Every call center manager knows the pressure: calls stacking up in the queue, agents rushing through conversations, and customers left feeling unheard. Average handle time sits at the center of this tension, a metric that can make or break your operation's efficiency and your team's morale. This article will show you how to reduce average handle time efficiently, call center optimization, so you can improve operational performance while maintaining high-quality customer service, without sacrificing the human connection that keeps customers coming back.
The solution isn't about pushing agents to talk faster or cut corners on service quality. Conversational AI tools can handle routine inquiries, verify customer information, and gather preliminary details before a human agent even picks up the call. This means your team spends less time on repetitive tasks and more energy solving complex problems that actually require their expertise, creating a win for both productivity metrics and customer satisfaction scores.
Summary
- Average handle time typically ranges from 6 to 8 minutes, according to industry data, but this benchmark obscures significant variation across service types. A tech support call resolving a software bug might legitimately require 15 minutes, while a simple account balance inquiry should take under three.
- When agents rush to hit time targets, they skip thorough problem diagnosis and provide surface-level answers that don't actually resolve underlying issues. This creates a recurring contact cycle in which customers call back days later, frustrated, and a different agent starts from scratch.
- Misrouted calls create a predictable cascade that doubles handle time regardless of agent performance. A customer lands in the wrong queue, gets transferred after 45 seconds of hold time, then spends another 90 seconds while the second agent rebuilds context. Skills-based routing and intent classification address this at the structural level by connecting customers with the right resource immediately, eliminating wasted time across multiple agents.
- Tool friction compounds into high hidden costs that most contact centers never account for. An agent who toggles between four applications per call, with each switch taking three seconds plus CRM lag and authentication timeouts, adds 90 extra seconds per interaction.
- First call resolution requires designing processes for completeness rather than speed, because incomplete resolution inflates total organizational handle time even when individual call durations look acceptable. Data shows that 1 in 3 customers will leave a brand they love after just one bad experience.
Conversational AI addresses this by handling routine verification, information gathering, and simple inquiries before human agents enter the conversation, removing structural obstacles such as manual data capture and misrouting that increase handle time regardless of how efficiently agents work.
What’s a Good Average Handling Time?
There isn't a universal "good" AHT because the right number depends entirely on your industry, customer expectations, and the complexity of issues you handle. According to Xima Software, typical AHT ranges from 6 to 8 minutes across most contact centers, but that benchmark is meaningless if your customers need 12 minutes to resolve technical support issues or if your sales team closes deals in 4 minutes.
The metric only matters when measured against resolution quality and customer satisfaction, not against arbitrary industry averages.
How AHT Actually Works
Average handle time isn't a single measurement. It's a composite metric built from three distinct components that together reveal how efficiently your team operates. Talk time measures the duration of an active conversation between the agent and the customer.
Hold time captures moments when customers wait while agents search for:
- Information
- Consult supervisors
- Navigate systems
After-call work includes documentation, ticket updates, follow-up scheduling, and any administrative tasks required to fully close the interaction.
Beyond the Stopwatch: Decoding the Three Pillars of AHT
The formula looks simple:
AHT = Total Talk Time + Total Hold Time + Total After-Call Work / Total Number of Interactions
If your team handles 150 calls with 3,000 minutes of talk time, 700 minutes on hold, and 500 minutes of after-call work, your AHT is 28 minutes. But that simplicity hides crucial operational insight.
When you calculate AHT without examining which component drives the number, you're flying blind. A 28-minute AHT caused by thorough after-call documentation tells a completely different story than 28 minutes driven by agents repeatedly placing customers on hold to find answers.
The Dangerous Assumption Most Teams Make
Most call centers assume that lower AHT is always better. Leadership sees the metric trending down and celebrates improved efficiency. Agents receive coaching to shorten calls. Performance reviews reward speed. The entire operation is optimized around a single number, chasing reduction as if it were the ultimate proof of operational excellence.
The Intersection of AHT and First Call Resolution (FCR)
In reality, chasing lower AHT without context often increases repeat calls, customer frustration, and total operational cost. When you pressure agents to end calls quickly, they rush through explanations, skip verification steps, and close tickets before fully resolving issues.
The customer hangs up confused or dissatisfied, waits a day to see if the problem was actually fixed, then calls back when it hasn't. Your AHT dropped by 45 seconds, but you've now handled two calls instead of one. The metric improved while performance declined.
