Modern call centers handle huge interaction volumes, but only a tiny fraction can be measured by manual QA—even though every single conversation has an impact on customer experience. For enterprise organizations, in particular, they’re missing out on critical information about customer sentiment, agent performance, and more.
As a QA manager, you know the limitations of doing things manually—it’s slow, time-consuming, expensive, and you only get a small glimpse at your contact center’s performance. But explaining this to other stakeholders—and showing them what can be achieved with AI-powered call center QA software—is tricky, despite the clear benefits of switching:
More satisfying customer experiences
Improved operational efficiency
Increased agent engagement and retention
In this guide, we’ll show you how to build a business case for AI-powered call center QA software so you can rally stakeholders to make changes. We’ll explore how you can showcase the upside of artificial intelligence (AI) and automation, push for new tools, and make a real impact on your organization.
Traditional call centers relying on manual QA struggle with modern customer (and operational) demands. Customer expectations are higher now than ever, so delivering a seamless customer journey and an exceptional experience is more important than ever, too.
Manual QA simply can’t cut it anymore—you end up spending excessive time on repetitive tasks while valuable insights slip through the cracks. To build a compelling business case for AI-powered call center QA software, start by outlining the core pain points of manual QA:
Start by compiling reports on your current QA process. Track the total number of customer calls, chats, and emails scored within a specific timeframe alongside the average time spent reviewing each one.
Then, document how often coaching sessions occur (whether one-on-one or group sessions), and calculate the labor costs for how long this process takes compared to adopting a new, AI-powered contact center QA process.
Beyond just baseline figures, think about the gaps that exist in your manual QA process. More than likely, you’re overlooking:
Shifts in customer sentiment over time
Compliance lapses hidden within conversations
Recurring patterns in agent behavior
Keywords or phrases that reveal product or service issues
It could take hours to collect this kind of information manually; time your QA team doesn’t have. But with AI, you can completely transform the process. What would have taken hours (or even days) happens in an instant, giving you a deeper look at your contact center operations.
Instead of manually sorting and listening to call recordings, AI automatically detects sentiment trends and compliance breaches, aggregates recurring behavior patterns, and highlights important keyword clusters all in real-time. Interactive dashboards are constantly updated, so you can immediately spot coaching opportunities, address risks, and monitor performance.
Your QA process has a significant impact on the overall performance of your call center, and its success. By laying out how common QA KPIs impact business metrics (and how a new QA process can improve both), you can illustrate just how useful AI-based call center QA software could be. Here are some common metrics you can use to drive your point home:
Average handle time (AHT): A reduction in AHT through faster issue resolution can lower operational costs and improve capacity planning.
First call resolution (FCR): Higher FCR rates drive customer loyalty, reduce repeat contacts, and boost NPS.
Quality scores: Improved QA scores tend to indicate stronger brand reputation and fewer escalations, protecting revenue and reducing churn.
Compliance adherence rates: Consistent adherence to regulatory standards minimizes fines, legal exposure, and audit-related expenses.
Agent engagement: Better, more consistent coaching can keep agents engaged, which improves service quality (and affects other KPIs).
Customer satisfaction (CSAT): Satisfied customers are 3.5x times more likely to come back in the future, boosting your bottom line.
Net Promoter Score (NPS): Just as they’re more likely to become repeat customers, delivering a better customer experience makes customers more likely to recommend your company to others, which can increase revenue and brand reputation.
If you’re figuring out how to build a business case for AI-powered call center QA (or how to start your own QA program), focus on the bottom-line impact of QA and what it can bring to multiple departments—like deeper insights into how effective your marketing is, new ideas for the product roadmap, or even lessening of security and compliance risks.
AI-powered call center QA software can completely transform the way you monitor and improve agent performance:
100% interaction coverage makes every call, chat, and email a source of actionable insights
Fully customizable AI scorecards you can apply specific evaluation criteria automatically, reducing manual workloads and speeding up QA feedback loops
Automated compliance detection flags breaches, reducing risk and ensuring consistent regulatory adherence
Faster QA-to-coaching feedback loops mean agents receive targeted guidance within hours, not days, leading to faster resolution of customer inquiries and complex issues
More consistent evaluations remove human bias, so every agent gets measured against the same benchmarks
Advanced AI analytics provides relevant insights in the form of sentiment trends, topic clusters, behavioral patterns, and more, helping managers prioritize coaching and track improvements over time
It’s also important to remember that AI does not replace the expertise of human effort and knowledge, it amplifies it. By automating routine tasks, AI allows your analysts to focus on strategic reviews and targeted coaching, but it shouldn’t take over the entire process.
Human intervention is still essential to interpreting nuanced conversations, crafting development plans, and ensuring that both virtual agents and human agents are getting the feedback they need to improve. You can’t replace one with the other—but you should combine the strengths of both.
Investing in AI-powered tools for QA delivers measurable results in terms of time, productivity, and engagement. If a QA evaluator saves 20 minutes per evaluation and completes 400 reviews monthly, that translates to over 133 hours saved—delivering a faster QA-to-coaching feedback loop that drives agent engagement and learning.
Your call center will see the results all across the board:
Shorter average handling time (AHT)
Fewer escalations
More confident agents
Your productivity gains will go beyond just saving time, however.
Think of AI-powered call center QA software that can flag compliance issues immediately—instead of analysts manually scanning each call for regulatory breaches, alerts surface in real time. A six-person QA team might find and address 50 critical issues per week without expending extra effort, where originally they might only find half.
