How it works
AHT is calculated as:
AHT = (Total Talk Time + Total Hold Time + Total After-Call Work) / Number of Interactions
For different channels:
- Phone: Talk time + hold time + after-call notes
- Live chat: Active conversation time + typing delays + wrap-up (SuperOffice measured a 6-minute, 50-second average)
- AI chatbot: Time from first message to resolution
- Email: Time spent reading, researching, and composing a response
AHT is a double-edged metric. Lower AHT means more efficiency, but pushing AHT too low can sacrifice resolution quality, agents may rush through conversations and fail to fully resolve issues, leading to repeat contacts.
Why it matters
AHT directly impacts staffing costs and customer wait times. A 1-minute reduction in AHT across 10,000 monthly conversations frees up 167 agent-hours per month. AI chatbots dramatically lower AHT by resolving simple issues in seconds rather than minutes, and by pre-gathering customer context before human agent involvement.
How Chatsy uses it
Chatsy keeps the preceding conversation available when a chat moves to a human agent. Measure AHT before and after rollout to see whether that context saves time for your team.
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What else should I know about Average Handle Time (AHT)?
In an IBM study, virtual agents reduced average agent handle time by 4 minutes
Key takeawaysTest this in your own agent
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Example scenarios
- 01
AI chatbot vs human AHT comparison
A support team measures AHT across channels: AI chatbot resolves "What are your hours?" in 12 seconds, while the same question takes a human agent 3 minutes (greeting, lookup, response, closure). For FAQ-type questions, AI AHT is 90% lower than human AHT.
- 02
Context-assisted AHT reduction
Before AI, agents spent the first 2 minutes of each conversation gathering customer details and understanding the issue. With Chatsy, the AI collects this information during the initial conversation, so when the agent takes over, they immediately start resolving, reducing AHT from 10 minutes to 6 minutes.
- 03
AHT optimization without quality sacrifice
A team discovers their AHT is high because agents spend 3 minutes per ticket writing wrap-up notes. They implement AI-generated conversation summaries that automatically fill in ticket details, reducing after-call work from 3 minutes to 30 seconds without sacrificing documentation quality.
Key takeaways
- AHT measures the total time to handle a customer interaction: conversation + hold + wrap-up
- In an IBM study, virtual agents reduced average agent handle time by 4 minutes
- A 1-minute AHT reduction across 10,000 conversations saves 167 agent-hours per month
- Measure AHT before and after adding AI context to escalated conversations instead of assuming a fixed improvement
- Balance AHT reduction with quality, rushing agents leads to repeat contacts that increase total cost