Delivering great customer support has never been as important as it is now (88% of consumers agree). But, this also means that customer expectations are higher than ever, and contact centers need to keep up if they want to prevent them from jumping ship to the competition.
With the future of customer service quickly turning towards AI-powered tools and automation, businesses can start to close the expectations gap. But this raises another issue: is artificial intelligence (AI) going to replace customer service agents?
Well, the answer is a resounding no, but that doesn’t mean that AI is useless—far from it. Getting the most out of both AI and human agents (and making them work together) gives you the best of both worlds.
In this article, we’ll take a look at how blending AI with human agents in your contact center could work—and how this balance is set to define the future of customer service.
AI is transforming call centers faster than most would have imagined. As of this year, 98% of contact centers have adopted AI for tasks like:
Running chatbots
Powering virtual agents
Performing data analytics
These tools are fantastic for managing repetitive, routine tasks and reducing operational costs. Chatbots can quickly answer FAQs, provide 24/7 support, process simple transactions, and power self-service. Virtual agents offer faster response times and shorter queues, and free up human agents for more complex tasks.
But despite these gains, AI is far from replacing customer support agents—and it never should. Generative AI-powered bots can now deflect 50-60% of inquiries, but that still leaves 40-50% requiring human oversight.
AI lacks the ability to fully understand nuance, context, and emotion in customer service interactions. Even the most advanced language models can misinterpret subtle cues or respond in ways that feel impersonal.
“There’s a rush to deploy GenAI bots, but customers still want to speak with people—especially in emotionally charged or complex scenarios.” - Derek Corcoran (CEO & Founder, Scorebuddy)
Customers need empathy, patience, active listening, and a human connection. AI has difficulty handling emotionally charged conversations or situations beyond its programming, so escalation paths are crucial to avoid frustrating your customer base.
Problem-solving is also something that AI can’t quite grasp. For complex issues, unexpected queries, or anything just outside the norms of your normal call center operations, agents need to think critically, improvise, and negotiate outcomes. AI simply isn’t capable of this level of creativity, critical thinking, or reasoning to come up with effective solutions.
Additionally, AI models need historical data to be trained on and to make accurate decisions. Without it (or if it’s not carefully managed) they can perpetuate biases or create hallucinations, giving customers the wrong information or impression. Trust in AI-driven systems is also dependent on the customers you support.
According to Axios, over 70% of people in India and China trust AI, while the United States stands at only 32%.
The goal isn’t to replace human agents with AI tools, but to empower them with new technology to make their jobs easier. AI can boost human performance by:
This enables your agents to focus on what they do best; delivering true personalization and high-quality service.
The future of customer service has evolved beyond basic scripts, limited spreadsheets, and simple chatbots. Today’s tools use natural language processing (NLP), machine learning, and real-time analytics to handle a growing range of tasks in modern call centers.
But, these technologies work best when they elevate human performance—not when they try to replace it.
AI-powered chatbots and virtual assistants are capable of managing a high volume of daily inquiries across multiple channels, including chat, email, SMS, and social media platforms. They handle routine requests like:
Order tracking
Scheduling appointments
Password resets
Account updates
This automation reduces call volume and frees up your customer service representatives to work on more complex cases, instead of getting bogged down by tedious, repetitive inquiries.
During customer interactions, AI offers real-time support to agents, analyzing the conversation as it happens.
These tools monitor keywords, tone, and sentiment to suggest the best next action or recommended adjustments to scripts to help them navigate through interactions. And with these deeper insights, agents can improve consistency, compliance, and customer satisfaction in every conversation.
The future of the customer service industry will hinge on the use of AI-powered quality assurance tools to automatically review 100% of interactions instead of the average of 2-3%.
They can flag interactions with compliance risks, poor sentiment, and coaching opportunities. Then, QA managers can use these insights to prioritize reviews, deliver targeted feedback, and continuously improve agent performance.
AI uses the massive amount of data collected by call centers to personalize customer service by analyzing historical data and past interactions from your customer relationship management (CRM) system.
It can suggest relevant offers, anticipate customer needs, tailor responses to individual preferences, and even suggest recovery tools to help agents prevent escalations. This supports agents in creating more engaging, relationship-driven conversations that boost customer loyalty and retention.
AI-powered analytics don’t just cover customer data, it can also identify performance trends across customer service teams and individual agents.
This information allows for custom coaching and training programs for individual teams and agents, providing unique and actionable feedback based on specific needs. With this targeted approach, agents can build key skills, close knowledge gaps, and deliver higher quality service.