The Segmentation Strategy: Setting Realistic AHT Targets
Industry benchmarks vary wildly depending on what you're actually doing.
- Technical support for enterprise software might require 15 minutes to properly diagnose issues, walk through solutions, and verify resolution.
- Retail order status inquiries may be resolved within 3 minutes.
- Financial services compliance calls could stretch to 20 minutes because regulations mandate specific disclosures and verification procedures.
Comparing your 12-minute AHT to a competitor's 7-minute average means nothing if you're solving fundamentally different problems for different customer segments.
The High Cost of Poor Documentation: Why AHT is a Long-Game Metric
The mechanism behind this dysfunction is straightforward. AHT measures efficiency, but efficiency without effectiveness is just speed. When agents rush to meet time targets, documentation suffers. They use vague notes, omit details about customer context, or fail to log critical information the next agent needs.
A customer calls back three days later about the same issue, and the new agent starts from scratch because nothing useful exists in the system. Hold time increases as agents search for information that should have been documented. After-call work extends as they try to piece together what actually happened. The entire operation becomes less efficient because you optimized for the wrong thing.
The Ghost in the Machine: How High Contact Propensity Destroys ROI
Here's what this looks like in practice. A team cuts AHT by enforcing 5-minute caps on all customer interactions. Agents learn to end calls quickly, even when customers still have questions. Repeat contacts are expected to rise by 18% over the next quarter. Total call volume increases because the same customers require multiple interactions to resolve a single issue.
Staffing costs are rising to accommodate increased volume. Customer satisfaction scores decline when customers feel rushed and unheard. The AHT metric improved from 7 minutes to 5 minutes, but operational performance declined across all key dimensions.
Root Cause Analysis (RCA): Turning Data into Actionable Efficiency
The goal isn't lower AHT. It's optimized AHT relative to resolution quality. That means understanding what drives your handle time, which components create friction, and where speed actually helps versus where it damages outcomes.
When agents spend 3 minutes on hold searching for information they should be able to access instantly, that's a problem worth solving. When they spend 8 minutes carefully explaining a complex solution that prevents future calls, that's time well invested.
The FCR-AHT Paradox: Decoding the Economics of Quality
Teams that optimize for resolution over speed often find that their AHT stabilizes at a higher level, while their repeat contact rate declines significantly. First call resolution improves. Customer satisfaction increases.
Total operational costs decrease because you handle fewer interactions per customer. The metric that looked worse actually signals better performance.
The Fragility of Efficiency: When Metrics Turn into Malpractice
But most teams never reach that insight because they're trapped in a cycle of measuring what's easy instead of what matters.
- They track AHT religiously while ignoring the context that makes the number meaningful.
- They celebrate reductions without checking whether customers are actually satisfied.
- They coach agents to work faster without first determining whether speed is the constraint limiting performance.
But what happens when the pressure to lower AHT starts breaking the very systems that should support your team?
Related Reading
- Call Center Optimization
- What Is a Warm Transfer in a Call Center
- How Do You Manage Inbound Calls?
- How Can You Verify the Authenticity of a Caller
- Call Center Authentication Best Practices
- Call Spike
- Inbound Call Handling
- Call Center Cost Reduction
- Call Center Security Best Practices
- Call Center Monitoring Best Practices
- Real-Time Monitoring in Call Center
- Intelligent Call Routing
- Inbound Call Center Sales Tips
- Inbound Call Routing
7 Root Causes That Actually Drive High Average Handle Time
High AHT rarely stems from slow agents. It comes from structural friction built into how contact centers route calls, organize information, and equip their teams. When systems force agents to search, transfer, or improvise, handle time climbs regardless of effort or urgency.
The difference between a 6-minute call and a 10-minute call usually stems from whether the right agent answered, whether they could find what they needed, and whether the tools worked as expected. Most organizations diagnose AHT as a performance problem. The real issue is architectural.
1. Misrouted Calls
Wrong queue placement creates a predictable cascade. A customer calls about billing but lands in technical support. The agent listens, realizes the error, and transfers. That transfer adds 45 seconds of hold time, plus an additional 90 seconds while the second agent rebuilds context.
The customer repeats their story. Total handle time doubles, not because anyone worked slowly, but because the initial routing logic failed.
