Before diving into contract terms, it is worth establishing a shared vocabulary. When this series refers to “artificial intelligence,” it means data-driven systems that use machine learning models, large language models, and related tooling to generate or inform outputs used in business processes. That definition is intentionally broad because the contracting challenges it creates are equally broad.
Why AI Vendor Transactions Are Not Just Another SaaS Deal
Experienced technology lawyers have spent years refining playbooks for software-as-a-service agreements. Many of those instincts remain useful, but AI vendor transactions introduce a distinct constellation of risks that traditional SaaS contracting frameworks were never designed to address.
First, there are data use and model training issues. In a conventional SaaS arrangement, customer data is stored, processed, and returned. In an AI engagement, the vendor may seek to use that data — or derivatives of it — to train, fine-tune, or improve models that serve other customers or the vendor’s own products. The line between “processing” and “learning from” customer data is one that the contract must draw with precision.
Second, AI systems produce unpredictable outputs. A SaaS platform generally returns deterministic results: the same query produces the same answer. AI models, by contrast, can generate different outputs from identical inputs, and those outputs can be wrong, biased, or infringing in ways that are difficult to anticipate at the time of contracting.
Third, the pace of change is fundamentally different. Rapid iteration cycles mean the model a customer evaluates during procurement may bear little resemblance to the model running in production six months later. Versioning, change management, and the right to test new versions before they go live become essential contract terms rather than afterthoughts.
Fourth, model behaviors are often opaque. Even the vendor may not be able to fully explain why a model produced a particular output. This opacity has profound implications for compliance, auditability, and liability allocation.
Fifth, intellectual property ownership and infringement concerns are uniquely acute. Who owns the outputs? What if those outputs infringe third-party rights because of what the model ingested during training? Traditional IP warranties and indemnities need substantial reworking to address these questions.
Sixth, data privacy and breach concerns take on new dimensions when models can memorize and regurgitate personal data, when multimodal tools process biometric information, and when model inversion attacks can extract training data from a deployed system.
Finally, regulatory scrutiny is intensifying across jurisdictions. Federal and state legislatures, the FTC, sector-specific regulators, and international bodies are all converging on AI governance, creating a compliance landscape that is both fragmented and fast-moving.
The throughline for counsel is this: every one of these risks can and should be mapped to specific contract terms and internal controls. That mapping exercise is the backbone of this series.
The AI Tool Landscape: What Counsel Actually Encounters
Before negotiating a single clause, counsel needs to understand what category of AI tool is on the table, because the category materially affects the contracting posture.
Foundation models and model-as-a-service platforms sit at one end of the spectrum. These are the providers offering APIs for text, image, and multimodal generation. The customer sends data in, and the model sends outputs back. The contracting focus here is on data exposure, training restrictions, output ownership, and the vendor’s downstream use of inputs and outputs.
Verticalized AI applications are the next layer. These are purpose-built tools that apply AI to a specific domain — contract analysis, e-discovery prioritization, customer support assistants, marketing content generation, or coding assistants. Because these tools operate in a defined context, the risks are somewhat more bounded, but the stakes can be higher. An e-discovery tool that hallucinates a document summary creates litigation risk. A coding assistant that introduces open-source contamination creates IP risk.
Embedded AI within existing SaaS or operational technology is increasingly common and increasingly invisible. Recommendation engines, predictive scoring, and anomaly detection are being layered into platforms that customers already use, sometimes without a clear contractual framework governing the AI components. Counsel should be alert to mid-contract feature additions that introduce AI capabilities without corresponding contractual protections.
Agentic and workflow automation layers represent a newer and more complex category. Orchestration tools and Retrieval-Augmented Generation systems that chain multiple AI calls together, access external data sources, and take actions on behalf of the user introduce compounding risks. Each link in the chain is a potential point of failure, data leakage, or unauthorized action.
Finally, deployment models matter. On-premises or private cloud deployments offer more control over data residency and access but come with their own operational burdens. Multitenant hosted offerings are more convenient but raise questions about data segregation. Fine-tuning and custom model hosting arrangements sit somewhere in between, creating a model that is partly the vendor’s and partly the customer’s — and the contract must sort out who owns what.
How Categories Shape Contracting Posture
Each of these categories shifts the dials on five key contracting dimensions. Data exposure varies enormously: a foundation model API that processes raw customer data in a multitenant environment is a different risk profile than an on-premises anomaly detection engine that never sends data off-site. Model control — the customer’s ability to influence, test, and constrain the model’s behavior — depends on whether the customer is consuming a general-purpose API or has fine-tuned a model on its own data. Auditability ranges from near-impossible with opaque foundation models to highly tractable with embedded AI in platforms that already have robust logging. Explainability is a function of model complexity, and the contracting implications are sharpest in regulated industries where decisions must be justified. Finally, allocation of responsibility for outputs must reflect who chose the model, who provided the data, who configured the guardrails, and who reviewed the results before acting on them.
Understanding where a particular vendor engagement falls on this spectrum is the first step toward drafting a contract that addresses the right risks with the right tools. The articles that follow will provide the framework for doing exactly that.