AI Vendor Assessment 2026: Questions to Ask Providers

Procuring artificial intelligence solutions is not equivalent to buying traditional enterprise software. For a $10-100 million SMB, the decision to integrate AI carries unique risks and demands a thorough AI vendor assessment. This process identifies potential liabilities before they materialize. Understanding these distinctions is critical for chief operating officers and non-technical founders aiming for responsible AI adoption in 2026.

Why AI Vendor Assessment Matters

Traditional software operates deterministically. Its functions are predictable. AI, however, introduces probabilistic outcomes, often with opaque decision-making processes. This fundamental difference creates new avenues for operational, financial, and reputational exposure.

Consider the following points:

• Data Handling: AI systems consume vast quantities of data. Mismanagement of this data can lead to significant privacy breaches, regulatory penalties, and loss of customer trust. Traditional software typically processes known data types in defined ways. AI often ingests and transforms data in complex, evolving patterns.
• Algorithmic Bias: AI models can inherit and amplify biases present in their training data. This can result in unfair or discriminatory outcomes, leading to legal action, negative publicity, and ethical dilemmas. Identifying and mitigating these biases is a non-trivial task that requires vendor transparency.
• Model Drift: AI models degrade over time as real-world data deviates from their training data. This "drift" can silently erode performance, accuracy, and utility, impacting business operations without immediate detection. Unlike static software, AI requires continuous monitoring and retraining.
• Intellectual Property: The outputs generated by AI systems, especially generative AI, raise complex questions about intellectual property ownership. If your AI vendor trains its models on your proprietary data, clarifying ownership of the resulting IP is paramount.
• Regulatory Uncertainty: The regulatory landscape for AI is still forming. Emerging frameworks, such as the EU AI Act or the NIST AI Risk Management Framework, will impose new compliance burdens. Vendors must demonstrate an understanding of, and a strategy for, adhering to these evolving standards. Neglecting this exposes your organization to compliance failures.
• Integration Complexity: Integrating AI solutions can be more complex than traditional software due to specialized infrastructure requirements, data pipelines, and the need for continuous data feeds. Seamless integration is vital to avoid operational friction.
• Reputational Risk: An AI system that performs poorly, produces biased results, or experiences a security incident can severely damage your company's reputation. The probabilistic nature of AI outputs means a higher potential for unexpected, harmful results.

Before proceeding, a comprehensive AI Readiness Assessment can help identify internal capabilities and gaps, setting a realistic foundation for vendor selection. It also provides a baseline for understanding where external AI solutions will genuinely add value. It helps quantify potential shadow AI risks that might already exist within your organization due to unmanaged software usage.

5 Assessment Domains with Specific Questions

A structured inquiry is essential. The following 25 questions are organized into five domains to provide a comprehensive AI vendor assessment.

Data Security and Privacy

These questions aim to understand how your data is handled throughout its lifecycle.

1. Data Handling Policies: Describe your data handling policies for customer data, from ingestion to deletion. Specify encryption methods, both in transit and at rest.
2. Geographic Data Storage: Where is our data stored and processed geographically? Are there options for data residency requirements?
3. Data Anonymization: What data anonymization or de-identification techniques do you employ for training or operational data? Can we verify the effectiveness of these methods?
4. Access Controls: What data access controls are in place for your personnel? How do you manage privileged access, and is there a strict need-to-know policy?
5. Regulatory Compliance: How do you ensure compliance with relevant data protection regulations applicable to our industry and region (e.g., GDPR, CCPA, HIPAA, ISO 27001, SOC 2 Type II)?

Model Performance and Explainability

Understanding the AI's core functionality and its limitations is critical.

6. Performance Metrics: How do you measure and report model accuracy, precision, and recall for our specific use case? Can you provide benchmarks against industry standards or alternative solutions?
7. Bias Mitigation: What specific steps are taken to identify and mitigate model bias in your training data and algorithms? Describe your testing methodology for fairness.
8. Model Transparency: How transparent is your model's decision-making process? Can we understand why a specific output was generated, or is it a black box?
9. Model Drift Monitoring: What is your process for monitoring model drift and performance degradation over time? How often is retraining conducted, and what triggers it?
10. Known Limitations: What are the known limitations, edge cases, or failure modes of your AI model? Under what conditions might its performance degrade significantly?

Integration and Scalability

Practical deployment and future growth considerations are covered here.

11. API Documentation: Describe your API documentation, SDKs, and the general integration process. What level of technical support is provided during integration?
12. Infrastructure Requirements: What are the infrastructure requirements, both hardware and software, for integrating your solution into our existing environment?
13. Scalability: How does your solution scale with increased data volume, transaction load, or user demand? What are the performance implications at higher scales?
14. Uptime and Disaster Recovery: What is your typical uptime guarantee, and do you offer Service Level Agreements (SLAs)? Describe your disaster recovery and business continuity plans.
15. Future Version Support: What is your strategy for supporting future versions of your software and maintaining backward compatibility? How are updates deployed?

Legal and Compliance

These questions address the legal and ethical implications of AI deployment. Implementing a robust AI governance framework internally will simplify this review.

