Reducing first response time requires fixing queue depth and routing, not just agent speed. AI resolution of the top 20% of ticket types removes them from the FRT queue entirely, typically dropping average FRT by 40-60%.
Reducing first response time (FRT) in customer service requires addressing the bottlenecks before and during ticket handling - not just speeding up individual agent responses. In 2026, the most effective FRT reductions come from AI agents that eliminate queuing for eligible ticket types, intelligent triage that routes contacts to the right handler immediately, and async workflows that acknowledge contacts within seconds. Teams that focus only on canned responses and macros improve FRT marginally; teams that fix routing and deploy resolution-focused AI see FRT drop by 60-80% for eligible ticket categories. Lorikeet's data shows that AI resolution of the top 20% of ticket types by volume removes those contacts from the FRT queue entirely.
82% of customers expect a response within 10 minutes for live chat; email expectations are typically 4 hours, per Zendesk benchmarking data.
The average email first response time is approximately 12 hours, per SuperOffice research across 1,000 companies - far above the 4-hour window 46% of customers expect.
AI agents that resolve eligible ticket types have a near-zero FRT for those categories, since they respond instantly with no queue dependency.
Intelligent routing that eliminates misrouting reduces FRT by removing the queue re-entry that mis-triaged tickets require.
First response time is often treated as an agent performance metric - something to improve through faster typing, better macros, and smaller queues. In reality, FRT is mostly a queue and routing problem. The time a ticket spends waiting for the first response is almost always longer than the time an agent takes to write it. Fixing the wait is more impactful than fixing the write.
This guide covers where FRT time actually gets lost and the strategies with the highest leverage for reducing it.
What Is First Response Time and Why Does It Matter?
First response time is the time between a customer submitting a support request and receiving an initial substantive reply from a support agent or AI. It's one of the most visible customer service metrics because customers experience it directly. Long FRT signals to customers that their issue isn't a priority - and in channels like live chat where expectations are sub-2-minutes, any significant wait actively damages customer trust.
Research from SuperOffice found that the average first response time for customer service emails is 12 hours - with only 36% of companies responding within the 4-hour window that 46% of customers expect. That gap represents a significant and addressable satisfaction problem for most support teams.
Where Does FRT Time Actually Get Lost?
Understanding where FRT delays occur is the first step to fixing them. Most FRT problems are not agent speed problems - they're queue, routing, and prioritisation problems.
Queue wait time
The majority of FRT in email and ticket-based support is queue wait time - the period between ticket creation and an agent opening the ticket. In high-volume environments during peak periods, queue wait can be 4-8 hours for non-urgent tickets. Reducing queue depth through AI resolution of eligible tickets is the highest-leverage FRT improvement available: when AI handles the top 20-30% of tickets by volume, queue depth drops significantly, and the remaining human-handled tickets wait less.
Misrouting and re-queuing
Tickets that arrive in the wrong queue, or are triaged to the wrong team, must be re-queued before getting a first response. A billing ticket that lands in the technical support queue can wait through the initial queue, be identified as misrouted, and then wait again in the billing queue. Each re-queue adds the full wait time again. Intelligent initial triage that routes tickets correctly on the first attempt eliminates this compounding delay.
After-hours gaps
Contacts submitted outside business hours accumulate in the queue until agents return. For B2C businesses with global customer bases, after-hours contacts can represent 30-50% of total volume. AI agents that resolve eligible ticket types 24/7 eliminate FRT for those categories regardless of when the contact arrives. For non-AI-eligible contacts, async workflows that send accurate acknowledgments with specific resolution timelines improve customer experience even when FRT on human resolution is unchanged.
What Are the Highest-Impact Strategies to Reduce First Response Time?
The strategies below are ordered by leverage - start with the ones that address queue and routing before optimising individual agent speed, as the former has 5-10x the impact of the latter.
Deploy AI agents for the highest-volume, best-defined ticket types. AI that resolves tickets end-to-end has an effective FRT of seconds - the system responds instantly when a ticket is submitted. For the ticket types AI handles, FRT becomes a non-issue. Focus AI deployment on the 20-30% of ticket types that generate 60%+ of volume, and FRT for those categories drops to near-zero immediately.
Fix triage and routing before optimising agent speed. A ticket in the right queue with the right agent context gets a first response faster than a ticket that gets re-queued twice. Implement routing logic that classifies ticket type and intent accurately at submission, and monitors misrouting rate (tickets re-assigned after initial routing) as a health metric.
Set up immediate automated acknowledgment for non-AI tickets. For tickets that will wait in a human queue, an immediate acknowledgment with an accurate expected response window sets expectations and reduces anxiety-driven follow-up contacts. Automated acknowledgments do not reduce FRT by definition - but they improve customer experience during the wait and reduce the repeat contacts that FRT delays generate.
