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The Production Transformation

When AI systems move from pilot to production, the data usage model fundamentally changes. Production deployments operate differently than pilots—they focus on applying the already-developed AI capabilities rather than using customer data to improve the underlying technology. This shift requires completely different legal frameworks.

 

The New Production Paradigm

Production agreements have evolved toward a clearer separation of concerns:

 

• Developer IP Protection: The pre-trained and customized models developed during the pilot phase become the developer's core intellectual property, protected and licensed to customers.

• Input-Output Model: Production focuses on processing customer inputs to generate outputs, rather than using customer data to train or improve the base system.

• Opt-Out from Further Training: Customers can prevent their production data from being used to improve the general AI system, protecting competitive advantages and sensitive information.

• Sandbox Systems: Many production deployments operate in isolated environments where the customized model serves only that customer, without feeding improvements back to public or shared systems.

 

Managing Production Data Rights

Production data governance centers on four distinct categories:

 

• Input Data: Information customers provide for processing. Customers retain ownership while granting limited processing licenses. Unlike pilot data, this typically cannot be used for further model training without explicit consent.

• Output Data: Results generated by the AI system. Customers typically own these outputs, but agreements must address potential IP infringement and quality disclaimers.

• Usage Data: System performance metrics and operational data. Developers typically can collect anonymized versions for service improvement, but customers may often have veto rights over specific metrics.

• Training Opt-Out Rights: Customers can prevent their data from being used to improve the base AI system, ensuring their competitive advantages and sensitive information don't benefit competitors.

 

Sandbox and Isolated Operations

Production agreements commonly include provisions for isolated AI operations, building on sandbox options that may have been established during pilot phases:

 

• Dedicated Environments: The customized AI model operates in a separate environment serving only that customer, without sharing learnings or improvements with other deployments. This addresses customer concerns about sensitive data contributing to competitors' capabilities.

• Version Control: Customers may receive their own version of the AI system that doesn't automatically update with general improvements, maintaining consistency and control.

• Data Isolation: All customer data processing occurs in isolated systems with no cross-contamination between different customer environments. This can include pre-processing anonymization for any data that might be used for general system improvements.

• Hybrid Approaches: Some agreements allow customers to contribute anonymized data to general improvements while keeping sensitive data in isolated systems, balancing innovation with protection.

 

Production Service Frameworks

Production agreements must address operational realities:

 

• Service Level Agreements: Availability, performance, and response time commitments specific to production workloads, typically requiring very high uptime.

• Update Management: How base model updates are tested and deployed, including customer rights to refuse updates that might change system behavior.

• Performance Monitoring: Regular testing and quality assurance, with specific remediation procedures when performance degrades.

• Support Structures: Technical support that goes beyond break-fix to include optimization, configuration management, and performance tuning.

 

Compliance and Documentation

Production deployments require comprehensive compliance frameworks:

 

• Training Documentation: Complete records of what data was used during development phases, essential for regulatory compliance and IP defense.

• Regulatory Compliance: Adherence to AI Act requirements (EU), fair use documentation (US), and industry-specific regulations.

• Audit Rights: Customer ability to review system operations, data handling practices, and compliance procedures.

• Data Retention Policies: Clear timelines for how long different types of data are retained and when they must be deleted.

 

Commercial Models for Production

Production pricing reflects the shift from development to operations:

 

• Licensing Models: Customers license the customized AI system rather than paying for ongoing development, with pricing based on usage, users, or value delivered.

• Maintenance and Support: Ongoing fees for system maintenance, updates, and technical support, separate from the core licensing.

• Isolation Premiums: Additional costs for dedicated environments and isolated operations that prevent data sharing with other customers.

• Opt-Out Implications: Customers who opt out of data sharing for improvements may face higher pricing since they don't contribute to general system enhancement.

 

Risk Management in Production

Production agreements must address operational risks:

 

• Liability Allocation: Clear division of responsibilities between developer technical issues and customer use case compliance.

• Insurance Requirements: Appropriate coverage for both parties, including specific AI liability provisions where available.

• Indemnification: Protection against IP claims, with appropriate carve-outs for customer-specific use cases and data.

• Business Continuity: Procedures for maintaining service during developer issues, including potential escrow arrangements for critical deployments.

 

Key Production Agreement Elements

Comprehensive production agreements should address:

 

1. Service Definition: Detailed specifications for the AI system and its capabilities, including sandbox vs. shared operation modes

2. Data Governance: Clear frameworks for input, output, usage data, and training opt-outs, with options for anonymization procedures

3. Operational Models: Sandbox systems, version control, isolation requirements, and data flow controls

4. Compliance Framework: Documentation requirements and regulatory obligations from both pilot and production phases

5. Commercial Terms: Licensing, maintenance, and isolation pricing models (sandbox operations may carry premium pricing)

6. Risk Allocation: Liability, insurance, and indemnification structures

7. Performance Management: SLAs, monitoring, and remediation procedures

8. Change Control: How updates, modifications, and improvements are managed while respecting isolation choices

Production agreements must balance the operational benefits of shared learning systems with customer needs for competitive protection and data sensitivity, often through tiered service models that offer different levels of isolation and data sharing.

 

Future-Proofing Production Relationships

Production agreements must accommodate ongoing evolution:

 

• Technology Refresh: Rights to new model versions and capabilities, with customer control over adoption timing.

• Regulatory Adaptation: Collaborative procedures for addressing new compliance requirements without automatic cost pass-through.

• Scope Evolution: Frameworks for expanding usage, adding new capabilities, or modifying service levels as needs change.

• Exit Strategy: Data portability, system transition, and knowledge transfer procedures for relationship termination.

 

Conclusion: The Evolution from Development to Operations

The transition from pilot to production represents a fundamental shift in AI partnerships. Pilot agreements focus on collaborative development, typically requiring substantial data utility to prove AI value through fine-tuning, prompt engineering, RAG, or agent workflows. However, even in pilot phases, parties can choose sandbox environments and anonymization procedures to protect sensitive information while maintaining development effectiveness.

 

Production agreements focus on operational deployment of already-developed capabilities, with customer data used primarily for processing rather than training. Production commonly emphasizes opt-out rights, isolated operations, and data protection measures that may have been established during pilot phases.

 

This evolution requires sophisticated legal frameworks that protect developer IP while giving customers control over their data usage and operational isolation. The most successful agreements recognize that parties need choices: maximum data utility during development phases balanced with sandbox options and anonymization procedures that protect competitive advantages and sensitive information.

Organizations that structure this transition thoughtfully—with agreements that provide clear choices about data sharing, operational isolation, and competitive protection—position themselves for long-term success in AI-enabled business transformation while maintaining appropriate control over their sensitive information and competitive advantages.

Do you need help starting your AI pilot or moving to production? Contact the author to discuss how to structure agreements that protect your data, IP, and competitive advantage.