SLAs and AI: Why “Uptime” Isn’t Trust Anymore

If you’ve ever signed a software contract, you’ve seen it: the Service Level Agreement, or SLA. It’s the “in writing” promise that your SaaS provider will keep the lights on, respond to support requests quickly, and deliver a certain level of service. For years, that was enough.

But as more SaaS products go all-in on AI, a new reality is setting in. The biggest risks aren’t downtime—they’re trust failures: AI showing private data, claiming to fix issues that aren’t solved, or behaving unpredictably from one day to the next. The SLA as we know it isn’t built for these problems, but buyers are starting to demand more.

What’s an SLA, and Why Should You Care?

An SLA is a formal agreement between a SaaS provider and a customer that spells out what good service looks like—how much uptime, how fast tickets get answered, how quickly bugs are fixed, and what kind of quality or accuracy you can expect. If the provider doesn’t deliver, the SLA usually says what the customer gets in return, like a service credit or refund.

Historically, SLAs have been about availability (“the app is up”), response times (“we’ll reply to you in 4 hours”), and issue resolution. They’re a way to set clear expectations, build trust, and avoid surprises.

The New Risks: When AI Breaks the Promise

But here’s the shift: With AI everywhere, the classic SLA is running into real-world problems it never imagined.

Recent public incidents highlight this:

Asana’s AI accidentally showed data between organizations because of a connector flaw. The service was “up,” but customer trust took a hit.
Intercom’s Fin marked tickets as “resolved” just because the user stopped replying, not because the user agreed it was fixed. That led to confusion and pushback.
Confluence’s AI sometimes answered from restricted pages or worked in some places but not others, leaving users unsure if they could trust the feature—even as the main platform ran normally.

It’s worth noting these gaps aren’t entirely new. Even before AI, SaaS buyers faced risks that classic SLAs didn’t capture—like a system being technically “up” while still leaking private data, scale issues where performance breaks for heavy users, or quality bugs that quietly disrupt production workflows. AI simply raises the stakes, making these trust failures more visible and potentially more consequential.

None of these failures were about downtime. They were about the AI system acting outside the rules, leading to unexpected exposure, frustration, or perceived unfairness.

What Today’s SaaS Buyers Want from SLAs

Modern SaaS buyers aren’t just worried about outages anymore. Here’s what they should be looking for in a truly “AI-ready” SLA:

Permission controls: AI must only answer with data the user is allowed to see—no leaks, ever.
Real solutions, not silent wins: Only count issues as solved when the user confirms it, not just because they gave up or went silent.
Safe rollout of new features: Any new AI connector or feature must be tested for trust and security, with clear rollback plans if something goes wrong.
Transparency: For big decisions, users want to see where AI’s answers come from and how confident the system is—not just a black box reply.
Consistency: Features should behave predictably and clearly signal when they’re unavailable or limited.
Clear data use policies: Users need to know what data trains the AI, and what stays private.

What’s the Path Forward?

The good news: These issues aren’t unsolvable. New best practices—including real-world testing across user roles, tracking real user confirmations, auditing new AI features before launch, and mapping data flows—are becoming the new standard.

A growing number of SaaS companies are starting to review their SLAs, add “trust metrics,” and seek independent audits to verify their claims. An AI trust audit simply means putting your system through these checks—permission, transparency, consistency—before the customer demands it.

Bottom Line

SLAs aren’t just about uptime anymore. They’re about keeping trust, not just keeping the servers online. With AI reshaping what’s possible (and what can go wrong), it’s time for SaaS leaders to make sure their promises match customer expectations.

Is your SLA ready for AI? If you’ve seen these new trust gaps in action, you’re not alone—and you’re not stuck. The tools and frameworks are here.

Recommended Reading

Is your SLA ready for AI? Have you seen these new trust gaps firsthand—or lived through an “AI breach” that wasn’t a classic outage? I’d love to hear your story or questions.
Reach out to me at Allexe.Law@ArtScienceGroup.com —let’s compare notes on what’s working, what’s breaking, and how leaders like you are rebuilding trust for the AI era.