The Arithmetic of Accuracy: How Routing Eliminates 'Dead Time'
Skills-based routing and intent classification solve this at the structural level. When systems accurately identify customer needs before connecting customers to an agent, transfer volume drops. According to Zendesk, 33% of customers say that efficiently answering questions is the most important aspect of a good customer experience.
That efficiency starts with routing to the right resource immediately, not after two failed attempts. The mechanism here isn't complicated. Every misrouted call burns time across multiple agents, inflates queue length, and frustrates customers who expected resolution on first contact. Fix routing, and a significant portion of the elevated AHT disappears without requiring anyone to work faster.
2. Knowledge and Script Gaps
Agents pause mid-call because they don't know where to find the answer. The knowledge base exists, buried somewhere across three different systems. Policy documentation is on a shared drive and was last updated two years ago. Script guidance covers common scenarios but falls silent when customers ask follow-up questions that deviate from the template.
This cognitive load breaks the call flow. The agent apologizes, places the customer on hold, searches frantically, possibly pings a supervisor on Slack, and then returns with an answer that may or may not be up to date. That 90-second pause happens thousands of times daily. It's not an agent problem. It's an information architecture failure.
Knowledge-Centered Service (KCS): Solving the Accuracy-Speed Paradox
When knowledge systems fragment, agents compensate by memorizing workarounds or guessing. Neither approach scales.
- Those who care deeply about accuracy take longer because they verify before responding.
- Those under time pressure respond faster but often incompletely, leading to repeat calls.
The system penalizes thoroughness and rewards speed, regardless of whether the answer actually resolves anything.
3. Tool and System Friction
- CRM lag
- Multiple tabs
- Manual copy-paste
- Authentication inefficiencies
These sound minor until you calculate the cumulative cost. An agent toggles between four applications per call. Each switch takes three seconds. The CRM loads slowly after every search query, adding another two seconds. Authentication times out mid-call, requiring re-login. Small frictions compound into 90 extra seconds per interaction.
Across 10,000 calls monthly, that's 250 hours of wasted handle time. Not because agents lack skill. Because the infrastructure fights them at every step.
The Systems Integration Gap: Measuring the 'Alt-Tab' Tax
Technical debt accumulates invisibly. Legacy systems weren't designed for the volume or complexity of modern contact centers. Integrations break. Data doesn't sync between platforms.
Agents develop elaborate workarounds, opening the same customer record in three different tools because none of them show complete information. The organization optimizes individual components without recognizing that friction between them undermines efficiency.
The Human-AI Synergy: From Displacement to Augmentation
Conversational AI addresses this by consolidating verification, information gathering, and routine inquiries before human agents enter the conversation. When an agent engages, they work with a pre-populated context and handle issues that require judgment, rather than navigating system limitations.
This architectural shift reduces handle time by removing structural obstacles rather than pressuring agents to move faster through broken workflows.
4. Lack of First-Call Resolution Design
If issues aren't fully resolved, customers call back. That repeat volume inflates total handle time across the organization, even if individual call durations look acceptable. A customer calls about a billing discrepancy. The agent corrects the charge but doesn't explain why it happened or confirm that the next statement will reflect the change. Three weeks later, the customer calls again because the issue recurred.
The second and third calls both trace back to an incomplete resolution in the first interaction. The mechanism is straightforward. When agents optimize for call duration rather than outcomes, they close tickets without closing loops. Documentation gets abbreviated. Follow-up steps get skipped. The customer leaves believing everything is handled, only to discover it isn't.
The Retention ROI: Moving from Throughput to Outcome
First-call resolution requires designing processes that ensure completeness, not just speed. That means giving agents time to verify fixes, explain root causes, and confirm understanding. It means building workflows that prompt for common follow-up actions rather than assuming agents will remember.
According to PwC, 1 in 3 customers will leave a brand they love after just one bad experience. Incomplete resolution doesn't just inflate AHT. It destroys retention.
5. Lack of Agent Training
Poorly trained agents struggle not because they lack effort, but because they lack the knowledge structure to diagnose problems quickly. They place customers on hold to request assistance. They take longer searching for information because they don't know where to look.
Complex issues that experienced agents resolve in eight minutes take new hires fifteen minutes, not because they work more slowly, but because they haven't internalized the patterns yet.
