[aallen]

One issue we ran into was explaining how AI-generated outputs were reviewed before being shown to customers. The product team assumed the process was obvious, while the compliance team wanted a formal control and audit trail.

 

[TamaraHicks]

I think this is becoming especially important for B2B products. Consumers might just try an AI feature, but enterprise buyers often have procurement, security, privacy, and legal teams looking at it from several different angles.

 

[mthompson219]

We had to answer questions about model providers, data location, retention, access permissions, and whether customer data could be used for training. None of those questions were surprising individually, but together they added considerable time to the deal.

 

[rharris137]

you are perfectly right..

In our case, the biggest problem wasn’t the AI itself. It was proving to the customer that we had the right controls around it.

as In our case, the biggest problem wasn’t the AI itself

 

[ehamilton478]

One thing I’ve learned is that “we don’t store the data” isn’t always enough of an answer. Customers often want to know what happens during processing, who has access, how long information exists, and what third parties are involved.

 

[Leslie Abbott]

I’ve actually had a positive experience with compliance reviews. A project was slowed down, but the review forced us to clean up our documentation and put better controls in place. It made the final product easier to sell to larger customers.

 

[lramirez751]

The biggest problem is usually not having a clear owner for AI governance. Engineering assumes legal is handling it, legal assumes security has checked it, and suddenly nobody has the complete picture.

 

[sgonzalez330]

I’ve seen teams underestimate how much evidence enterprise customers want. Saying that you have policies is one thing; being able to demonstrate those policies with documentation, logs, testing, and processes is another.

 

[jvelasquez474]

We had a situation where different teams had different assumptions about how customer data was handled by the AI system. The compliance review exposed that misunderstanding. It was uncomfortable, but it forced everyone to agree on the actual process.

 

[azimmerman]

One of my biggest lessons was that “the AI works” doesn’t mean “the product is ready.” We had to prove how it worked, what data it touched, who could access it, and how issues would be handled.

 

[NicholasMercado7]

We once had an AI feature that was technically ready but couldn’t be released to everyone at once. The solution was to roll it out gradually while we completed additional reviews. It wasn’t ideal, but it kept the project moving.

 

[bsawyer581]

A customer once asked us whether their information could be used for model improvement. We knew our intended setup, but we hadn’t documented it clearly enough. We ended up spending more time proving the answer than we expected.