Early findings from 2,500+ enterprise leaders
Find out why getting to production wasn’t the hardest part.
See what the rollback data actually says about AI at scale.
Understand where the cost of failure really lands.
Find out why investment alone isn’t closing the gap.
See who participated in the research and get the survey methodology.
For two years, the dominant story around enterprise AI has been about being stuck in the pilot phase. In customer communications, that story is no longer true. Sinch research (2026) shows 62% of enterprises already have AI agents in production. The debate about whether to deploy is over, but what happens next isn’t what the market expected.
Nearly three in four enterprises have been forced to shut down or roll back a deployed AI communications agent. And that number doesn’t decline with experience. Among organizations with fully mature guardrails, it’s higher. This is what production actually looks like.
When an AI communications agent fails, the impact splits three ways: the support queue, the brand perception, and the engineering team. Most organizations are tracking the first, but the other two are where the real cost accumulates.
Infrastructure satisfaction is the strongest predictor of AI deployment success across every variable analyzed, and yet for most enterprises, it’s the clearest gap in their current setup. Investment in AI communications is growing, but can the infrastructure underneath handle what’s coming?
2,527 enterprise decision-makers. 10 countries. 6 industries. We surveyed the leaders responsible for their organization’s AI communications strategy. Here’s how the research was conducted and what the methodology covers.
Here’s what 2,527 enterprise decision-makers reported, and what it means for organizations deploying AI in customer communications right now.
“The industry has assumed that better governance leads to better outcomes. But that’s not enough: If governance was the fix, the most mature teams would roll back less, not more. Our data points to a deeper issue. Engineering teams are spending most of their time building and maintaining safety systems, a lot of which their communications infrastructure should be providing, instead of focusing on improving the customer experience. That’s the guardrail tax that slows organizations down.”
The AI Production Paradox is a comprehensive look at the state of AI deployment in customer communications. The full findings of this research – including regional and vertical deep dives – will be available in June.
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