Bridging the Gap Between Classroom and Calls
This isn't a motivation problem. It's a knowledge transfer failure. Organizations onboard agents with two weeks of classroom training, then expect them to perform like veterans. The gap between training scenarios and real customer interactions creates constant friction.
Agents encounter:
- Edge cases they've never seen
- Policies they didn't know existed
- System behaviors that contradict what they were taught
Building Structural Knowledge: Moving Beyond Rote Memorization
The instinct is to blame the agent for not learning faster. The actual failure lies in designing training that builds structural knowledge rather than relying on on-the-job learning to compensate for gaps.
Comprehensive training reduces handle time by eliminating the pauses, holds, and escalations caused by uncertainty.
6. Complicated Call Center Processes
Agents manage excessive workflows on top of solving customer problems. They switch among multiple software programs, follow convoluted scripts that don't account for conversation flow, and complete extensive forms that capture data no one uses. This process bloat diverts attention from the actual work that matters: understanding the customer's needs and resolving them.
Imagine trying to have a meaningful conversation while simultaneously filling out a tax form and answering trivia questions. That's the cognitive load agents carry when processes pile complexity onto what should be straightforward interactions. The result is distraction, inefficiency, and inflated handling times because agents are doing three jobs simultaneously.
Lean Operations: Eliminating Waste in the Customer Journey
Simplifying processes requires examining which steps add value and which exist simply because no one questioned them.
- Does the agent really need to document 12 fields per call, or would 4 suffice?
- Does the script need to cover every possible branch, or could agents exercise judgment for common variations?
Process bloat accumulates over the years as different departments add requirements without considering the cumulative burden.
7. Outdated Contact Center Technology
Legacy systems constrain agent potential and limit operational visibility. Old technology doesn't integrate with modern tools. It lacks automation capabilities that could eliminate repetitive tasks. It provides managers with lagging indicators rather than real-time insights, enabling intervention before problems compound.
Agents using outdated technology can't reach their full potential because the infrastructure prevents them from doing so. They know faster ways to accomplish tasks, but the system doesn't support them. They see inefficiencies but lack the tools to address them. The technology becomes the ceiling, not the foundation.
Systems Thinking: Moving from Symptom Fixes to Structural Solutions
Modernizing infrastructure isn't about chasing the newest features. It's about removing constraints that prevent good agents from doing great work. When technology enables rather than restricts, handle time drops because agents spend less time fighting systems and more time solving problems.
When routing fails, knowledge fragments, and systems slow agents down, AHT rises regardless of how fast anyone speaks. The instinct is to push harder, set tighter targets, and pressure agents to move faster. But if the root causes are structural, speed pressure only increases errors and repeat volume. The solution isn't urgent.
Related Reading
- How to Improve First Call Resolution
- Inbound Call Analytics
- First Call Resolution Benefits
- Multi-turn Conversation
- How to De-Escalate a Customer Service Call
- How to Improve Call Center Agent Performance
- How to Handle Irate Callers
- Edge Case Testing
- Handling Difficult Calls
- How to Integrate VoIP Into CRM
- Best Inbound Call Tracking Software
- Call Center Voice Analytics
- How to Handle Escalated Calls
- Acceptable Latency for VoIP
- How to Reduce After-Call Work in a Call Center
- How to Automate Inbound Calls
- How to Set Up an Inbound Call Center
- Best Inbound Call Center Software
- GoToConnect Alternatives
- GoToConnect vs RingCentral
- Aircall vs CloudTalk
- CloudTalk Alternatives
- Inbound Call Center Metrics
- Contact Center Voice Quality Testing Methods
- Best After-Hours Call Service
How to Reduce Average Handle Time Without Increasing Repeat Calls
Reducing AHT without inflating repeat volume requires fixing the structural causes that extend handle time while simultaneously ensuring complete resolution. This means addressing tool friction, routing accuracy, knowledge accessibility, and process complexity to enable agents to work faster and more thoroughly. The tactics work because they remove obstacles rather than pressuring agents to cut corners.
Most teams approach AHT reduction backward. They set time targets, monitor compliance, and celebrate when average durations drop. As repeat call rates rise, customer satisfaction declines and total operational costs increase, even as the metric improves. The problem wasn't the goal. It was the method.
Use Efficient Tools And Processes
The right tool eliminates friction. The wrong one creates it at every interaction. A scrappy startup using enterprise-level help desk software designed for 10,000-agent operations doesn't gain capability. They inherit complexity that slows down every ticket. Features they'll never use clutter interfaces.
