Artificial intelligence now sits at the center of many technology products, fundamentally reshaping how software functions, how data is processed, and how legal risk is allocated. Despite this reality, many companies continue to rely on legacy SaaS agreements that were drafted for deterministic software and are ill-suited for AI-enabled services. These traditional contracts rarely address how AI models are trained, how customer data is used in machine learning workflows, or how liability should be handled when AI outputs are probabilistic or context-dependent. As AI adoption accelerates, the absence of AI-specific contractual protections exposes companies to risk.
Traditional SaaS agreements assume static code bases and predictable performance. AI systems have disrupted these assumptions. Machine learning models evolve through retraining and fine-tuning based on large and dynamic datasets and often generate outputs that vary even when provided the same inputs. Additionally, vendors frequently reserve broad rights to use customer data for AI training and model improvement, and intellectual property ownership of AI-generated outputs remains unsettled under current copyright regimes. Without an AI services addendum, these risks are typically allocated by default to the customer through broad limitations of liability.
An AI Services Addendum is designed to modernize existing SaaS contracts by addressing risks unique to artificial intelligence services. Properly drafted, it defines what constitutes AI services as distinct from standard cloud software and establishes clear rules governing usage of data. It also allocates ownership or licensing rights in AI-generated outputs, addresses responsibility for model hallucinations, and incorporates compliance obligations tied to emerging AI regulations such as the EU AI Act and U.S. state AI laws, such as those passed in California and Colorado. Rather than replacing a SaaS agreement, the addendum recalibrates it to reflect how AI systems actually operate.
Absent AI-specific contractual language, several critical protections are commonly missing in a template SaaS agreement. Vendors may retain expansive rights to reuse customer data, including confidential or sensitive information, for ambiguously labelled “service improvements.” Output ownership may be impliedly reserved to the vendors, limiting a customer’s ability to commercialize AI-generated content. And compliance obligations related to transparency, bias mitigation, and human oversight are frequently omitted despite increasing regulatory scrutiny.
An AI Services Addendum is particularly important where AI outputs are customer-facing or deployed in regulated environments such as healthcare, employment, financial services, or education. In these scenarios, AI risk is not hypothetical; it is operational and continuous.
Beyond mitigating legal exposure, an AI Services Addendum provides strategic value by standardizing AI contract positions. As AI regulation and enforcement evolve, the Addendum also allows companies to update compliance obligations without renegotiating entire agreements.
AI fundamentally changes how technology contracts should be structured. Companies that continue to rely solely on legacy SaaS terms are contracting for a technological landscape that no longer exists. An AI Services Addendum offers a practical and efficient mechanism to align contractual risk allocation with the realities of AI-driven services.
Defining AI Services: How Precise Scoping Reduces Legal Risk
One of the most overlooked sources of risk in AI services agreements is the failure to clearly define scope. When AI services are described vaguely or bundled with traditional software functionality, contractual ambiguity increases. These scoping deficiencies often remain hidden until a dispute arises, at which point customers may discover that liability allocations are far less favorable than anticipated. In AI contracting, scope is not a formality; it is a risk-allocation mechanism.
AI services differ fundamentally from traditional software services, and scope provisions must reflect that distinction. In addition to describing features or deliverables, AI scope definitions must address the nature of the AI model, e.g., whether the system is predictive, generative, classificatory, or decision-support in nature, and how it is intended to be used. Scope provisions should disclose any training activities, identify the degree of model autonomy, and explain how and when the model may be updated. Without this precision, vendors often treat AI services as continuously evolving offerings while customers expect stable and predictable functionality.
Poorly scoped AI agreements frequently contain the same deficiencies. Training and inference may be conflated, obscuring whether customer data is used solely to generate outputs or also to train and improve the model. Additionally, vendors may reserve unilateral rights to modify models without notice. These gaps create uncertainty around customer expectations for the services and benefit only the vendor. As a practical matter, a “poor” scope provision, includes defining the scope of the AI services based solely on reference to a vendor’s to marketing materials or documentation, which it can update unilaterally. Scope should be anchored in the agreement itself, with incorporated documentation versioned and subject to notice or consent requirements for material changes.
A well-drafted AI scope provision should clearly define the AI system’s purpose and limitations. It should specify permitted and prohibited use cases, particularly where AI deployment could trigger heightened regulatory or ethical obligations. The provision should address whether customer data may be used for training, fine-tuning, or benchmarking, and should establish clear rules around model updates. Such a clear and precise scoping provision forms the foundation upon which customers can hold vendors more accountable for their appropriate share of liability for issues regarding data usage, IP ownership and infringement, and service levels, which are inherently invoked in the provision of AI services.