When AI changes the deal: Contracting beyond legacy SaaS

As artificial intelligence tools move from experimentation to enterprise deployment, contracting strategies need to move just as quickly. Traditional technology and SaaS agreements were not built for probabilistic outputs, evolving models, autonomous functionality or complex data-use risks. For organizations buying, selling or implementing AI-enabled solutions, the contract has become a critical governance tool.

That was the central theme of our recent webinar, Papering AI: Contracting strategies for a fast-moving AI market. The discussion highlighted why AI contracting playbooks must evolve beyond legacy SaaS concepts and better reflect the operational, data, liability and governance risks that AI creates.

Why legacy SaaS paper is no longer enough

For years, technology contracts have centered on predictable software performance: uptime, support, defined ownership and familiar data processing terms. AI changes that calculus. Outputs may vary, models can drift, vendors may depend on upstream foundation models, and customer data may be used in ways that traditional “product improvement” language does not clearly address.

Contract architecture matters

AI-specific terms may appear in the main agreement, a data protection addendum, a standalone AI addendum or linked product terms. Wherever they live, consistency is key. Fragmented provisions can create conflicting obligations or leave critical points unresolved.

A practical approach is to use an AI addendum supported by a particulars exhibit that identifies each tool, its purpose, the data involved, whether outputs support consequential or automated decisions and any required human review. For autonomous agents, the contract should also document permitted actions, escalation triggers and override rights.

Training data rights are becoming the defining battleground

Training data is one of the most heavily negotiated AI contracting issues. Buyers increasingly seek to prohibit use of customer data to train general-purpose or shared models, or require express authorization, de-identification or anonymization before any training occurs. Vendors often seek broader rights to improve their products using outputs, metadata or usage patterns.

The contract should clearly state what data can be used, for what purpose, in what environment and for whose benefit. Buyers should also consider training kill switches, deletion commitments and monitoring obligations that extend across the AI stack.

Foundation models create flow-through risk

Many AI vendors build on third-party foundation models. Customers should consider requiring disclosure of those models and providers, notice before material updates or substitutions, change-control procedures where appropriate and vendor responsibility for failures across the full technology stack.

AI performance commitments need to measure more than uptime

A high uptime commitment provides little comfort if an AI tool produces confidently wrong answers. AI service levels should address the risks that matter most to the use case, including accuracy, relevance, latency, hallucination rates, model drift and, for agentic AI, the quality of autonomous actions.

Remedies should also move beyond standard service credits and may include suspension of autonomous functionality, human override rights, model rollback, root-cause analysis, revalidation, chronic-failure termination and transition support.

Liability and indemnity should reflect AI-specific exposure

AI can generate harmful, infringing, inaccurate or discriminatory outputs at scale, while agentic AI may act before human intervention. As regulatory frameworks develop, contracting parties should revisit whether traditional limitation-of-liability caps, disclaimers and indemnities are adequate.

Privacy and security require an AI-specific lens

AI contracting sits at the intersection of data protection and data maximization. Data protection addenda should be reviewed for AI-specific red flags, including vague product-improvement rights, training on personal information, insufficient deletion obligations and limited support for data subject rights.

Not every AI deal needs the same paper

The goal is not to over-paper every AI procurement. A low-risk assistive tool may require only targeted rider language, while higher-risk deployments involving sensitive data, customer-facing outputs, regulated use cases or autonomous action may require a full AI schedule with governance, validation, audit, escalation, enhanced liability and transition provisions.

The emerging imperative is to match the complexity of the contract to the reality of the AI use case. Under-papering high-risk AI can leave critical exposure unallocated; over-papering low-risk tools can slow adoption without materially reducing risk.

Key takeaways

1. Supplement legacy SaaS templates with AI-specific terms.
2. Document the AI tool, use case, data types, human review and automated decision-making.
3. Lock down training data rights, deletion obligations and model-change controls.
4. Rethink SLAs to address accuracy, drift, hallucinations and autonomous action quality.
5. Revisit liability caps, indemnities and remedies for AI-specific risks.
6. Update vendor management, incident response, acceptable use and privacy review processes for AI-enabled tools.

For a deeper discussion of these issues and practical strategies for negotiating AI-related contract terms, watch the full Papering AI: Contracting strategies for a fast-moving AI market webinar on demand.

This post was co-authored by Matt San Roman and Denver Ellison.