How an AI-Powered Contact Center Can Drive Better QA

    Contact centers need to deliver the best possible customer experience—after all, that’s what they’re supposed to do. However, factoring in agent happiness, operational costs, and compliance can make it difficult. This is why an effective quality assurance (QA) program is essential to achieving optimal CX.

    But the way most call centers approach QA is problematic. Whether it’s a perceived sense of QA as “policing” (rather than a path to development opportunities), inconsistency among scores and evaluators, messy data, or small sample sizes—it can all lead to an ugly, bloated system.

    While it can’t solve the problem outright, AI can contribute significantly to improving your QA processes. It can help you create (and reach) actionable goals for both the customer and agent experience through automation, analytics, and more.

    Let’s take a look at the modern problems QA teams are facing and how AI solutions can bring them to an end.

    Why do we need AI-powered call centers? The problem with QA today

    AI capabilities have been slowly creeping upward in the last ten years. Apple’s Siri assistant was released in 2011 (and was incredibly impressive at the time), but it is nowhere near the same level as OpenAI’s new GPT-4. The GenAI boom has completely shaken up the world, with these new capabilities far outperforming anything we’ve seen before.

    AI is now capable of:

    • Performing tasks that used to depend entirely on human intelligence (such as decision-making and problem-solving)
    • Natural Language Processing (NLP) and Natural Language Understanding (NLU)
    • Advanced computer vision and image/text recognition at a faster rate than humanly possible

    This all feeds into key quality assurance functions like evaluations, analysis, reporting, and more.

    Plus, it’s able to manage almost the entire customer journey with automated responses, more effective chatbots and self-service options, and even personalization of interactions based on historical customer data.

    These features are a massive boon to call centers as they continue to face newer and more difficult challenges.

    Many of these challenges, such as higher interaction volumes and customer expectations, make it incredibly difficult for call centers to keep up.

    An AI-powered contact center can leverage these tools to not just speed up workflows but handle more interactions with a greater level of care and attention than ever before—without burning out agents or hiring additional staff.

    Let’s break down some specific examples of how AI for contact center QA can enhance processes—leading to better customer interactions and stronger CX—and how it contributes to overall call center operations.

    7 biggest contact center QA challenges (with AI-powered solutions)

    Limited QA visibility? 100% interaction analysis

    It’s not uncommon for call centers to struggle with auditing a large volume of their interactions.

    Traditionally, most contact centers are only able to process between 1-2% of their interactions, and there’s no guarantee they are relevant interactions to pull insights from (as opposed to your run-of-the-mill interactions where everything is just fine).

    Your agents feel this way, too—they don’t want their performance measured on the basis of an incredibly small sample size. They’re tasked with hundreds of calls, why should they only be measured by an insignificant fraction of them?

    On top of that, it doesn’t give your teams enough data on what’s driving customers to reach out, their overall experience, or where processes and service may be lacking.

    However, with an AI-powered contact center, you can dramatically improve the number of interactions you audit—going from maybe 2% to 100% without any extra effort from your evaluators. That’s the power of AI for contact center QA.

    With AI tools, your teams can easily parse and analyze every single interaction that passes through your call center, whether it’s voice calls with agents or text conversations with chatbots.

    You’re able to collect data that can show you what’s causing customers to reach out and how they feel about their service journey, as well as give insights on how you can improve your processes.

    Overwhelming quality assurance demands? QA auto-scoring

    Interaction volumes have risen dramatically in the last few years—61% of call center leaders reported an increase in calls in 2022 alone.

    Trying to keep up with this demand is incredibly difficult, leading to staffing more agents, massively increased wait times, and agents stretched thin by the sheer number of interactions in a day.

    Integrating AI for call center QA can make a significant difference in the workload for your evaluators.

    On top of analyzing 100% of interactions, you can automatically score these interactions, giving evaluators more time to analyze and offer meaningful feedback, instead of spending hours just trying to find these meaningful interactions.

    Plus, AI tools are far more consistent and objective than a human, offering more detailed insights for your evaluators to work with at a faster pace than ever before.

    Lengthy QA scoring process? AI-powered evaluator assistance

    More complex customer demands and interactions mean your evaluations will become more complex, too.

    Your evaluators will need to keep up with these more complex issues and situations, which means that scoring takes longer, and it becomes more difficult to stay consistent between interactions.

    With an AI-powered call center, your evaluators can keep up with this increased (and likely increasing further) level of demand.

    On top of automatically scoring interactions, they can analyze and summarize data for your evaluators to get information at a glance, or dive deeper when necessary.

    This means that you can not only reduce the time spent scoring interactions, but lower the labor costs for each interaction—while still increasing the number, and quality, of manual evaluations.

    Poor agent engagement? Targeted coaching backed by AI analysis

    Maintaining high agent engagement is crucial for running an effective contact center. If you have low engagement, you’re at risk of high agent turnover, another common industry challenge.

    And hiring new agents isn’t a simple fix—hiring, training, and onboarding are all far more costly and time-consuming than holding on to your existing employees. If you don’t solve the problem at its source, hiring fresh talent will never make up for low morale and high turnover rates.

    This is precisely why Generative AI for contact centers can be so powerful. It can work with your agents to offer personalized, real-time feedback and even drive tailored coaching sessions to help them work on their own weaknesses.

    The more impactful and personal the training offered to your agents is, the more effective it is.

    This leads to a better return on investment and overall buy-in for the QA process, which leads to lower stress, less agent burnout and churn, and a more positive, healthy working environment.

    Messy QA data? AI analytics and reporting

    A ton of data flows through your average contact center, but it’s not always structured in an easily digestible way (or even found all in one place). And this data is valuable—you shouldn’t be throwing it away or leaving it to collect dust.