Agent engagement is another key driver for improving call center ROI (and we’ve already hinted at it). When customer service agents get timely, relevant feedback, they become more confident and motivated. Higher morale and clearer development paths can make a significant difference in your agent turnover rate too, which then saves on hiring and training new employees.
Finally, faster decision-making powered by AI insights drives continuous learning and improvement opportunities. Real-time dashboards highlight shifts in sentiment, call drivers, and performance trends, allowing managers to course-correct faster than before.
A quick response to a sudden spike in product-related calls, for example, can prevent customer dissatisfaction from spreading, reducing the impact on revenue generation and your brand reputation.
When you’re planning on how to build a business case for AI-powered call center QA, anticipate the questions you’ll get from stakeholders and have clear, concise answers ready. Addressing concerns proactively builds confidence and shows that you’ve prepared for most edge cases. Some objections you might get could include:
“The cost is too high.” Outline the total cost of ownership versus savings from reduced manual reviews, lower turnover, and fewer compliance fines.
“Will it integrate with our systems?” Highlight open APIs, pre-built connectors, and implementation timelines that minimize IT workload.
“How can we measure ROI?” Show them the metrics we discussed previously—like time saved per evaluation, coaching turnaround, and compliance incident reduction—that tie directly to financial outcomes.
“Won’t it disrupt our current workflows?” Explain phased rollout plans, pilot programs, and creating hands-on training to help ease the transition.
“Is our data going to be secure and compliant?” Cover different encryption standards, access controls, and audit trails that meet industry regulations.
Persistent concerns around accuracy and reliability are, understandably, important considerations when it comes to AI deployment—especially with frequent stories about AI models continuing to hallucinate even as newer versions roll out.
Be ready to share evaluation studies, confidence scores, and including protocols to verify the results of the AI-powered call center QA software you want to use. Reassure teams that human reviewers will still be a core part of the process, with AI serving as an aid, not a replacement.
Don’t attempt to convert the entire call center at once—start small with a pilot project, focusing on a single team or channel, for example.
Develop targeted training plans tailored to individual roles (QA evaluators, supervisors, agents, etc.), processes for interpreting AI-generated customer insights, and proposed integrations into daily workflows. Try hosting hands-on workshops and creating step-by-step guides to help every team member gain confidence and proficiency.
It’s also vital to outline the most relevant key performance indicators (KPIs) to monitor during the rollout. Track important call center metrics that will show the rollout’s progress, such as:
Evaluation completion rates
Coaching turnaround time
AI confidence levels
Establish a feedback loop where QA analysts and coaches review AI suggestions, misclassifications, and propose adjustments to keep it accurate (and ensure that human oversight is involved from end to end).
Collaborating with your QA vendor is a must to calibrate AI performance. Share representative call samples and evaluation rubrics so the vendor can align their AI model with your standards (and prevent drifting or bias from impacting results). Then, schedule periodic check-ins to assess calibration progress, review dashboard metrics, and adjust thresholds as necessary.
If you can prove that your AI-powered call center QA software is meeting (or exceeding) your projections, then you know you’re on the right track. If you can showcase success early on, you’ll be better equipped to expand to other parts of your call center, and it makes it easier for stakeholders to become advocates for broader adoption.
Try to sum up your business case into a concise pitch deck or one-pager that can capture stakeholder attention and get your point across quickly. Create different versions of your pitch to address the priorities of different stakeholders, so you can make it relevant to their perspective.
Start with a clear value proposition: faster QA cycles, 100% interaction coverage, automated workflows, and keeping humans in the loop to act as safeguards. Organize slides or sections around business outcomes—like cost savings, risk reduction, improvement in customer satisfaction scores, and increased agent retention—not just the product features of AI-powered call center QA software.
Try to outline the benefits in clear terms, like:
Increased operational efficiency: Accelerated evaluations free up analysts to work on more high-value work.
Delivering consistent quality: Standardized scoring eliminates human bias and boosts reliability.
Risk mitigation: Real-time compliance alerts safeguard against regulatory penalties.
Better performance insights: Advanced analytics highlight coaching priorities to drive revenue growth.
More engaged agents: Faster, more precise (and personalized) feedback to agents creates a more motivated workforce.
Quality assurance isn’t a money pit that drains finances—it’s essential to driving performance, improving customer relationships, and achieving goals across the entire business. And AI contact center solutions powered by machine learning, large language models, and natural language processing have accelerated these benefits.
If you can find the best AI-powered call center QA software and build a convincing business case, you’ll gain access to brand new insights, drive employee accountability, create better coaching programs, engage agents, and reduce customer churn.
But, pitching a successful business case isn’t a walk in the park. Outlining the issues, collecting and connecting the data to highlight what can be improved, showcasing the potential ROI and careful planning for a successful rollout are all critical if you want to make a strong case.
And without the right AI-powered QA software, your business case could fall flat, potentially ending the project before it ever gets off the ground. Which means you need to pick cutting-edge technology that can support both your business case—and your business.
Scorebuddy can help show your stakeholders the strengths of AI-driven tools for QA. With 100% interaction analysis, deep insights into agent performance, automated evaluations, and powerful workflow tools, it can help drive your point home and set your call center up for success.
Start your interactive demo now to see how Scorebuddy’s AI-powered quality assurance platform supports the needs of all your key stakeholders.