Workflows built to meet compliance requirements don't need to include unnecessary steps. According to Sycurio, average handling time (AHT) is a key metric in customer service operations, but optimizing it requires matching tools to actual operational needs rather than aspirational scale.
The Macro Advantage: Automating the Small Moments
Keyboard shortcuts and macros compress repetitive actions from fifteen seconds to two. An agent who types the same account verification message forty times daily wastes ten minutes on a task that should be instant.
Multiply that across a team of fifty agents, and you've burned over eight hours daily on avoidable manual work. Because tool efficiency directly impacts workflow speed, proper tool selection and training reduce friction, which lowers AHT without requiring agents to rush through interactions.
Experiential Learning: Why Simulation Trumps Theory
Training matters as much as tool choice. Agents who don't know their required software spend time discovering features mid-call or working around capabilities they don't realize exist.
Onboarding should include hands-on practice with every system agents will touch during live interactions. Not classroom demonstrations. Actual workflow simulations where they navigate the tools under realistic pressure.
Know The Product You're Supporting
Support agents need structural product knowledge, not just script familiarity. When agents understand how features connect, why errors occur, and what typical failure modes look like, they diagnose faster.
Effective ticket routing amplifies this by ensuring technical questions reach agents with relevant expertise. Because routing determines who answers the call, improving routing reduces transfer time, which lowers AHT without rushing agents.
Living Knowledge: Moving from Static Libraries to Dynamic Content Loops
Agile development creates a knowledge maintenance problem. Products change weekly. New functionality ships constantly. Features get deprecated. Agents trained three months ago are working with outdated mental models.
The customer describes behavior that matches last quarter's version, but the current release works differently. The agent doesn't know this, searches for solutions that no longer apply, and extends handle time trying to resolve issues that no longer exist.
The Feedback Loop: Turning Support Insights into Product Strategy
Regular lunch-and-learns and weekly demos close this gap. Product teams walk through recent changes. Support agents ask questions about edge cases they've encountered. The documentation team captures new troubleshooting patterns.
This continuous knowledge transfer prevents drift that occurs when support teams operate independently of product development. Because product knowledge eliminates research delays, staying current on features enables faster resolution, which reduces handle time by preventing knowledge gaps.
Make First Contact Resolution The Goal
Resolving tickets in the first response eliminates the cumulative handle time from follow-up cycles. An agent who takes eight minutes to deliver a complete answer prevents two additional four-minute calls.
Total organizational handle time drops even though the individual interaction ran longer. Because complete initial answers prevent follow-up cycles, first contact resolution eliminates additional exchanges, which dramatically reduces cumulative AHT.
Visual Evidence: Eliminating the ‘Description Gap’
Screenshots and animated GIFs remove ambiguity. Written instructions for visual tasks create confusion. "Click the settings icon in the upper right" means nothing when the customer sees three icons that could qualify.
A screenshot with an arrow eliminates interpretation. An animated GIF showing the exact click sequence prevents the back-and-forth clarification that extends conversations.
Contextual Intelligence: The End of the ‘Starting from Zero’ Loop
Proactive information collection changes the conversation structure. When contact forms capture account details, error messages, and the troubleshooting steps already attempted, agents start with context rather than spending three minutes gathering it. CRM integrations automatically pull purchase history, previous tickets, and account status.
The agent sees what the customer bought, when they last contacted support, and whether similar issues occurred before. This pre-populated context eliminates repetitive questions and enables agents to start solving immediately.
The Paradox of Choice: Why Curated Recommendations Beat Endless Options
Provide the single best answer when possible. Multiple options create decision paralysis. "You could try A, or maybe B would work, and some customers prefer C," sounds helpful but forces customers to evaluate trade-offs they're not equipped to assess.
They pick the wrong option; it doesn't work; they call back. A definitive recommendation tailored to their specific situation sets them on the right path immediately.
Look At Other Data
Understanding which cases have high versus low AHT reveals root causes. If elevated handle times cluster around specific agents, training gaps exist. If AHT is consistently high across the team, process, or system, issues are likely the culprit.
Because understanding patterns reveals root causes, data analysis enables targeted training or process fixes that address specific AHT drivers rather than applying blanket solutions.
