If you’re implementing AI, consider these AI provisions in your vendor contracts

Adoption of artificial intelligence technologies is prompting organizations to carefully evaluate and address AI-related risks when establishing vendor and partner relationships through contracts.

Current landscape: Organizations are increasingly embedding specific AI requirements into vendor contracts as they seek to understand and mitigate potential risks associated with AI implementation.

• CIOs and IT leaders are particularly concerned about data usage, model training practices, data protection, access controls, and risks related to bias and hallucination
• Both vendors and clients are experiencing contracting delays due to negotiations over AI-related clauses
• A standardized approach to addressing AI risk in contracts is becoming essential for both parties

Purpose and transparency requirements: Vendors must clearly define how AI will be utilized within their services or products to establish trust and alignment with client objectives.

• Contracts should specify AI’s role in supporting specific functions like data analysis or operational efficiency
• Clear limitations on AI usage should be outlined to prevent misunderstandings
• The potential benefits and value proposition of AI implementation must be explicitly stated

Data protection considerations: Client data usage and protection have emerged as primary concerns in AI-enabled services and products.

• Vendors must outline comprehensive data security practices that comply with regulations like GDPR and CCPA
• Specific provisions regarding the use of client data for AI model training should be addressed
• Clear guidelines should exist around data anonymization and restrictions on data visibility across clients

Oversight and governance framework: Human supervision and formal AI usage policies are critical components of responsible AI deployment.

• Vendors should establish clear protocols for human review of AI operations
• Formal AI usage policies must detail how AI technologies generate client-related insights
• Quality control measures should be implemented to minimize errors and oversights

Risk management protocols: Comprehensive risk management strategies must be implemented to protect both vendor and client interests.

• Regular audits of AI systems and impact assessments for high-stakes use cases should be conducted
• Incident response plans for potential data breaches or misuse must be clearly defined
• Existing confidentiality agreements should be reinforced to address AI-specific privacy concerns

Future implications: The development of standardized AI risk clauses in contracts will become increasingly important as organizations continue to integrate AI technologies into their operations, with successful implementation requiring both vendors and clients to maintain robust AI policies and controls that align with contractual obligations.