Short Summary:
Why Customers Care Which Model You Use

AI creates a different risk profile from traditional SaaS and software. Traditional systems generally operate through predetermined rules and produce predictable results. Generative AI is probabilistic, meaning it can produce different answers to the same question and generate information that appears credible but is wrong.

The concern is not simply that AI can make mistakes. AI is increasingly being used to perform tasks that previously required professional judgment, detailed review and human decision-making.

‍When users rely on the finished output without applying the same level of scrutiny, errors can be repeated quickly and at scale.

At the same time, vendors generally do not provide warranties or service levels guaranteeing that every AI output will be accurate, free from bias or below a defined hallucination rate.

From the customer’s perspective, this creates a gap between the risks it is being asked to accept and the contractual protections it receives.

That gap helps explain why customers want to know which foundational model powers the product. Many enterprise customers have already evaluated major model providers and created internal lists of approved and prohibited models based on their security, performance, bias and compliance concerns.

As a result, the underlying LLM can become a threshold procurement issue.

A customer may like the product and accept the price but still reject the vendor because the model powering the AI functionality has not been approved.