After analyzing how call centers handle routing, coaching, and automation, I’ve come up with 13 practical ways to improve agent productivity. Discover how AI can help your support team do more without burning out.
13 ways you can improve agent productivity using AI
Most call centers face productivity issues because agents spend too much time on work that's not their core job. AI can help with these tasks, like searching for context, logging calls, routing requests, and more. You can improve your agents' productivity with these 13 strategies:
1. Offload admin and operational tasks to AI
Tasks like ticket tagging, email responses, meeting scheduling, and CRM updates follow predictable patterns. Instead of asking your reps to do them manually, AI can take care of them in the background.
An AI agent uses artificial intelligence to handle repeatable tasks, like writing call summaries, tagging tickets, updating the CRM, or drafting follow-up emails, based on clear rules you control.
That means reps spend less time on busywork and more time actually helping customers.
Example: After a support call, an AI agent can automatically log the summary, update the CRM, and draft a follow-up email.
2. Use smart routing
Smart routing uses AI to assign tickets based on skills, experience, intent, or current workload. It reduces transfers and shortens resolution time. When issues reach the right agent sooner, both productivity and customer outcomes improve.
Example: You can configure the AI routing agent to route technical issues to senior agents, while basic requests can go to newer reps who can handle them confidently.
3. Make knowledge accessible during conversations
Call center reps lose time when they search for answers across PDFs, internal docs, or chat threads. AI solves this by surfacing the right information inside the conversation. It reduces resolution time and helps new reps perform well.
Example: The rep can see the relevant policy automatically next to the ticket while chatting with a customer about refunds.
4. Add real-time coaching and feedback
AI-powered prompts guide agents during the conversation. Simple reminders help agents confirm details, adjust tone, or clarify next steps. This builds consistency across the team without constant manager involvement.
Example: During a call, an agent can see a prompt to recap the solution before ending the conversation.
5. Triage tickets with AI
AI sorts incoming tickets by urgency, sentiment, or intent before the agents see them. Instead of scanning queues manually, agents focus on what needs attention first. It helps teams address the high-risk issues first.
Example: AI can automatically move the frustrated customer message with negative sentiment to the top of the queue.
6. Automate follow-ups and call summaries
Follow-up emails and post-call notes are necessary, but they slow agents down. AI handles summarizing conversations, logging outcomes, and setting next actions in the CRM. AI keeps records clean without extra effort from agents.
Example: After a call ends, the AI can generate a concise summary and schedule a follow-up task.
7. Use omnichannel tools
When agents juggle chat, email, phone, and social across different tools, productivity drops. Omnichannel platforms unify conversations and preserve context across channels. AI customer support tools like Zendesk, Intercom, and Lindy have made significant progress in omnichannel support.
Example: An agent can easily switch from chat to email with full conversation history already visible.
8. Give agents context upfront
AI surfaces customer history, past tickets, recent orders, and sentiment before agents reply. With complete context, agents respond faster and avoid repetitive questions. It improves first-contact resolution and customer satisfaction (CSAT) score.
Example: An agent can see that a customer contacted support yesterday and can avoid asking the same questions again.
9. Monitor the right KPIs
AI helps teams track metrics beyond average handling time (AHT) and CSAT. Signals like escalation rate, time to action, and resolution speed reveal where workflows break down. Leaders coach agents based on real patterns, not guesses.
Example: AI can highlight a spike in escalations about a workflow, prompting a process fix instead of agent coaching.
10. Reduce overload with AI-based queue balancing
AI balances workloads across agents based on availability and capacity. It helps teams prevent burnout and have steady response times during busy hours.
Example: AI can distribute tickets evenly during peak times instead of assigning them to the fastest agents.
11. Simplify the agent experience
Clunky tools slow agents down. AI reduces friction by pre-filling fields, hiding unnecessary inputs, and guiding agents through workflows. You can speed up resolution and lower training overhead.
Example: An agent sees only the fields needed for the current ticket type, not the entire form.
12. Make onboarding more adaptive with AI
AI supports new agents with real-time nudges and guided workflows. Instead of memorizing playbooks, agents learn while doing the work. It shortens ramp time and reduces early burnout.
Example: AI can give step-by-step guidance to a new hire during their first refund request.
13. Offer real-time performance insights
AI gives agents immediate feedback on tone, pacing, and accuracy, helping them improve continuously instead of waiting for weekly reviews. It builds ownership and confidence, especially in remote teams.
Example: After a call, an agent can receive feedback about speaking too quickly and adjust in the next interaction.
How to improve agent productivity: Quick-glance table
These strategies and tools help teams take their call centers to the next level. Here’s how you can improve call center metrics:
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Key metrics to measure agent productivity
If you want to improve agent productivity, you need visibility into how your team works. The right metrics show where the agents slow down, where workflows break, and where tools fail to support them. These are the metrics that matter most:
Average handle time (AHT)
AHT measures how long an agent spends on each interaction, including talk time and wrap-up work. It’s useful, but only with context. Low handle times with poor CSAT usually mean agents are rushing. High handle times paired with low resolution rates point to broken workflows, missing context, or too much manual work.
First contact resolution (FCR)
FCR tracks how often an issue gets resolved in one interaction. It’s one of the strongest signals of agent effectiveness. High FCR usually means agents have the right context, tools, and authority to solve problems without follow-ups.
CSAT and QA scores
CSAT and quality assurance (QA) scores add the human layer. You can hit speed targets and still deliver a poor experience. These metrics reveal whether agents communicate clearly, follow the process, and handle issues with care, even when volume is high.
