Average Handle Time, or AHT, measures the average amount of time spent handling a customer interaction from start to completion.

It is commonly used in customer support and contact centers to understand workload, staffing, and operational efficiency.

What Is Average Handle Time?

AHT usually includes the time spent:

Talking or messaging with the customer
Placing the interaction on hold
Researching information
Updating records
Completing after-contact work

The exact components depend on the channel and how the organization defines the metric.

Average Handle Time Formula

A common formula is:

AHT = (Talk or Interaction Time + Hold Time + After-Contact Work) / Number of Interactions

For messaging or digital support, the formula may be adapted to reflect active handling time rather than phone-specific talk time.

Example of AHT

Suppose a support team handles 100 interactions.

Across those interactions, the team spends:

600 minutes in customer conversations
100 minutes researching or waiting
200 minutes on follow-up work

The total handling time is 900 minutes.

AHT = 900 / 100 = 9 minutes

AHT vs. Time to Resolution

Time to Resolution measures the total elapsed time until the issue is resolved.

AHT measures the amount of active handling effort.

An issue may have low handling time but a long resolution time if the customer is waiting for another team or system.

AHT vs. First Response Time

First Response Time measures how long the customer waits for the first meaningful reply.

AHT measures how much handling work the interaction requires.

The two metrics measure different parts of the service process.

Why AHT Matters

AHT can help organizations understand:

Agent workload
Staffing needs
Process complexity
Repetitive manual work
Knowledge-access problems
Tool inefficiency

A sudden increase in AHT can indicate that support agents are spending more time searching, switching systems, or handling more complex cases.

Why Lower AHT Is Not Always Better

A lower AHT can indicate efficiency.

But optimizing too aggressively can create poor customer experiences.

Examples include:

Rushing conversations
Incomplete answers
Premature escalation
Closing unresolved cases
Avoiding complex issues

AHT should be balanced with quality metrics such as:

First Contact Resolution
Customer Satisfaction
Customer Effort
Repeat Contact Rate

How AI Can Affect AHT

AI can reduce repetitive handling time by helping with:

Information retrieval
Summarization
Classification
Suggested responses
Data extraction
Routing
Automated resolution

The biggest gains often come from reducing manual search and repetitive data collection.

AHT and Support Automation

Support Automation can reduce handling time by completing routine tasks before a human becomes involved.

For example, the system can:

Collect customer details
Identify intent
Retrieve relevant information
Summarize the conversation

The human agent starts with more context.

AHT and AI Support Agents

An AI Support Agent can fully resolve some routine interactions.

This can lower average human handling time because simple cases never enter the human queue.

For escalated cases, the AI can still reduce AHT by passing a summary and collected information.

AHT and Knowledge Access

Poor knowledge access increases handling time.

Agents may spend unnecessary time searching for:

Policies
Product information
Procedures
Account details

Improving search and knowledge retrieval can reduce this work.

AHT in Digital Channels

Measuring AHT in messaging is more complex than on phone calls.

Agents may handle several conversations at the same time.

Organizations should define whether AHT includes:

Total elapsed time
Active agent time
Response-writing time
After-contact work

The metric should be consistent with how the channel actually operates.

Improving Average Handle Time

Useful approaches include:

Better knowledge search
Automated information collection
Stronger routing
Fewer system switches
AI summaries
Better tools
Clearer workflows
Automated resolution for routine issues

The objective should be efficient handling, not simply shorter conversations.

Average Handle Time in AskHandle

AskHandle can reduce repetitive handling work by using AI agents to answer routine questions, collect information, search documents or data, use enabled capabilities, and route requests before a human needs to take over.

When a Human Handoff occurs, the workflow can preserve the conversation context so the receiving team has more information available.