The Comm100 AI Live Chat Benchmark Report 2026

The 2026 Live Chat Benchmark Report from Comm100 is here! Since 2016, Comm100 has been publishing its annual benchmark report. This year’s report focuses on the increasing impact of AI on customer support processes and uncovers some interesting findings.

This year’s report analyzes over 220 million live chat interactions across 18 industries to reveal the key customer service benchmarks that separate high-performing teams from the rest.

It also introduces new metrics for measuring AI impact, including chatbot resolution rates and bot-to-agent handoff satisfaction.

Featured Statistics

What You’ll Learn

• Agent efficiency trends: Why agent workloads dropped 5.8% while chat duration stayed flat, and what it signals about the changing nature of customer queries.
• Wait time and response time benchmarks: How large teams cut wait times by 37.5% while small teams saw increases, and the AI investments driving the gap.
• AI automation metrics: The percentage of chats now handled by AI Agents (75.3%), resolution rates by team size, and why chatbot satisfaction jumped 9.1%.
• Industry-specific data: Benchmarks for iGaming, higher education, banking, healthcare, government, and 13 other sectors, covering agent workload, chat duration, CSAT, and mobile adoption.
• CSAT analysis: Why customer satisfaction held steady at 4.1 out of 5 despite major operational changes, and which industries lead or lag.

Why It Matters

Customer support benchmarks exist for a reason: they tell you where you stand and where to focus. But benchmarks from last year miss the impact of AI adoption, and generic averages hide the variation across industries that makes direct comparison meaningful.

Support leaders need clarity on two questions. First, how does my team’s performance compare to similar organizations? Second, what operational changes are driving improvement for teams that outperform the average?

This report answers both. It provides the benchmarks to measure your current state and the trend data to understand what’s working for teams that have invested in AI-powered automation. The findings show that efficiency gains and customer satisfaction aren’t trade-offs when AI is deployed strategically.

Whether you’re building a business case for AI investment, setting performance targets for the year ahead, or trying to understand why competitors seem to do more with less, this data provides the foundation.