AI Warranties, Insurance, and the SLA Problem Nobody Talks About

AI contracts are strange.

They’re full of disclaimers, light on guarantees, and oddly quiet about what actually happens when something goes wrong. If you’re used to SaaS agreements, the contrast is jarring: fewer promises, weaker remedies, and almost no meaningful service levels.

That’s not an accident.

AI vendors aren’t being evasive because they’re sloppy. They’re doing it because AI risk is real, unpredictable, and still legally unsettled. Contracts don’t eliminate that risk — they quietly push it somewhere else.

Usually onto you.

Why AI contracts feel hollow compared to SaaS

Traditional SaaS contracts are built around uptime and performance. The software does what it does. If it’s down, there’s a credit. If it breaks IP, there’s an indemnity.

AI doesn’t work that way.

• Outputs vary

• Accuracy isn’t guaranteed

• Models change over time

• Behavior can drift

• Law is still catching up

So vendors respond by narrowing commitments instead of expanding them.

The result: contracts that sound professional but don’t actually protect outcomes.

The warranty gap: what vendors promise — and don’t

Most AI warranties fall into one of three buckets:

• “We have the right to provide the service”

• “We won’t intentionally violate the law”

• “We didn’t knowingly train on restricted data” (with carve-outs)

What’s missing is more important than what’s included.

You’ll almost never see warranties about:

• accuracy

• bias

• hallucinations

• regulatory compliance of outputs

• suitability for any particular use

And when vendors say “no training on your data,” it’s often paired with:

• broad exceptions

• vague definitions

• rights to use derivatives, metadata, or de-identified versions

So while the warranty sounds comforting, it often doesn’t map to how the model actually behaves.

Why SLAs are rare (or meaningless) in AI contracts

Service Level Agreements assume something measurable and controllable - the product will work a certain minimum amount of time that you have that software under contract. SaaS products work off of a formula. If the product doesnt work right, its probably the vendors fault because they created the formula.

AI breaks both assumptions. Is AI "working" if its hallucinating outputs?

Most AI vendors either:

• offer no SLA at all, or

• limit it to basic service availability

What they avoid SLAs for is:

• output quality

• response correctness

• downstream harm

And even when an SLA exists, the remedy is usually trivial — a credit that doesn’t come close to covering real-world damage.

This matters because founders often assume:

“If it fails badly and often, the contract will protect us.”

In AI, that assumption is usually wrong.

Indemnities don’t solve the real problem

AI indemnities are typically narrow and defensive.

Common patterns:

• IP indemnity only (and often limited to training data claims)

• No coverage for regulatory violations (like data breaches)

• No coverage for misuse — broadly defined

• Exclusions if you rely on outputs “without human review”

That last one quietly wipes out protection for the very way most businesses actually use AI.

And there’s a bigger issue: future law.

Many AI risks don’t exist yet in enforceable form. Contracts signed today can’t meaningfully indemnify against regulations that haven’t been written — and vendors know it.

Why insurance matters more than contract language

Here’s the uncomfortable truth: In AI, insurance often matters more than promises.

Contracts allocate risk. Insurance absorbs it.

Founders should care less about elegant warranty language and more about:

• whether the vendor carries meaningful coverage

• what that coverage actually applies to

• whether your own policies respond to AI-related incidents

If a vendor can’t stand behind outcomes financially, you’re the backstop — regardless of what the contract says.

How sophisticated buyers actually think about AI risk

The most realistic approach looks like this:

• Accept that AI outcomes are uncertain

• Use contracts to cap exposure, not eliminate risk

• Align legal terms with how the tool is actually used

• Decide consciously which failures you can absorb

This is less comforting than traditional SaaS contracting — but far more honest.

The goal isn’t zero risk. It’s bounded risk.

The practical takeaway

If an AI vendor:

• won’t warrant outputs

• won’t commit to meaningful SLAs

• won’t carry insurance

• won’t indemnify real exposure

then you need to assume you own the downside.

That doesn’t mean you shouldn’t use the tool. It means you should plan for failure instead of pretending it won’t happen.

AI contracts don’t need to be perfect — but they do need to be honest.

Hope is not a risk strategy.