As artificial intelligence (AI) and machine learning (ML) become central to modern technology services, vendors increasingly rely on these systems to refine performance and deliver greater value. Yet many customer contracts now restrict or prohibit vendor data use—limiting the very learning that drives product improvement. These provisions can unintentionally compromise a vendor’s intellectual property, scalability, and enterprise value.
Vendors should proactively negotiate data-use rights that protect both innovation and customer confidence. Clear, limited licenses—coupled with confidentiality safeguards—allow vendors to evolve their tools responsibly while maintaining legal compliance and client trust.
Protecting the Vendor’s Right to Learn
Machine learning tools depend on exposure to data to improve accuracy, efficiency, and reliability. In early AI adoption, vendors addressed this need through contractual language granting limited licenses to use customer data for system improvement and benchmarking. This structure balanced the vendor’s need to innovate with the customer’s need for confidentiality and control.
In recent years, however, large enterprise customers—particularly in regulated industries such as life sciences and financial services—have adopted more restrictive terms. Many contracts now prohibit any vendor data use beyond the direct performance of services. While the intent is to protect sensitive information, the effect often undermines the vendor’s operating model.
Because ML systems cannot easily isolate data on a customer-by-customer basis, these prohibitions can render the technology nonfunctional. Vendors risk breach of contract by allowing their tools to learn, or they must disable learning altogether. Both outcomes reduce system efficacy and long-term value.
Addressing Customer Concerns
Customer hesitation is understandable. Organizations do not want proprietary data indirectly benefiting competitors. However, most vendor use of data is indirect, anonymized, and statistical. These systems identify trends and performance patterns—they do not expose client-specific information.
Effective communication can bridge this gap. When vendors involve both legal and technical stakeholders in negotiation, customers are more likely to appreciate the limited and controlled nature of AI-driven data use.
Essential Contractual Safeguards
To preserve operational flexibility and enterprise value, vendors should incorporate the following elements into customer agreements:
- Limited Improvement License – Permit the vendor to use, process, and analyze customer and system-generated data to operate, secure, and improve the services, develop enhancements, and create de-identified analytics or benchmarks.
- Carve-Out from Conflicting Clauses – Ensure these rights are expressly excluded from broad confidentiality or “sole use for customer” provisions.
- Defined Safeguards – Require de-identification, aggregation, and strict prohibitions on re-identification or customer-specific disclosures.
- Ownership Clarity – Confirm that the customer retains ownership of its data, while the vendor owns its models, methodologies, and derivative works (with a license to such data solely to the extent necessary for the use, exploitation, or creation of such models, methodologies, or derivative works).
- Regulatory Compliance – Align data-use rights with applicable data privacy, protection, and security standards.
AI and ML systems deliver continuous value through learning. Restricting that learning not only weakens the technology but also erodes the vendor’s strategic and financial position. Protecting data-use rights through disciplined contract negotiation ensures compliance, builds customer trust, and preserves enterprise value.
OlenderFeldman LLP has extensive experience structuring and negotiating these provisions to protect vendor assets and preserve long-term value. For counsel or collaboration, contact Craig D. Bronsnick at .
