Most support teams we talk to are dealing with the same problem. Tickets keep piling up, response times are slipping, and adding more agents is not a sustainable fix anymore.
The good news is that the data is clear on what actually works. A Stanford-MIT study found that support agents using generative AI improve their productivity by 14% on average. The Federal Reserve Bank of St. Louis found that workers are 33% more productive during every hour they use AI tools. And according to a 2025 report by NextPhone, service professionals save over 2 hours every single day just by using AI for faster responses.
But here is the thing. Most teams are still using AI as an add-on rather than building it into how agents actually work day to day. That is where the real productivity leak happens.
At Thunai, we have seen that the best support teams are not choosing between AI and humans. They are using AI to handle the volume and the repetition, while human agents focus on the conversations that actually need judgment and empathy.
This guide covers 7 practical ways to improve agent productivity for AI-powered support teams, with real examples from companies already getting results.
How to Improve Agent Productivity and Deliver Exceptional CX
1. Automate Recurring L1 Tickets Using AI Agents
Here’s the deal! Most customer support issues raised are recurring issues for the customers, and the fact is that they can be solved instantly with AI.
So, the most impact can be created by automating L1 support, which does not hamper business revenue or profitability.
- A good example for this would be how Salesforce resolves 75% of visitor issues using Agentforce (thier own AI platform), for self-help support that responds and shows customers fixes instantly.
- Thunai AI voice agents come with screen-sharing capabilities that help automate L1 support and also automate ticket resolution in under 0.8 seconds on platforms like ServiceNow and Salesforce.
- To drive this point home, Gartner estimates that AI will reduce contact center labour costs by over $80 billion in 2026.
- According to the report, they estimate this will be achieved by automating around 10% of Agent interactions. Meaning, L1 support should be the first thing your support team automates.
2. Help Agents With Accurate Information In Real-Time
A lot of the time, for some support teams, the real issue is digging through multiple databases and keeping customers on hold until they get answers.
Getting the right answers often means agents need to go through policy information, CRM data, or even past tickets (wasting time, effort, and ROI).
But when it comes to how to improve agent productivity, real-time agent assist or tools that listen in on calls, read emails, and chats to help avoid most timeslips.
- Companies like Crédit Mutuel use IBM Watson for real-time agent assist. This helps one of France’s leading banks resolve each of the 350,000 inquiries they get daily over 60% faster.
- Another good example would be how companies like CarMax managed to maintain a nationwide inventory of 45,000+ cars using AI, so customers can get immediate answers (something that would take 11+ years manually).
3. Build a Positive Work Environment With Career Growth Plans and Incentives (With Immediate Visibility)
The most no-brainer solution to improving support agent productivity would be to create CDP (career development plans) or, alternatively, incentivize agents with bonuses or benefits for certain metrics hit.
The fact is that most agents now realize that to survive in a newly changing ecosystem, they need to change their skill set and adapt to the newly arising AI trends
- One good example of this would be how Microsoft tracked the daily progress of its customer support agents on personal dashboards based on learning and customer support resolution benchmarks. Hitting this added them to a higher bonus bracket and resulted in Microsoft achieving 10% higher productivity.
- Another instance would be how Barona (formerly Ageris) allowed real-time visibility of their agents' compliance and FCR (first call resolution), which would translate into immediate corporate perks or monetary bonuses. This led to 9% growth in productivity KPIs.
4. Measure Agent KPIs for Customer Support With Automated Call Scoring
Similar to our earlier point, as mentioned, tracking agent KPIs helps drive productivity and coaching opportunities for teams.
The reality is that most customer support agents want to do a relatively good job, but lack the visibility needed to improve.
- Tracking call center KPIs or customer support metrics like FCR and AHT, with automated QA on calls, helps add this visibility
- An excellent example of this would be how Observe AI used Amazon EKS to scale QA automation and achieve a 40% reduction in infrastructure costs.
5. Set-Up AI Agents for Omnichannel Support
Omnichannel AI support is, to many, seen as a buzzword or another overhead cost that’s immediately needed.
AI voice agents help support customers with real-time translation and low-latency human-like voices. Email AI agents and AI chatbots do the same.
- For companies that need to make sure there's compliance and accountability, pairing AI assist capabilities with human agents helps speed up replies and reduce overall burnout. But most reduce the number of customers who have to wait for a resolution.
