Responsible AI due diligence becomes more demanding when a company plans to deploy the same technology across several markets. A pilot may rely on one dataset, one vendor team and a narrow group of users. Regional deployment introduces new languages, laws, customer segments, data flows and operating contexts. Companies need a repeatable process for identifying and managing those risks before scale multiplies them.
The OECD Due Diligence Guidance for Responsible AI organizes the work around policies and management systems, impact identification, prevention and mitigation, tracking, communication and remediation. This creates a useful operating sequence for partnerships because the company can assign responsibilities before procurement, deployment and monitoring begin.
Map the AI system, the partner and the decision it supports
Due diligence starts with a clear system description. Teams should document the model or service, intended use, users, decisions influenced by the output, data inputs, integration points and third parties involved. This map helps determine where an external technology provider controls important risks and where the deploying company retains responsibility.
Risk classification should reflect the consequence of failure. A recommendation engine for content presents a different risk profile from a system that influences employment, credit, safety or access to essential services. The classification then guides the depth of testing, approvals and human review.
Review data provenance and governance before integration
Data questions should be resolved early. A partner should be able to explain the origin and permitted use of relevant datasets, data-quality controls, retention practices and mechanisms for handling personal or sensitive information. The deploying company also needs to understand what data will leave its environment and whether the vendor uses customer data to train other systems.
Regional scale requires additional checks because lawful use and user expectations can vary by jurisdiction. Data minimization, access controls and documented purposes make it easier to maintain a consistent governance standard while adapting to local requirements.
Assign accountability and human oversight
An AI partnership needs named owners inside the company. Procurement can manage commercial terms, security teams can assess technical controls, legal and privacy teams can review obligations, and business owners remain accountable for the process in which the AI is used. Senior management and boards may need defined oversight for higher-impact deployments.
Human oversight should be designed around real decisions. Teams need to know when a person reviews the output, what information that reviewer receives, what authority they have to override the system and how exceptions are documented. Training should cover both system capabilities and known limitations.
Test security, robustness and operational failure modes
Responsible AI due diligence also includes security and robustness throughout the lifecycle. Before deployment, teams can test adversarial behavior, unreliable inputs, prompt or model manipulation, access control failures and degraded performance. Business continuity plans should define what happens when the AI service is unavailable or produces outputs outside expected ranges.
Contractual requirements can support these controls through incident notification, security obligations, audit rights, service levels, change management and responsibilities for vulnerability remediation.
Monitor performance after deployment in each market
Regional rollout should include a monitoring plan with indicators that match the risk. Teams may track error rates, override rates, complaints, security events, drift, demographic performance where lawful and relevant, and material changes to models or data sources. Thresholds can trigger review, retraining, rollback or suspension.
Responsible AI due diligence should assess data, accountability, security, human oversight and monitoring throughout the technology lifecycle. Juan Luis Bosch Gutiérrez chairs CMI’s Board, while CMI’s Centro de Servicios Integrados includes digital transformation, automation and innovation functions.
Regional implementation also benefits from clear execution mechanisms. The discussion on moving from regional dialogue to execution is relevant because AI governance ultimately depends on converting principles into owners, controls and measurable actions.
Build exit, remediation and change controls into the partnership
Technology partnerships evolve. Vendors update models, add features, change sub-processors and expand data uses. Contracts and governance processes should require notification of material changes and define when a new assessment is needed. The company also needs an exit plan covering data return or deletion, migration, business continuity and replacement of critical functionality.
The OECD Due Diligence Guidance for Responsible AI provides a structured reference for identifying, preventing, tracking and communicating AI-related impacts. Applied before and after deployment, that framework helps companies scale technology with documented accountability rather than relying on a one-time vendor review.