Data policy: who actually controls the data
The very first layer of evaluating an AI vendor is data policy. In practice, every interaction with an AI system is also a potential input for further processing, logging, or even model training.
The key question is not only whether the vendor “protects data”, but whether it clearly defines what happens to the data after processing. In enterprise environments, it must be unambiguous whether data is used for model training, how long it is retained, and whether there is a real possibility of complete deletion. It is equally important to understand data geography, as it often determines the legal regime of its protection.
If these rules are not explicit and auditable, the organization effectively loses control over its own data assets.
Auditability: black box or explainable system
The second critical area is auditability. In traditional software systems, it is possible to reconstruct what logic led to a specific result. In AI systems, this no longer applies automatically, because the output is the result of a probabilistic model, not a fixed logic.
For enterprise deployment, it is therefore critical that the vendor can provide at least a basic level of traceability. This means the ability to see inputs, model version, context used, and ideally also intermediate processing steps.
Without this capability, the AI system becomes a black box, which significantly complicates internal audits, compliance, and incident handling.
Model and vendor flexibility: hidden lock-in
One of the most underestimated risks of AI solutions is vendor lock-in, which is much stronger than in traditional SaaS. The reason is that the value of an AI system is not only in the application itself, but in the combination of model, prompts, data layer, and workflow logic.
In practice, this means that even if the system is technically replaceable, migration is extremely difficult. Prompt engineering is not standardized, workflows are often tied to a specific implementation, and embeddings or memory layers are often incompatible.
Therefore, it is important to verify early whether the system can work with multiple models and whether it allows export of configuration and logic. If not, it is more of a closed ecosystem than a flexible enterprise solution.
Onboarding and offboarding: the test most companies don’t do
With AI systems, companies often focus on onboarding, meaning how quickly the system can be deployed. However, a much more important question is what happens when the organization wants to stop using it.
Offboarding includes not only data export, but also the ability to rebuild workflows elsewhere, shut down the system without information loss, and ensure that no data remains locked inside the vendor system.
A strong risk signal is when the vendor cannot clearly describe or demonstrate what exiting the platform looks like. In practice, this often means the exit is either very expensive or technically complex.
Incident response: AI-specific failures
AI systems have types of incidents that do not exist in traditional software. These include situations where the model starts generating incorrect or dangerous outputs, where sensitive data is leaked through input prompts, or where the system is manipulated through specific attacks on language models.
Therefore, a general incident response plan is not enough. The vendor must have defined processes specifically for AI behavior, including monitoring inputs and outputs, the ability to quickly deactivate parts of the system, and mechanisms for retesting the model after an incident.
If these processes do not exist, the organization relies on a response that is not adapted to the nature of the risk.
SLA: the issue is not availability, but quality
Traditional SLA models based on uptime are insufficient for AI. API availability alone says nothing about whether the system provides consistent or correct outputs.
In the AI context, SLAs must therefore be extended with metrics such as latency, response stability, error rates, and the existence of fallback mechanisms in case of model degradation.
Without these parameters, the SLA becomes a formal document that does not reflect the real quality of the service.
Integration: where AI becomes value
The major difference between demo AI and production systems is the level of integration. Many solutions work well as standalone chatbots, but their value only appears when they are integrated into the organization’s existing processes.
Real enterprise value is created only when AI is not an isolated tool, but part of CRM, ERP, support, or operational systems.
Therefore, it is important to evaluate not only model quality but also the ability to integrate into real workflows and existing infrastructure.
Pricing: where complexity hides
AI pricing is often much less transparent than it appears at first glance. In addition to the base cost of using the model, there may be costs for tokens, storage, logging, fine-tuning, or scaling.
The problem is that these costs often only become significant under production load, not during pilot phases.
Therefore, it is crucial to simulate real usage at scale and understand how pricing changes with increased traffic or long-term usage.
Conclusion: AI vendor is a decision system, not software
The fundamental shift in thinking in AI procurement is understanding that you are not buying a tool, but a system that processes data, generates decisions, and can change over time.
Therefore, it is not enough to evaluate features or price. It is important to understand data control, auditability, system flexibility, and the ability of the organization to exit without losing value.
An AI vendor that cannot transparently explain these aspects represents a higher risk than added value.