- One real-world case study that shows how omnichannel AI support helps improve agent productivity would be OPPO. Since most of their customer preferred WhatsApp or Facebook Messenger, by partnering with Sobot, OPPO saw 94% positive feedback and a 57% repurchase rate.
6. Optimize Call Routing with AI so Customers and Teams Save Time
Ever wait on hold to have a customer support agent tell you they need to transfer your call again? It goes without saying that it wastes your time. But even worse, it just makes you more annoyed with the support team.
Companies like Nissan are staying ahead by using conversational AI to remove this. They went ahead to more than double their overall call containment (the number of calls resolved with automation) from 25% to 55%.
- But here the main idea is to use conversational AI with NLP. Traditional tools like IVR rely on static tools and keyword matching that can be wildly inaccurate.
- In fact, according to a 2026 Natterbox report, most CX leaders (over 76%) use AI for the triage part and add human-in-the-loop for resolution since that is actually what customers want.
7. AI-Powered Insights and Sentiment Analysis for CX Improvement
The fact is, the support industry has mixed feelings at best when it comes to using AI for ALL support.
But when dealing with how to improve agent productivity, customer support sentiment analysis is beneficial.
More than just having KPIs, this adds emotions based on tone, words used, and even complete call summaries that are helpful in the case of customer escalations or recurring issues.
- This can be seen with major companies like T-Mobile and even PayPal using CX tools like NICE to automate 100% of their call scoring.
- It was reported by NICE that globally, this method of call scoring resulted in 256% more positive sentiment, 25% shorter calls, and 53% fewer repeat contacts.
Start Improving Agent Productivity With Thunai AI
When dealing with how to improve agent productivity using AI, enterprise AI CX platforms like Thunai help by allowing both automation and an experience where human agents and AI work alongside each other.
For CXOs, it's very clear that using AI completely might not be valid and, in many ways, creates a cold and non-approachable brand image.
Thunai helps navigate that with tools that help improve human interactions and agent productivity with:
- Thunai AI Co-Pilot (Thunai Sidekick): This tool helps human agents answer customer questions in real-time without switching tabs or putting them on hold. It pulls information from your approved SOPs, policies, and past closed tickets to help resolve issues more quickly.
- Thunai Omni: Using Thunai Omni, you can engage with customers over voice, chat, and email. Moving past this, AI agent workflows can also vary based on your requirements and needs. Thunai Omni also integrates with your VoIP or CCaaS to answer customer queries with human-like responses.
- Real-Time Live Translation: Thunai allows you to deal with global barriers with real-time translation in over 200+ languages - this means customers can choose the language they’re most comfortable with.
- Thunai MCP: This tool allows you to automate manual entry on CRM or ticketing tools. Using Thunai MCP, you can connect with all CCaaS, CRMs, databases, and relevant tools to automate full workflows the way you want.
- Unified Dashboard for CX Metrics: Thunai allows teams to track all team call metrics, recurring issues, and even escalations in real-time to avoid the hassle of switching tools.
- 100% QA and Call Scoring Using AI: Thunai helps you track customer sentiment on 100% of calls (with transcripts), even based on your own SOPs and benchmarks. Meaning, you can map your CX experience to what matters most to your company.
- Thunai Brain (Knowledgebase): For finance and insurance support teams that have to deal with many detailed policies, this tool helps with an accurate knowledge base that pulls the latest information from the files, videos, URLs, and call transcripts you add to it.
Want to know more about what this looks like? Schedule a free demo with our team!
FAQs on How to Improve Agent Productivity Using AI
How to improve agent productivity?
When it comes to improving agent productivity, there are three basic principles - namely, scoring agent performance with metrics like AHT. And on the other hand, also providing coaching based on insight and incentives to help improve agent performance.
How does agentic AI improve productivity?
Agentic AI helps reduce the number of tabs and tools agents have to switch between. It also reroutes calls to the right agents and completely automates the full L1 customer support process to help them focus on more major issues.
How to improve agent productivity in a call center?
When it comes to improving agent productivity in call centers, tracking call center metrics in real-time and then incentivizing agents to upsell or hit specific benchmarks has proven to be one of the most effective routes.
