RingCentral Sees AI Adoption Strengthen Customer Economics

What happened

RingCentral has reported that revenue tied to its AI products, alongside revenue from services indirectly boosted by AI features, doubled year-on-year in the second quarter of 2026. The company also posted net revenue retention above 100%, meaning existing customers are on balance spending more over time rather than downgrading or churning.

Taken together, the figures suggest that enterprise customers are moving past pilot-stage AI experiments and embedding the vendor's AI-enabled communications and contact centre tools into daily operations, according to CX Today's coverage of the results.

Why it matters

Net revenue retention above 100% is one of the clearer signals available that a technology is delivering ongoing, felt value rather than novelty appeal — customers are expanding usage rather than merely renewing. For CX and service-design leaders evaluating AI tooling, this is a useful proof point: it indicates that AI features embedded into communications platforms are increasingly treated as operational infrastructure, not optional add-ons.

The result also speaks to a broader shift in enterprise buying behaviour. When AI-attached revenue grows faster than the base business, it implies that AI is shaping renewal and expansion decisions themselves — a signal that procurement and CX teams are starting to evaluate vendors on the strength of their AI roadmap as much as their core product.

By the numbers

• Doubled year-on-year growth in RingCentral's AI and AI-influenced revenue in Q2 2026.
• Above 100% net revenue retention, indicating existing customers expanded spend rather than reduced it.

The Renascence take

Headline AI adoption numbers are easy to overstate; the detail that should draw attention here is retention, not growth. Doubling a revenue line is achievable through aggressive bundling or one-off upsells — sustaining retention above 100% is much harder, because it requires customers to keep finding new reasons to use the feature after the initial sale.

What most operators will overlook is that this data point isn't really about AI capability — it's about habit formation. Customers don't expand usage of a feature because it's clever; they expand it because it quietly removes friction from a workflow they already rely on, to the point where switching it off would feel like a loss. The lesson for CX teams isn't "add AI features" — it's "instrument for embeddedness": track whether a feature becomes load-bearing in daily operations, not just whether it gets adopted at launch. Vendors and internal teams alike should be measuring dependency, not just usage.