How To Improve Your Average Handle Time (Feat. Real Case Studies)

What Is Average Handle Time?

Average Handle Time (AHT) is a contact center performance metric that represents the average duration of a single customer interaction. This is broken down into three parts: talk time (the actual time spent talking to a customer), hold time (the time the customer spends on hold), and after-call work or ACW (the administrative tasks completed after the call ends, such as updating records, logging notes, or completing follow-up actions).

AHT is one of the most closely watched efficiency metrics in contact center operations. It directly affects staffing requirements, cost per contact, and service level achievement. Higher AHT means lower number of contacts an agent can handle per hour and consequently either customers must wait longer in queue to reach an agent, or additional agents must be employed, both of which increase the cost of operations.

However, AHT is also one of the most frequently misused metrics. When contact centers chase aggressive AHT targets without considering quality of resolution or customer satisfaction, agents start to rush conversations, bypass certain diagnostic steps or close a case before being completely certain. All of this leads to an increase in the number of repeat contacts and decreased FCR and CSAT levels.

How to Calculate Average Handle Time

AHT = (Total Talk Time + Total Hold Time + Total After-Call Work) ÷ Total Contacts Handled

For example, if an agent handles 50 calls in a day with a combined talk time of 200 minutes, hold time of 25 minutes, and after-call work of 25 minutes, the AHT is (200 + 25 + 25) ÷ 50 = 5 minutes.

Most contact center platforms calculate AHT automatically from call data, but it is worth auditing the inputs periodically. After-Call Work is usually underreported since some agents pass to the next call before writing any follow-up, or closing the ticket, which artificially lowers AHT and hides some downstream quality issues.

Industry Benchmarks for Average Handle Time

According to Talkdesk's AHT analysis, aiming for around 6 minutes per contact is a reasonable general benchmark, though this can go up or down depending on the nature of customer inquiries and the complexity of the service being offered. SQM Group's 2024 benchmarking data which covers all sectors including complex higher-AHT types, has calculated an average AHT(Talk + Wrap-up) of 697 seconds or approximately 11.6 minutes.

However, the average AHT will always vary depending on the industry and contact type. Industries like healthcare or insurance have a higher average handle time (8-10+ minutes) because they involve complex verification procedures and rigid regulatory demands. E-commerce and retail have a lower AHT (3-5 minutes) since they deal mostly with simple transactional inquiries.

Industry benchmarks are useful for orientation but should not be used as absolute targets. A company with a higher proportion of complex, technical or sensitive calls will always have a higher average handle time than one handling simple transactional inquiries. Forcing AHT down to the industry average may decrease customer satisfaction.

How to Improve Average Handle Time

Improve agent access to information

It's common to have an agent waste significant, if not the majority, of their contact time retrieving information that should already be readily available: previous interaction notes, call reason, customer history. When agents have to ask customers to repeat information or search multiple systems for context, handle time increases and customer satisfaction decreases simultaneously. Unified customer data and well-designed agent interfaces reduce AHT without any reduction in conversation quality.

Reduce after-call work

After-call work can sometimes be a "hidden" aspect of AHT. When agents manually log call outcomes, update CRM records, and write interaction summaries after every call, they spend significant time on administrative work that could be automated. AI-powered call summaries and automatic CRM updates can reduce ACW substantially, freeing agents to return to the queue faster.

Improve first-contact routing

When customers reach the wrong team, they have to be transferred. Every transfer adds time: the time to explain the transfer, the time the customer spends on hold, the time the receiving agent spends re-establishing context. Using well-defined IVR trees, AI-assisted intent detection, or pre-call qualification, eliminates this source of inflated AHT entirely. telli solves for this with contextualized warm trasfers.

Use AI to pre-qualify and pre-populate

One of the most effective ways to reduce AHT for human-handled contacts is to ensure that by the time a customer reaches a human agent, the basic information has already been gathered. telli's AI voice agents collect caller intent, verify identity, retrieve account details, and route to the right team before human pickup. This compresses the information-gathering phase from several minutes to near zero.

How telli's AI Voice Agents Help Reduce Average Handle Time

telli's AI voice agents contribute to AHT reduction in two ways: by handling certain contacts entirely (removing them from the human AHT calculation), and by compressing the information-gathering phase of contacts that do involve human agents.

Fully handled contacts by AI do not take AHT into account in any way, because they do not engage the human agents at all. These contacts are handled to completion by the AI agent, and the cost and time associated with them are decoupled from human capacity entirely.

For contacts that eventually reach human agents, the AI pre-qualification layer means agents receive callers with context already established. They know who is calling, why they are calling, and what the AI has already covered. This eliminates the opening information-gathering that typically consumes the first two to three minutes of many calls.

Customer Spotlight: Zenjob

Zenjob, the Berlin-based AI-powered staffing platform, was running a recruiting process for its long-term staffing product, Zenjob Pro, that had a classic AHT problem: not in a traditional contact center, but in a recruiting workflow with the same dynamics.

Each candidate screening required a ten-minute preliminary call to collect key information before a human recruiter could decide whether to advance to a full interview. As application volume grew, this became a serious bottleneck.

After deploying telli's AI voice agents to handle initial candidate screening calls, Zenjob automated 90% of information-gathering calls and cut the time spent advancing candidates by 75%. The AI agents call applicants, review position requirements, collect work history, and compile transcripts and AI-generated summaries. So, when a human recruiter reviews a candidate, the context is already there.

Critically, candidate satisfaction did not suffer. Zenjob surveyed applicants after AI-handled calls and found a steady CSAT score of 9 out of 10, demonstrating that AHT reduction through automation does not have to come at the expense of experience.