A well-polished AI solution is an investment, not a simple purchase. But the excitement surrounding a new technology often overshadows the core task of procurement: risk management.

When an AI vendor presents a case study, they are naturally selling their best-case scenario. As a business leader, your responsibility is to vet the worst-case scenario. You need to look past the slick demo and probe the fundamentals: How is our data protected? Who is liable when the model fails? What happens five years from now?

Most procurement departments still use checklists designed for traditional software. AI, however, introduces unprecedented complexities regarding data governance, intellectual property, and algorithmic fairness. It requires a different, more forensic level of inquiry.

To help your team move from a superficial sales conversation to a critical technical assessment, we have compiled a checklist of 15 essential, pointed questions. These are structured to expose weak governance, security gaps, and a lack of long-term planning, positioning your organization to make a decision based on concrete evidence, not just enthusiasm.

Part I: Data, Security, and Intellectual Property

Data is the lifeblood of any AI system, and your greatest liability. These questions clarify ownership, protection, and usage rights.

1. What specific encryption standards (at rest and in transit) are used for our data, and where is the data physically hosted?

Any vendor will confirm they use encryption, but the answer must be specific. Demand to know the protocol (e.g., AES-256) and key management strategy. Crucially, the physical location of the data servers must align with your industry’s data sovereignty and jurisdictional requirements (e.g., GDPR, CCPA). Vague responses about “cloud security” are inadequate.

2. Is our input data or the resulting output used to train or improve your general models?

This is one of the most critical legal and competitive questions. If your proprietary business data or the model’s output (which may contain confidential information) is being fed back into the vendor’s central model, your organization may be giving away a competitive advantage. The answer must be a definitive “No,” backed by contractual language, if data privacy is paramount.

3. Who maintains ownership of the intellectual property (IP) created by the AI output?

While you own the input, the IP rights for the novel content, code, or images generated by the AI are complex. Clarify whether the contract explicitly assigns full and exclusive ownership of the output to your company. Any ambiguity here can lead to future litigation or an inability to utilize the AI’s product freely.

4. What is the explicit data retention and deletion policy upon contract termination?

Vendors must provide a documented, transparent process outlining how all your data—including training data, operational logs, and backups—will be completely and verifiably purged from their systems once the partnership ends. A simple promise to delete is not enough; you need an auditable process with a clear timeline.

Part II: Model Performance and Technical Validation

A model is only as good as its performance in your specific environment. These questions move beyond general claims to focus on technical reality and reliability.

5. What is your methodology for detecting and mitigating algorithmic bias, and at what frequency are these tests performed?

Bias is inherent in training data and can lead to unfair or costly decisions (e.g., biased hiring or loan algorithms). The vendor must detail their audit process, including the specific demographic or operational fairness metrics they track. The answer should include evidence of external audits or ongoing adversarial testing to prove a commitment to fairness beyond simple internal checks.

6. Can you provide the validation metrics (precision, recall, F1 score) specifically against a dataset relevant to our domain?

Generic performance metrics (e.g., “99% accuracy”) are meaningless. Accuracy in one domain rarely translates to another. Demand metrics calculated on a test set that mirrors your specific operational data, highlighting the system’s performance on both common and rare edge cases relevant to your business needs.

7. What monitoring mechanisms are in place to detect model drift and how quickly is your team alerted?

All AI models degrade over time as real-world data changes—this is called “model drift.” If left unaddressed, model predictions can become unreliable, leading to business harm. Ask for their specific drift monitoring solution (e.g., statistical change detection) and the service-level agreement (SLA) for automated alerts and subsequent human intervention or retraining.

8. Is the model explainable, and if so, how is the “Reason for Decision” (RFD) logged and auditable?

In many regulated industries, you cannot deploy an AI without understanding why it made a specific decision. This is explainable AI (XAI). Ask for the specific XAI technique used (e.g., SHAP or LIME) and verify that the output provides a human-readable, auditable record that can stand up to regulatory scrutiny.

Part III: Compliance, Governance, and Accountability

Responsible AI requires formal policy adherence. These questions ensure the vendor operates within established ethical and legal structures.

9. Which formal AI risk management or governance framework (e.g., NIST AI RMF, ISO 42001) has your solution adopted?

Certification or adherence to a recognized, independent standard (like the NIST AI Risk Management Framework) signals maturity and a commitment to responsible deployment. If they haven’t adopted a framework, they are building their governance from scratch—a significant risk to you. This is a critical factor for establishing their Expertise and Trustworthiness.

10. How is your solution’s security and compliance posture independently audited and validated?

Internal security checks are necessary but insufficient. Look for third-party compliance reports like SOC 2 Type II or an independent penetration test report performed within the last 12 months. External validation provides proof that their security controls meet industry benchmarks.

11. Can you detail your plan for managing legislative and regulatory changes, particularly in the US and EU?

AI regulation is rapidly evolving (e.g., the EU AI Act). A professional vendor must have a formal, documented strategy for monitoring, assessing the impact of, and rapidly adapting their solution to new legal requirements without disrupting your operations or imposing unexpected costs.

12. In the event of model failure, data breach, or incorrect action, what specific liability protection is written into the contract?

Traditional software liability is often capped or limited. Given the potential for catastrophic error in AI, your contract must contain clear, adequate provisions for indemnity and financial liability. If the vendor attempts to offload all risk onto the client, it is a significant red flag regarding their confidence in their product.

Part IV: Implementation and Future-Proofing

The deployment process and long-term costs define the true Total Cost of Ownership (TCO) of the AI solution.

13. What are the expected hardware, cloud resource, or API usage costs that are not included in the base subscription price?

The purchase price is often only the beginning. Many vendors fail to transparently communicate the required cloud compute resources (GPU usage), database storage requirements, or the cost structure for external API calls the solution makes. Require a detailed, itemized projection of all potential variable costs for a 24-month period based on your projected usage.

14. What is the required expertise level (and training plan) for our internal team to successfully manage and maintain the model long-term?

A vendor may promise an easy solution, but the long-term success of any AI depends on your staff. Ask for specifics on the necessary internal roles (e.g., a data scientist for monitoring, an ML engineer for retraining) and the curriculum they provide to ensure your team has the capabilities to manage model performance and address minor issues without immediate vendor reliance.

15. What is the documented decommissioning plan for the AI solution and our data at the end of its lifecycle?

Technology inevitably reaches obsolescence. You must plan for the exit. A comprehensive decommissioning plan outlines how the vendor will assist you in migrating your accumulated knowledge base, how they will verify the secure deletion of all data remnants, and what procedures are in place to ensure a seamless transition to a future technology without vendor lock-in.

Moving Forward with Confidence

Vetting an AI vendor is fundamentally about interrogating a complex system of code, data, and human policy. These 15 questions provide a foundational framework, compelling your potential partners to back their claims with documented procedures and auditable evidence. In a rapidly changing technological landscape, comprehensive due diligence is the only way to safeguard your assets and ensure the AI you purchase remains a competitive tool, not a costly liability. Partnering with a specialist agency to conduct this forensic review can close the knowledge gap and secure your investment.