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.