moving Target
Many AI products change frequently. Models are updated, safety settings are adjusted, sub-processors are added, and functionality expands. A supplier may describe those changes as ordinary service improvements. For the customer, they may alter accuracy, data flows, regulatory status, or operational reliance.
The agreement should require advance notice of material changes and give the customer defined remedies where a change creates a compliance, security, or performance concern. Depending on the system’s significance, those remedies may include testing rights, a suspension right, a rollback option, termination, or transition assistance. The appropriate level of control depends on the use case. It is stronger where AI affects regulated decisions, critical infrastructure, public procurement, or sensitive data.
Data Rights Must Be Specific
The central commercial question in many AI transactions is simple: can the provider use customer data to train, improve, evaluate, or operate its models?
Silence is not a safe answer. Contracts should separate service delivery from model training and product improvement. If a provider seeks rights to use prompts, outputs, metadata, feedback, or uploaded files, the agreement should identify the data categories, purposes, retention period, security safeguards, geographic processing locations, and onward transfers involved.
For personal data, the parties must assess their respective GDPR roles rather than relying on labels selected by the vendor. A standard data-processing agreement may be necessary, but it does not resolve every issue. Organizations should also consider lawful basis, transparency, data minimization, automated decision-making restrictions, international transfers, and whether special category data can enter the system.
Confidential business information requires equal discipline. In construction, infrastructure, M&A, tenders, and disputes, prompts may contain pricing, designs, technical specifications, bid strategy, evidence, or privileged communications. The contract should prohibit unauthorized reuse and require segregation measures appropriate to the service model. It should also make clear that customer inputs do not become a source of competitor advantage.
Allocate Compliance Duties Before an Incident
A supplier statement that its product is “compliant” has limited value unless the contract identifies the standard, scope, and consequence of noncompliance. Compliance commitments should be tied to applicable law, the agreed use case, and the supplier’s role in the supply chain.
For systems that may trigger heightened AI obligations, the provider should be required to maintain relevant technical documentation, instructions for use, testing evidence, quality controls, logging capabilities, and post-market monitoring processes. The customer, in turn, should accept only those deployment duties it can realistically perform, such as following documented instructions, maintaining human oversight, or reporting serious incidents.
This allocation cannot be copied from a template. A company integrating an AI model into its own product may need extensive audit and documentation rights. A company using a low-risk tool for internal administrative support may prioritize confidentiality, data controls, and exit rights instead. Precision is more valuable than broad language that neither party can operationalize.
Audit Rights Need a Commercial Design
Buyers often ask for unrestricted audit rights; vendors often reject them. Neither extreme is usually effective. A better approach combines documentary evidence, independent assurance reports, targeted follow-up questions, and on-site or third-party assessment rights when a material risk, incident, regulator inquiry, or contractual breach justifies escalation.
For high-impact deployments, audit provisions should cover data governance, security, model change management, bias and performance testing where relevant, subcontractor controls, incident records, and evidence supporting regulatory claims. The customer also needs a right to obtain information quickly enough to respond to authorities, affected users, insurers, or business partners.
Accuracy Is Not the Same as Accountability
AI providers commonly limit warranties for outputs, and for good reason. Probabilistic systems can produce incorrect, incomplete, biased, or fabricated results. A customer should not expect a vendor to guarantee that every generated output is correct.
That does not mean the provider should avoid accountability. The contract can require the supplier to disclose known material limitations, provide reasonable instructions for safe use, maintain agreed performance standards, correct defects, and avoid misleading claims about capability or regulatory status. Where the tool is used for a critical process, parties may also agree testing criteria, acceptance procedures, escalation paths, and human review requirements.
Liability provisions deserve particular attention. A low cap tied to a few months of fees may be commercially unacceptable if the tool processes sensitive data, supports regulated decisions, or creates major intellectual property exposure. Different caps or exclusions may be appropriate for confidentiality breaches, data protection failures, willful misconduct, infringement claims, regulatory fines where legally recoverable, and breach of agreed security controls.
Indemnities should be equally focused. A customer may seek protection against third-party claims arising from the supplier’s technology, unauthorized data use, or intellectual property infringement. The supplier will reasonably resist liability for customer-provided content, unauthorized uses, or modifications outside its control. The agreement should draw that line expressly, not leave it for a dispute after deployment.
Procurement and Public-Sector Uses Raise the Stakes
In public procurement and infrastructure projects, AI contracting involves an additional layer of scrutiny. Contracting authorities and bidders must consider equal treatment, transparency, confidentiality, recordkeeping, and the integrity of evaluation processes. AI-generated analysis cannot become an unexamined substitute for accountable human decision-making.
Contractors using AI to prepare tenders, manage claims, analyze project records, or support FIDIC disputes should also control the quality and confidentiality of outputs. An inaccurate contractual interpretation or an exposed bid document can have consequences far beyond the subscription fee. Internal use policies and supplier terms should work together.
Build an AI Contracting Playbook
Organizations do not need to negotiate every low-risk AI purchase as if it were a major technology acquisition. They do need a disciplined intake process that identifies the tool, data, use case, business owner, risk level, and applicable approval path before signatures are exchanged.
A practical playbook usually includes a risk-tiering model, approved contractual positions, a data and security questionnaire, rules for employee use, and an escalation route for high-risk deployments. It should also assign responsibility after signing. Legal cannot monitor every model update; procurement cannot assess every data-processing issue; business teams cannot carry compliance obligations without clear instructions.
Sora & Associates supports businesses that need AI terms to withstand regulatory review, commercial pressure, and potential disputes. The strongest agreement is not the longest one. It is the agreement that assigns each material risk to the party best placed to control it, with evidence, remedies, and decision rights that can be used when the project is under pressure.
Before approving the next AI contract, ask one practical question: if the tool causes harm, changes materially, or attracts a regulator’s attention tomorrow, does the agreement tell your team exactly what happens next? If the answer is no, the negotiation is not finished.