AI-driven sentiment analysis can detect shifts in customer sentiment in real-time, giving call centers the ability to see how individual customers feel at any given moment.
If frustration or dissatisfaction gets picked up from an interaction, the system can alert supervisors or trigger an escalation path to help recover the situation before it worsens. This ensures that vulnerable interactions receive human attention (or supervisor intervention) before they become bad experiences or spiral out of control.
The future of customer service will always be centered around humans, no matter what side of the interaction they’re on. While AI tools handle repetitive tasks and provide valuable insights into customer behavior, they aren’t equipped to manage complex, nuanced, or emotional conversations.
“Agents aren’t going anywhere. They’re just getting better support.” - Derek Corcoran (CEO & Founder, Scorebuddy)
Real agents bring empathy, creativity and judgement to human interactions that AI cannot replicate. The most effective call centers should be combining the efficiency gains of AI-driven solutions alongside a human connection if they want to excel.
Traditional QA practices in call centers are severely lacking. Most QA teams are only able to cover a tiny fraction of customer interactions due to time and resource constraints. This means they’re only randomly sampling 2-3% of thousands of calls per month, leaving serious blind spots in quality monitoring and forcing them to be reactive instead of proactive.
Manual QA is also inconsistent. Human evaluators can interpret scoring criteria differently, leading to biased results that impact fairness and coaching effectiveness. The future of customer service means keeping pace with new scripts, regulatory changes, and evolving customer demands—which is incredibly difficult when teams can’t scale or agree on the same criteria.
By automating core QA functions and surfacing actionable insights, AI allows managers to focus on what matters most; improving agent performance and driving better customer engagement.
By following the four-pillar model for AI-powered call center quality assurance (as outlined by Derek Corcoran in our webinar on Integrating AI With Human Expertise in Customer Service), you can create a framework to modernize your QA program for the future of customer support.
AI enables the complete automation of core QA workflows. One of the biggest shifts is the ability to analyze 100% of customer interactions across all communication channels, such as:
Voice calls
Live chat
SMS
Social media channels
By automatically transcribing, analyzing, and scoring every conversation (without human intervention), this comprehensive coverage ensures no interaction slips through the cracks.
With AI-based customer service automating 50-60% of questions on a standard QA scorecard, human evaluators have more time for conducting detailed analyses and providing feedback. AI can auto-fill scorecards based on objective measures, including:
Adherence to specific phrases
Tone and sentiment
Keyword usage
Issue resolution outcomes
Plus, automation improves both consistency and efficiency. QA teams can shift focus from repetitive scoring to in-depth analysis, targeted coaching, and strategic improvements. It also makes it easier to maintain QA programs at scale as call volumes grow or fluctuate seasonally.
AI does more than just analyze interactions, it reveals patterns that manual reviews can’t easily detect through their smaller, random sample sizes. By processing entire data sets, AI surfaces:
Trends in customer concerns
Product and customer feedback
Friction points in the entire customer journey
Increased scale in compliance monitoring enhances trust, dependability, and leads to more accurate (and efficient) regulatory adherence. AI can automatically flag interactions that present compliance risks or deviate from company standards, providing managers with early warnings.
Recurring issues—like common call drivers or frequent issues—become visible through AI-driven insights. Then, managers can use this data and share it with product, marketing, and operations teams to address root causes and improve end-to-end CX.
AI enhances the capabilities of QA managers and supervisors by powering dynamic dashboards that visualize key customer service trends. These dashboards provide real-time visibility into metrics like average handle time (AHT), agent and customer sentiment, first call resolution (FCR), escalation rates, and more.
Performance trends can be visualized across individuals, teams, shifts, or sites, helping pinpoint strengths and weaknesses. AI-powered customer service can also highlight outliers or changes in performance patterns that might need a closer look.
“QA has always been a superpower. With AI, it now has a turbo engine.” - Martin Teasdale (Founder & Host, Get out of Wrap)
All of these capabilities help QA leaders make more informed, faster decisions. Instead of relying solely on limited samples and anecdotal feedback, managers can act on objective data that reflects the full customer experience.
The final pillar helps QA teams move beyond measurement to action. AI can analyze agent-level performance data and recommend personalized coaching and training plans.
Agents get targeted feedback based on their unique conversation history and skill gaps, making customer service training more effective and relevant. AI can also identify missing or unclear content in knowledge bases or call scripts, helping refine the materials that support agents in live calls.