The Tiered Support Architecture: Aligning Complexity with Competence
Highly technical tickets legitimately require longer resolution time. A customer with a complex integration issue spanning multiple systems won't be resolved in four minutes. Treating that extended handle time as a performance problem misdiagnoses the situation.
The real question becomes whether you have enough specialized agents to handle technical volume without creating queue backlogs. Sometimes the solution to high AHT is to hire more expertise, not pressure existing agents to work faster.
Information Architecture: The Hidden Physics of Search Speed
Internal knowledge that's difficult to navigate inflates handle time invisibly. An agent knows the answer exists somewhere in the documentation, but can't remember which folder, which document, or which section.
- They search while the customer waits.
- They ask colleagues in Slack.
- They eventually find it, but three minutes elapsed.
Multiply that across hundreds of daily interactions, and you've identified a systemic AHT driver that has nothing to do with agent capability.
Make Internal Knowledge Easily Accessible
No support agent relies solely on memory. The information volume is too large. Internal documentation becomes their external brain, but only if it's organized for rapid retrieval. When someone asks about the account cancellation protocol, agents should respond within 10 seconds, not 3 minutes.
Because accessible knowledge eliminates search time, organized internal libraries enable instant protocol retrieval, which speeds every interaction and lowers AHT.
Cognitive Offloading: Using Decision Trees to Simplify Complexity
Document common scenarios with decision trees, not paragraphs. An agent mid-call doesn't have time to read three pages on the nuances of the refund policy.
They need:
- "Customer within 30 days of purchase? Full refund.
- Between 30-60 days? Store credit. Beyond 60 days? No refund unless defective."
The structure matches how agents think under pressure.
Version control matters more than most teams realize. Outdated documentation is worse than no documentation because it leads to confident, incorrect answers. Agents follow procedures that changed two months ago, provide information that's no longer accurate, and extend handle time when the approach doesn't work. Assign ownership for keeping each document current. Set review cycles. Retire obsolete content instead of leaving it to confuse people.
Human-in-the-Loop (HITL): Augmenting Intelligence, Not Replacing It
Conversational AI handles routine verification and information gathering before human agents join the conversation. When agents engage, they work with a pre-populated context and handle issues that require judgment, rather than navigating system limitations.
This architectural shift reduces handle time by removing structural obstacles instead of pressuring agents to move faster through broken workflows.
Introduce AI Into The Support Process
High call volume creates a capacity problem that speed alone can't solve. If routine inquiries consume 60% of agent time, you're staffing for volume rather than complexity. Intelligent bots and AI agents can resolve simple requests before they reach humans.
- Password resets
- Account balance inquiries
- Order status checks
These don't require empathy or judgment. They require information retrieval and basic logic. Native AI-assisted voice provides human-centered responses that help customers feel heard during their requests. This isn't about replacing human connection. It's about reserving human attention for interactions that genuinely need it. When AI handles the routine work, agents focus on problems that require expertise, de-escalation, or creative problem-solving.
The Warm Handoff: Ensuring Consistency Across the Human-AI Divide
Chatbots direct customers to the right resources, escalate complex issues to live agents, and share relevant context during handoffs. The agent receives a summary of what the customer already tried, what information they provided, and what the bot determined they need. This eliminates the repetitive questioning that frustrates customers and wastes time.
Onboard And Continuously Train Agents
Adequate onboarding and ongoing training equip agents to meet customer expectations efficiently. New hires who receive two weeks of classroom instruction followed by immediate live call exposure struggle for months.
- They put customers on hold more frequently.
- They escalate issues they should handle.
- They take longer on every interaction because they're still building the mental models that experienced agents access instantly.
Because trained agents work more efficiently, reviewing recordings and scheduling performance reviews builds skills that progressively reduce AHT as agents improve.
The Science of Questioning: Precision over Politeness
Call listening identifies communication opportunities. An agent consistently takes eleven minutes on a call type that others resolve in seven. Reviewing recordings reveals that they ask open-ended questions during the resolution phase, inviting elaboration that extends the conversation.
Coaching them to use closed-ended questions during that phase brings their handle time in line with the team's performance.
Humanizing Metrics through Customer Voice
Performance reviews set new goals based on current capability. An agent who started at a 15-minute AHT and reduced it to 10 minutes over six months has demonstrated learning capacity. The next goal might be eight minutes, with specific focus areas identified through data analysis. This creates a development path rather than a static expectation.