Agent occupancy and utilization
Occupancy shows how much time agents spend actively handling tickets. Utilization goes further by measuring how much of their logged-in time is productive. These metrics surface problems like uneven workloads, poor queue balancing, or workflows that waste time.
Resolution rate
Resolution rate shows how many tickets an agent closes in a given period. On its own, it doesn’t reflect complexity or quality. However, you’ll get the most useful signal about overall output and efficiency when you combine resolution rate with AHT and QA.
Most teams track a few of these. Strong teams look at them together and use the patterns to guide coaching, tooling decisions, and process changes.
Challenges of agent productivity
Agent productivity rarely drops because agents lack effort. It drops because the systems around them slow everything down. Fragmented tools, poor prioritization, and manual work show up again and again across support teams. Here’s what holds agents back:
Recurring tasks that don’t need human judgment
Summarizing calls, updating CRM fields, and searching for policy details drain energy and slow response times. This busywork pulls agents away from problem-solving and customer conversations.
AI customer service tools reduce this load by taking over repetitive tasks and freeing agents to focus on higher-value work.
High ticket volume with little prioritization
When every ticket looks the same in the queue, urgent issues get buried. Agents fall into a first-come, first-served pattern, even when it hurts the customer experience. Without AI-based triage or smart escalation, teams stay in reaction mode.
Long resolution times
Resolution time increases when agents lack context. Switching between systems to find account history, previous tickets, or internal notes adds friction to every interaction. Longer handle times follow, and overall agent productivity drops.
Lack of unified tools and information
Many agents jump between five or six tools to resolve a single issue. Each switch costs time and increases the chance of errors. Tool fragmentation makes it harder for teams to automate customer service, wears agents down, and contributes to performance dips.
Agent burnout and slow onboarding
Burnout often starts early. Managers often expect new hires to memorize workflows instead of getting support from intuitive systems. Training stretches on for weeks, and in fast-moving teams, that delay creates a productivity gap that’s hard to close.
Low morale and high turnover
When agents feel stuck doing low-impact work with little recognition or growth, morale drops. Turnover follows. The cost goes beyond hiring and training, and shows up in missed SLAs, slower resolution times, and uneven performance across the team.
How Lindy helps teams improve agent productivity
Lindy’s AI agents automate repetitive support tasks so your human agents can focus on higher-value work. With ready-to-use templates like support email ticket visibility agent or urgent ticket alert agent, you can start automating support with ease.
You can customize these templates using the drag-and-drop workflow builder, helping non-technical users offload repetitive work easily.
Here’s how Lindy can support call center teams:
Hands-on AI assistant
Most support tools ask agents to tag tickets, update CRM fields, summarize calls, and log follow-ups. Lindy changes that by listening to the reps, understanding the context, and taking the next steps automatically within the tools your team already uses.
By collaborating and sharing context, Lindy’s AI agents complete complex tasks and free up your team to focus on helping customers.
Supports multiple tasks
Lindy can take up a lot of repetitive tasks. Here’s what it can handle:
- Writing follow-up emails after customer calls
- Logging meetings and next steps directly in the CRM
- Drafting summaries for internal standups
- Triaging inbound tickets by urgency or topic
- Coordinating calendar invites for follow-ups
When Lindy automates these tasks, call center productivity and morale both improve.
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Connects with the tools you already use
Lindy integrates with more than 4,000 tools, including native integrations with Salesforce, HubSpot, Gmail, Google Calendar, and Notion. For teams already using other customer support tools, this flexibility helps them get the most out of their investment.
Let’s look at an example: A midsized allergy clinic can use Lindy to handle patient intake and EMR updates. One of the Lindy agents reads inbound faxes, extracts medical data, and summarizes it for the doctor, saving hours every day.
For support teams, the impact looks similar. They spend less time typing, have better context, and can hand off information faster, leading to better conversations.
Secure and privacy-first
Lindy follows industry best practices for security, including SOC 2, HIPAA, and PIPEDA compliance, as well as AES-256 encryption. It is ideal for teams working in regulated sectors like healthcare and finance.
Affordable to start
Lindy starts free and lets you execute up to 40 tasks/month. That’s enough to automate light workflows or try its capabilities before committing. The paid plans begin from $49.99/month, and offer phone agents, higher task limits, and a bigger knowledge base.
Try it today for free and help your reps focus on customers while AI handles the background work.
Frequently asked questions
What’s the most effective way to improve call center agent productivity?
Improving ticket routing, reducing manual steps, and automating repetitive tasks are the most effective ways to improve call center agent productivity.
How does AI reduce burnout in call centers?
AI reduces burnout in call centers by handling repetitive tasks like tagging, summarizing, and CRM updates. Agents spend less time on admin work and more time solving actual customer problems.
Which metrics should I track to measure agent performance?
You should track AHT, FCR, CSAT, QA, occupancy, and resolution rate to track agent performance. If you want to go beyond these, look at time-to-next-action and coaching response time.
What are the best tools to automate agent workflows?
Zendesk AI, Lindy, and Talkdesk are some of the best tools to automate agent workflows. They offer AI customer service capabilities, AI SDRs, and other AI features that help agents do their job better.
How can Lindy help increase productivity in my customer support team?
Lindy automates post-call admin tasks, triages tickets, and assists agents in real time to increase productivity in your customer support team. It integrates with your existing tools and can handle the first step of support. You can also set it up to answer common questions without needing a human.
Can AI replace human agents?
AI can assist with and even fully automate many repetitive, low-value tasks in the call center. However, tasks that require human judgment, empathy, creativity, or nuanced decision-making will always benefit from a human touch.
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