16. Intellectual Property Ownership: Who owns the intellectual property of the output generated by the AI system when using our proprietary data? Who owns the IP of models trained partially or entirely on our data?
17. Ethical AI Principles: What are your company's ethical AI principles and how are they implemented in your development and deployment processes? Do you have an internal ethics review board?
18. AI-Specific Regulations: How do you ensure your AI system complies with current and anticipated AI-specific regulations, such as those from the EU, US, or other relevant jurisdictions?
19. Indemnification: What indemnification do you offer regarding intellectual property infringement claims or data breaches directly attributable to your AI solution?
20. Audit Trails: What audit trails are available for model decisions, data usage, and access to the AI system? Can we access these logs for our own compliance needs?

Vendor Stability and Support

Assessing the long-term viability and reliability of the vendor is crucial.

21. Support Model: Describe your support model, including available channels, response times for different severity levels, and escalation paths. Is 24/7 support available?
22. Product Roadmap: What is your product roadmap for the next 12-24 months? How is customer feedback incorporated into product development?
23. Business Continuity: What is your business continuity plan in case of service disruption due to internal issues, external attacks, or natural disasters?
24. Financial Stability: How long have you been in business, and what is your financial stability outlook? Are you venture-backed, self-funded, or publicly traded?
25. Data and Model Portability: What is your policy regarding vendor lock-in? Can we easily extract our data and any custom-trained models in a usable format if we decide to terminate the contract?

Red Flags That Should Kill a Deal Immediately

Some responses, or lack thereof, indicate fundamental problems that should prompt an immediate halt to negotiations.

• Vague Security Answers: Any vendor unwilling or unable to provide concrete details on data security, encryption, access controls, or compliance certifications. Generic assurances without specifics are insufficient.
• No Data Ownership Clarity: If a vendor claims full ownership of all AI outputs generated using your data, or if they are unclear about data ownership when your proprietary information is used for training, this is a major issue.
• Lack of SLAs: Refusal to provide Service Level Agreements for uptime, performance, or support response times suggests a lack of commitment or confidence in their service.
• Evasive on Bias: A vendor who dismisses concerns about model bias or claims their AI is "objective" without explaining mitigation strategies. This indicates either ignorance or an unwillingness to address a known problem.
• No Disaster Recovery: The absence of a clear, tested disaster recovery plan means your operations will be at significant risk in the event of an outage.
• Unclear Financial Health: If a vendor cannot provide reasonable assurance of their financial stability, you risk investing in a solution that may cease to exist.
• "Magic" Claims: Any vendor presenting their AI as a black box that delivers results without explanation or transparency. This is an indicator of unmanaged risk.
• Pressure for Immediate Commitment: High-pressure sales tactics that discourage due diligence. A reputable vendor understands the need for thorough review.

What Good vs. Bad Answers Look Like

The quality of answers reveals more than just information; it exposes a vendor's maturity and commitment.

Example: Data Handling Policies

• Bad Answer: "We keep your data safe, we use industry best practices." (Vague, lacks specifics, relies on trust without proof.)
• Good Answer: "All customer data is encrypted using AES-256 both in transit (TLS 1.2+) and at rest (AWS S3 with KMS encryption). Access to production data is strictly controlled via role-based access control (RBAC), requires multi-factor authentication, and is logged for auditing. Our infrastructure is SOC 2 Type II certified, and we undergo annual third-party penetration testing." (Specific technologies, controls, certifications, auditability.)

Example: Model Bias Mitigation

• Bad Answer: "Our AI is objective; it just processes data." (Demonstrates a fundamental misunderstanding of AI limitations or an attempt to avoid the issue.)
• Good Answer: "We implement a continuous bias detection pipeline using fairness metrics such as demographic parity and equalized odds. Our training datasets are regularly audited for representation, and we employ techniques like re-sampling and adversarial de-biasing. Any detected biases are reviewed by our internal ethics committee, and remediation steps are prioritized in development sprints." (Specific methods, continuous process, ethical oversight.)

Example: Intellectual Property Ownership

• Bad Answer: "We retain all rights to any content generated by our AI system, as it's our proprietary technology." (This directly conflicts with your need to own outputs derived from your proprietary inputs.)
• Good Answer: "While we own the underlying AI models, you retain full intellectual property rights to all outputs generated by our AI when derived from your provided input data. We will never use your data to train models for other customers without explicit written consent." (Clear distinction, protection of customer IP, transparency on data use.)

Practical Next Steps

Once you have completed your AI vendor assessment, the work is not over.

1. Document Everything: Maintain a detailed record of all questions asked, answers received, and any due diligence conducted. This serves as a critical reference point and legal record.
2. Legal Review: Have your legal counsel review the vendor's terms of service, data processing agreements, and any SLAs. Ensure they align with your internal policies and regulatory obligations.
3. Pilot Projects: Before committing to a large-scale deployment, consider a pilot project. This allows you to evaluate the AI solution's performance in your specific environment, test integration points, and assess vendor responsiveness with minimal risk.
4. Internal Preparation: Ensure your internal teams are prepared for AI adoption. This includes data governance, change management, and training.

Engaging with experts can streamline this complex process. Our team offers specialized services to guide SMBs through AI strategy, vendor evaluation, and secure deployment.

Ready to demystify AI procurement and ensure your next AI investment is a sound one? Start with a comprehensive AI Readiness Assessment today.