    Modern AI for call center QA is able to analyze, review, and categorize this data to help identify churn risks, pinpoint opportunities for upselling and cross-sales, and even offer personalized deals and discounts.

    Plus, this data can be used for more than just sales—it can give your customers a personalized experience from start to finish.

    You’ll be able to pick out the root causes that are driving customers to reach out, and even automate customer sentiment analysis to gain a deeper understanding of what they want and need.

    Inconsistent scoring? Objective QA with AI

    Scoring interactions, while time-consuming, can also be affected by humans beyond efficiency levels. The human element introduces chances for errors, bias, and subjectivity that can get in the way of a fair and neutral score.

    AI for contact center QA can eliminate these issues, ensuring that evaluators can provide clear, correct scores and feedback that isn’t impacted by human limitations. This means your agents get more detailed and accurate feedback, leading to a better customer experience in the end.

    This also helps get your agents involved in the QA process. Without the fear of bias and the elimination of differing scores from different evaluators, agents get a more transparent (and consistent) experience, leading to better buy-in.

    Facing more complex regulatory demands? AI-backed compliance

    Compliance is non-negotiable, but that doesn’t mean it’s easy for agents to follow. And with the increase in customer demands (and interaction volumes), especially within finance and the healthcare sectors, staying compliant becomes even more challenging.

    An AI-powered contact center can automate compliance, though it does have limitations. It can minimize the risk of human error by observing interactions and correcting agents mid-interaction.

    It can also automate some processes, such as redacting personally identifiable information (PII) and performing and maintaining audits. This helps lessen the risk of fines and data breaches and gives your agents more time to focus on improving conversation quality.

    Bonus examples: 4 other practical uses of AI in the contact center

    Weak first contact resolution (FCR)? Real-time guidance for agents

    Did you know that 52% of customers rank FCR as the most important factor that impacts their customer experience? And with waiting times ranked as the second-highest factor, it’s evident how important nailing that first interaction is for your customers.

    By adding AI for contact center QA teams, both your agents and your customers are able to get the information they need, preventing bounce-back and the risk of repeat calls.

    Agents can leverage AI during interactions to search internal knowledge bases and documentation to get specific answers in real-time.

    Customers can get the same information from AI-powered self-service options like chatbots and virtual agents, which can be trained on the same data.

    Overwhelmed by interaction volumes? Virtual contact center agents

    Speaking of virtual agents, an AI-powered call center can use these AI tools to create (or improve) self-service options to expand their reach and meet demands. They can run 24/7 and extend your call center to a global, multilingual audience.

    This means you can not only provide support to more customers, but you can meet demands regardless of the time of day.

    And since AI tools can scale much faster than you’re able to onboard new agents, they can handle fluctuating call volumes without impacting wait times and customer service standards.

    Long queues and wait times? Intelligent call routing

    Nobody wants to wait in a queue for hours, but it’s been an unfortunate reality for many consumers in the past.

    Integrating AI tools and creating an intelligent routing system is an excellent way to cut down on lengthy wait times, and speed up workflows.

    NLP can interpret your customer’s intent, analyze their sentiment, and then direct them accordingly to the right agent, department, or team. It can also utilize customer data to automatically route based on prior interactions and provide the agent with a plethora of information to assist the guest before they’re connected.

    Difficulties with staffing? Predictive analytics

    One of the most prevalent issues that plague any contact center is staffing. Whether it’s agent churn, rising interaction volumes and wait times, seasonal spikes, or just random fluctuations, it’s never easy to correctly predict how much staff you’ll need at any given time.

    AI changes this thanks to its ability to analyze historical data, agent schedules, and real-time data and offer predictive analytics. Your teams can then leverage that data to call in additional agents, create more efficient schedules, and cut down on stress from understaffing (or wasted spend from overstaffing).


    We all know that AI isn’t going anywhere.

    An AI-powered contact center has clear advantages over the competition, solving challenges that constantly plague the call center industry.

    Whether it’s low agent engagement, complicated and lengthy QA processes, and evaluations, or just a sheer lack of actionable, informative data, AI alleviates these issues with practical, tangible solutions.

    Adopting AI offers call centers a way to provide a dynamic range of solutions for common QA challenges with accessible, integrated solutions that benefit your evaluators and your agents alike. But if you want to offer the biggest benefits to your call center, you need Scorebuddy AI.

    With Scorebuddy, you can achieve 100% interaction analysis, automatic QA scoring, AI assistance for evaluators, and more.

    Sign up for a demo today and see how you can transform your QA process.


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      Will AI take over call centers?

      AI will not completely take over call centers but will significantly transform them. AI will handle routine tasks and provide real-time support, enhancing efficiency and customer experience. Human agents will still be crucial for complex, emotionally nuanced interactions.

      How to use AI in contact center QA?

      Some of the most common use cases for contact center AI include:

      • Automated customer support: AI, through chatbots or voice assistants, provides immediate responses to common queries 24/7, improving customer satisfaction.
      • Personalized customer experiences: Leveraging AI analytics, contact centers can offer personalized advice and solutions, enhancing customer engagement.
      • Call routing optimization: AI intelligently routes calls based on complexity and agent expertise, reducing wait times and improving resolution rates.
      • Sentiment analysis: AI tools analyze customer mood during interactions, allowing for timely adjustments to improve the conversation tone.
      • Predictive analytics: Using historical data, AI predicts caller intent, enabling preemptive solution offerings and streamlined service.
      • Agent assist tools: AI improves agents’ efficiency and confidence by offering real-time insights and information during calls.

      Can AI be used for customer service?

      AI can be used for customer service by automating routine tasks, providing 24/7 support through chatbots, offering real-time assistance to agents, and analyzing interactions for actionable insights. This improves efficiency, reduces costs, and customer experience.