Customer feedback provides direct insight into what works. When a customer leaves a positive review mentioning how quickly their issue was resolved, share that with the agent who assisted them. When feedback indicates confusion or multiple contacts needed, that's coaching material. The emotional connection to real customer outcomes drives behavior change more effectively than abstract metrics.
Provide Self-Service Options
Customer self-service reduces low-complexity ticket volume before it reaches agents. A comprehensive knowledge base and help center articles answer common questions without human involvement.
According to Zendesk, average handle time (AHT) can vary by industry and preferred communication channels, but self-service consistently reduces the number of interactions that require agent time.
Building the ‘Internal-External’ Knowledge Bridge
Self-service benefits agents by filtering out simple inquiries. They're not explaining password reset procedures for the fortieth time today. They're handling problems that require actual troubleshooting. This makes the work more engaging and reduces cognitive fatigue from repetitive explanations.
New agents benefit from self-service resources as they learn. When they encounter an unfamiliar issue, they can reference the same help articles customers use. This provides a learning scaffold that accelerates their development while ensuring they provide accurate information.
Offer Proactive Support
Proactive support prevents issues before they generate calls. When you know a system update will affect specific customers, contact them before they notice the issue. When a product shipment is delayed, notify customers immediately rather than waiting for them to call asking where their order is.
Because preventing issues reduces incoming requests, proactive outreach and customer success training eliminate problems, thereby decreasing complex interactions and lowering AHT.
From Reactive Fixes to Proactive Success
Customer success teams educate new buyers about products, troubleshooting resources, and where to find help. This upfront investment reduces the likelihood of support needs later. A customer who understands how to use advanced features doesn't call confused about why something isn't working.
Data analysis identifies patterns that enable anticipation. If 30% of customers who purchase Product X call within two weeks with the same configuration question, that's a proactive opportunity. Send setup guidance automatically after purchase. Include a video walkthrough. Prevent the call from happening.
Automate Tasks, Processes, And Communications
Skill-based routing rules direct calls to agents with relevant expertise. A billing question reaches someone who handles billing. A technical issue should be escalated to technical support. Because routing to appropriate agents prevents mismatches, well-designed IVR and routing systems increase first-time resolution, reducing transfers and lowering AHT.
Routing customers to agents with insufficient knowledge wastes time for both customers and agents. The customer explains their issue. The agent realizes they can't help. Transfer. The customer explains again. If the second agent also lacks the right expertise, the cycle repeats. Each iteration adds minutes to handling time and compounds frustration.
Deflection Logic: Designing IVR for Resolution, Not Frustration.
Interactive voice response systems can resolve simple requests without human involvement.
- Account balance? Automated.
- Payment due date? Automated.
- Order tracking? Automated.
These transactions don't benefit from human interaction. They just need accurate information delivery. Well-designed IVR handles this efficiently, freeing agents to focus on interactions that require judgment.
Employ Closed-Ended Questioning
Open questions unlock insights into complex problems. Closed questions move conversations forward during resolution. Once an agent understands the issue and knows the solution path, closed questions keep the customer task-focused.
- "Can you confirm the account number?"
- "Have you tried restarting the app?"
These yes/no questions prevent tangential conversation that extends handle time.
Closed questions help agents regain control when conversations drift. A customer begins explaining their entire history with the company, when the agent only needs to verify their identity. A closed question redirects: "Before we proceed, can you confirm the email address on the account?" This isn't rude. It's efficient guidance that benefits both parties.
Engineering the Conversation Flow
The skill is knowing when to switch from open to closed questioning. Early in the call, open questions gather context. During resolution, closed questions maintain momentum. Agents who use open-ended questions throughout the interaction unnecessarily extend handle time.
Those who jump to closed questions too early miss important context, leading to an incomplete resolution.
Use Signposting To Guide Customers
Signposting clearly outlines the next steps, allowing customers to prepare the required information in advance. "In a moment, I'll need your account number," gives the customer time to locate it while the agent completes a previous step.
This eliminates the awkward pause when an agent asks for information, and the customer scrambles to find it. Because advance notice allows customers to prepare, informing them of the information needed eliminates:
- Gathering delays
- Accelerates progress
- Reduces AHT
Reducing the Psychological Duration of Time
Signposting also manages expectations about hold time. "I'll place you on hold for approximately one minute while I check your account status" is better than silence followed by an unexpected two-minute wait. The customer knows what's happening and why. This reduces anxiety and prevents the perception that they've been forgotten.