Additionally, AI can recommend workflow optimizations to streamline day-to-day operations. By identifying process bottlenecks, frequent call escalation causes, and coaching opportunities, it enables call center managers to continuously improve operations, agent performance, and customer satisfaction.
The future of customer service will involve AI, but adopting it in your organization isn’t the same as just adding some new software. It’s a complete cultural shift that will affect the entire operation from top to bottom.
AI will change how agents work, how managers coach, and how performance is measured. Which means preparing your team requires thoughtful leadership, clear communication, and ongoing support.
Rolling out AI-driven tools without preparing employees can lead to resistance or confusion. Agents may fear that AI will replace them or view new tools as surveillance rather than support. You need to show them that AI is designed to enhance—not replace—their jobs or what they do.
So how do you do it? Here are some ideas you can use to get the ball rolling and avoid potential issues with AI-based technology:
Employees need to understand the purpose of advanced AI-driven technologies. Less than 40% of the global workforce has access to AI in the workplace, and 56% of call center agents think that AI isn’t even helpful for daily tasks.
You need to show them that AI is there to handle the repetitive work, surface accurate insights in the data, and give them more time to focus on the customer. Be transparent about how AI will impact workflows and performance expectations, and let them be involved in the process to build trust and buy-in.
Understanding the upsides of AI and what you're aiming for calms fears and makes collaboration easier.
AI tools are only effective if employees know how to use them. You must provide hands-on training that shows agents and management how to take advantage of AI insights, dashboards, and day-to-day recommendations.
Offer coaching to help agents understand how AI evaluates conversations and scores performance. When agents trust the system and see its value, they’re more likely to engage with the tools and embrace feedback.
This also means you need to keep up with the training as time goes on. A handful of sessions right at the start won’t suffice.
59% of call centers fail to provide ongoing support to help customer service reps navigate AI-driven workflows, which only increases frustration, decreases engagement, and damages agent experiences. AI skills training helps employees discover how AI can boost their work, leading to a better overall QA process.
AI can elevate performance, but the human element remains essential for the future of customer service. Emphasize the irreplaceable human skills of empathy, critical thinking, and relationship building in contrast to AI-based customer service.
Encourage and coach your agents to focus on these strengths while using AI to enhance their efficiency and accuracy. QA managers should provide coaching that blends data-driven insight with emotional intelligence and soft skills development.
“If team leaders just forward AI reports to agents without interpretation, it won’t land. Coaching matters more than ever.” - Martin Teasdale (Founder & Host, Get out of Wrap)
AI is transforming every industry, and that includes how customers are reaching out and connecting with businesses. And to maximize this impact, call centers need to carefully manage every step of the customer journey—from routing and first contact to escalation and resolution—to ensure AI and human agents work together to create a seamless experience.
With intelligent call routing systems, AI can analyze intent and direct customers to the best resource—whether that’s a self-service tool, a chatbot, or a live agent. This reduces frustration and helps customers get the help they need faster, from the right source.
“We’ve all been stuck in a chatbot loop. By the time you reach a human, you’re already frustrated. The handoff has to be seamless.” - Aaron Mulville (Scorebuddy)
As interactions progress, AI can support agents in real time with insights and next-best actions. Escalation paths should be clearly defined, allowing human agents to take over when empathy or complex problem-solving is required. On top of that, customers should always have the option to speak to a human—no matter what the issue may be.
AI-powered QA tools play a critical role in improving these journeys. By analyzing 100% of interactions, AI can uncover where customers are getting stuck or frustrated. These friction points—such as confusing menus or repeated handoffs—can then be re-evaluated.
With AI making such a massive impact on the world as a whole, it’s impossible to see how businesses can maintain a competitive advantage without using it. And within customer success, adopting AI means a significant boost to speed, efficiency, and scalability—as long as it’s paired with human empathy and insight.
So, how do you get ready for what’s coming? The future of customer service lies in AI-powered collaboration; prepare your team to embrace it, and you’ll be ahead of the game.
But, that doesn’t mean that it’s just as easy as adding a new tool and calling it a day. For successful AI-powered support, teamwork is crucial. You need to ensure your AI tools are accurate, precise, and capable of continuously fulfilling agent and customer needs.
To see how Scorebuddy is supporting the future of customer service, try our interactive AI-QA demo. With AI-powered QA, you can evaluate 100% of your interactions across both AI bots and human agents.