Segment your queue by SLA priority, not arrival order. Treating every ticket with the same queue priority means high-value customers and urgent issues wait behind low-complexity routine queries. Priority segmentation based on customer tier, issue type, and urgency signals ensures your FRT improvement effort goes where impact is highest.
Reduce handle time through better knowledge access. Agent-side improvements - faster knowledge base search, better canned response libraries, and contextual CRM data surfaced automatically - reduce the time agents spend researching responses. This doesn't reduce queue wait time, but it does reduce the time from "agent opens ticket" to "first response sent," which matters particularly for synchronous channels like live chat.
What FRT Improvements Should You Expect?
FRT improvement depends heavily on which approach you take. Queue and routing fixes produce the largest absolute improvements; agent-side optimisation produces smaller but still meaningful gains.
AI resolution of the top 20% of ticket types by volume reduces those tickets' FRT to near-zero and reduces overall queue depth - improving FRT for the remaining human-handled tickets as a secondary effect. Teams implementing this typically see overall average FRT drop by 40-60% within the first quarter after deployment. Fixing routing logic that eliminates re-queuing reduces FRT by 20-30% for misrouted ticket types - a significant improvement with relatively low implementation complexity. Priority queue segmentation reduces FRT for high-value and urgent tickets by 50-70%, at the cost of slightly increased FRT for low-priority contacts. For most businesses, this trade-off is strongly positive on customer satisfaction and retention impact.
Combining AI resolution, routing improvement, and priority segmentation typically moves average FRT from 7-10 hours to under 2 hours for email within a quarter - pushing most teams above the 4-hour threshold that 46% of customers consider acceptable.
Lorikeet's Take on First Response Time
At Lorikeet, we see FRT treated as an agent metric when it's almost always a queue metric. Teams run "speed training" and add macros, shave 30 minutes off handle time, and find FRT barely moves. The queue wait is 8 hours; agent response time is 4 minutes. The 4 minutes isn't the problem. Lorikeet's approach to FRT starts with queue analysis: which ticket types are clogging the queue, which are AI-eligible for instant resolution, and which routing decisions are causing re-queuing overhead. Fix the queue, and FRT improves for every agent and every ticket type at once. See how Lorikeet approaches queue design and triage for fast, high-quality first responses.
Key Takeaways
82% of customers expect responses within 10 minutes for live chat; industry average email FRT is 7-10 hours, well above the 4-hour expectation most customers hold.
AI resolution of the top 20% of ticket types by volume reduces FRT for those categories to near-zero and reduces overall queue depth for remaining tickets.
Misrouting and re-queuing is a compounding FRT problem - each re-queue adds a full additional queue wait, doubling or tripling FRT for affected tickets.
Combining AI resolution, routing improvement, and priority segmentation typically moves average email FRT from 7-10 hours to under 2 hours within a quarter.
Frequently Asked Questions
What is a good first response time for customer service?
Benchmarks vary by channel: under 2 minutes for live chat (40 seconds is considered strong by Zendesk), under 1 hour for social media, and under 4 hours for email. Most teams achieving strong CSAT scores target the 4-hour email benchmark as a minimum - teams aiming for premium support quality target under 2 hours. See detailed FRT benchmarks by channel and industry for specific targets.
Does faster first response time improve CSAT?
Yes, but the relationship is non-linear. Moving from 12-hour to 4-hour FRT has a significant CSAT impact. Moving from 2-hour to 1-hour FRT has a smaller impact. Beyond a certain threshold, first-contact resolution rate matters more than response speed - customers value resolution over speed when both are available, but abandon interactions entirely when speed is very poor. The highest-CSAT teams optimise for both: fast first response AND high FCR on that first response.
How does AI reduce first response time?
AI reduces FRT in 2 ways. First, AI agents that resolve eligible tickets respond instantly - zero queue wait - giving those ticket types an effective FRT of seconds. Second, by handling high-volume routine tickets, AI reduces overall queue depth, which reduces wait times for the human-handled tickets that remain. Both effects are immediate from the day AI deployment goes live on eligible ticket types.
First response time is a visible, measurable proxy for how much a support team values customer time. Long FRT signals capacity problems, prioritisation problems, or routing problems - and customers interpret all 3 the same way: their issue isn't urgent to you.
The fastest path to FRT improvement is almost always queue-level: reduce what goes into the queue (AI resolution), improve how tickets move through it (intelligent routing), and prioritise what comes out of it first (SLA segmentation). Agent-level improvements matter - but they produce incremental improvements on top of a structural fix, not a substitute for one.
If your FRT is above the channel benchmark or isn't improving despite agent-side optimisation, the problem is in your queue and routing design. See how Lorikeet approaches queue design for fast, high-quality first responses.