Clear roadmapping creates a shared understanding of the conversation structure. "I'm going to verify your account, then check your order status, and finally process the refund," tells the customer exactly what will happen. They're not wondering what comes next or whether the agent forgot something.
Employ A Buddy Or Mentor System
New agents struggle more with AHT than experienced professionals because they lack time-efficient techniques. Pairing beginners with experts who have mastered efficient service accelerates skill transfer.
Because mentorship transfers time-efficient techniques from experts to beginners, buddy systems accelerate the learning curve, which brings new agents to optimal AHT performance faster.
Scaling Tacit Knowledge through Peer Mentorship
Buddy systems enable peer-to-peer learning without supervisor involvement. Agents listen to each other's calls, provide feedback, and share approaches that work. This creates a culture in which knowledge spreads efficiently organically rather than through formal training sessions.
Mentors benefit too. Teaching someone else forces you to articulate why you do things a certain way. This reflection often reveals inefficiencies in your own approach that you hadn't noticed. The mentor improves while helping the mentee develop.
Empower Agents To Act Without Approval Delays
Some actions agents can take to reduce AHT include empowerment so they don't waste time seeking permission to do the right thing for the customer. When agents must escalate every discretionary decision, resolution delays occur.
The customer waits while the agent messages a supervisor. The supervisor is busy with another issue. Ten minutes pass. By eliminating approval workflows, agents can resolve issues immediately, cutting wait time and reducing AHT per interaction.
Mapping the Spectrum of Autonomy
Empowerment requires clear boundaries. Agents need to know what decisions they can make independently and when escalation is truly necessary. A refund under $50? Approved. A refund of over $500? Escalate. This clarity enables confident action within defined parameters.
The alternative is agents who ask permission for everything because they're afraid of making mistakes. This creates a bottleneck at the supervisor level and trains agents to avoid responsibility. The long-term cost of this risk-averse culture far exceeds the occasional error that empowered agents might make.
Related Reading
- Nextiva Vs Ringcentral
- Dialpad Vs Nextiva
- Aircall Vs Talkdesk
- Aircall Alternative
- Twilio Alternative
- Aircall Vs Dialpad
- Five9 Alternatives
- Convoso Alternatives
- Dialpad Vs Ringcentral
- Aircall Vs Ringcentral
- Talkdesk Alternatives
- Nextiva Alternatives
- Dialpad Alternative
Stop Chasing AHT. Fix What’s Inflating It With Bland AI
High AHT usually starts before the agent even picks up.
- Poor routing sends callers to the wrong department.
- Manual data capture burns the first two minutes of every conversation.
- Verification steps that could happen automatically instead require agents to ask the same five questions hundreds of times daily.
- Unnecessary transfers compound the problem, adding hold time and forcing customers to repeat themselves.
By the time a human agent enters the conversation, the interaction is already behind.
The Yield Trap: Why High Speed Often Masks Low Quality
Most contact centers address this by pressuring agents to work faster. They set tighter time targets, monitor compliance more closely, and celebrate when average durations drop. But the structural problems remain.
Agents still waste time gathering information that should have been captured upfront. They still transfer calls because the routing logic failed. They still put customers on hold while searching for context that should have been pre-populated. Speed pressure doesn't fix broken intake processes. It creates rushed interactions that lead to repeat calls.
Elastic Infrastructure: Decoupling Service Capacity from Headcount
Bland replaces outdated IVR trees with conversational AI voice agents that handle the repetitive elements before escalation.
- Caller information gets captured during the initial interaction, not after an agent picks up.
- Intent-based routing connects customers to the right resource immediately based on their actual needs, not which button they pressed.
- Hold-time bottlenecks disappear because verification and account lookup happen automatically.
- During peak volume, the system scales instantly without adding headcount or creating queue backlogs.
Transitioning from Data Entry to Solution Engineering
When intake is automated and structured, agents spend less time diagnosing and more time resolving. They receive calls with context already established, account details already verified, and the customer's specific need already identified.
This eliminates the first three minutes of every interaction, which previously involved repetitive questions and navigating the system. The agent can focus on the problem that requires human judgment, not the administrative work that doesn't.
Book a demo to see how Bland reduces handle time at the system level.
