I recently had the privilege of speaking at Frost & Sullivan’s fall event about an exciting technological shift happening today—conversational AI, powered by Large Language Models (LLMs). As contact centers wrestle with the increasing demands of customer experience, agent proficiency, and operational efficiency and quality, AI presents a unique opportunity to tackle some of the industry’s biggest challenges head-on.

In my session, I discussed the power of LLMs to transform Quality Assurance (QA) in contact centers, and I want to take this opportunity to dive a bit deeper into the practical insights that could take your contact center operations to the next level.

The QA Bottleneck: Why It’s a Problem

For decades, Quality Assurance in contact centers has been largely manual and slow. Even with dedicated QA teams, most contact centers only manage to review a small sample of agent interactions—around 1-2%. This limited sample size makes it difficult to extract meaningful, data-driven insights about agent performance or to implement changes that truly move the needle.

Further complicating matters, the time lag between an agent’s interaction and receiving feedback is often significant enough that by the time performance issues are flagged and addressed, it may be too late to course-correct in a way that meaningfully impacts the customer experience.

This is where conversational AI can be a game-changer.

Leveraging Conversational AI to Overcome QA Challenges

Conversational AI enables contact centers to analyze 100% of an agent’s interactions—every call, every chat, every interaction, no matter which type. Read that again. 100%. What does this mean, exactly?

AInsight, Alta’s proprietary AI-powered QA tool, eliminates the statistical insignificance of current QA practices by evaluating every single interaction that comes into a contact center, instead of the industry standard, 2%. Even better, these analyses are:

• Done in real-time, which means immediate feedback is possible
• 100% calibrated, so there is no risk of variation in evaluations due to human fluctuation.

With the ability to evaluate every interaction, in real-time, and with machine-like consistency, the use cases are broad:

• Agents have the ability to adjust their approach during interactions. When you combine that with the ability of LLMs to detect sentiment, emotion, and intent, the data becomes incredibly rich and actionable.

For example, imagine being able to identify trends in agent behavior that lead to negative customer experiences—not days or weeks later, but immediately. This can empower agents with AI-driven recommendations in the moment, and change the landscape for product development, process gaps, potential recalls, and more. With AI, it’s not just quality of the agent-customer conversation that can be improved—quality across companies will forever be impacted.

• NPS/CSAT scores can be determined, even when consumers don’t offer them. Historically, brands have to rely on consumer feedback to measure the quality of a brand interaction, but sadly, on average, less than 20% of consumers provide that feedback, and it’s commonly reserved for highly positive or highly negative scores. Now, with stunning accuracy, we can predict the scores based on historical data and sentiment analysis.

• Feedback can be shared immediately with Product Management, Finance, Legal, and more. This can do wonders for brand integrity, by being able to move time-sensitive information to many powers, quickly.

• AI enables a much more agile and responsive contact center—one where agents can continuously improve based on data rather than waiting for periodic performance reviews from their managers.

Seeing the Impact of AI on Quality Assurance (QA)

Our clients who AInsight have seen the following:

• 40% increase in CSAT
• 15% increase in NPS
• 6% increase in first-time call resolution (FCR)

Demonstrating the Value of Conversational AI to Your Stakeholders

One of the key takeaways from my talk was the importance of demonstrating the value of AI decision makers in your company who might remain hesitant in a way that resonates with them. You can’t just expect to implement AI and see the benefits overnight. You need to showcase the clear, measurable outcomes AI can deliver, first.

Start small by identifying a specific pain point in your contact center—such as high handle times, inconsistent customer feedback, or low agent morale—and pilot a conversational AI solution focused on that problem. The goal should be to generate quick wins that can be shared across your organization, and work your way up from there.

For instance, using AI to monitor 100% of interactions can lead to immediate improvements in call quality, which in turn reduces escalations and improves customer satisfaction. These measurable outcomes will help you build momentum and demonstrate the long-term value of a broader AI implementation.

A Practical Approach to Implementation

When it comes to implementing conversational AI in your contact center, a phased approach is key. Here are a few steps to ensure success:

1. Start with a Pilot Program: Choose a specific area where conversational AI can have an immediate impact. For example, deploy it in your QA process to monitor agent interactions and provide real-time feedback.
2. Engage Cross-Functional Teams: Ensure your QA, Operations, IT, and Talent teams are aligned on the goals and expectations of the AI initiative. This will help smooth the transition and foster collaboration.
3. Iterate and Improve: Use the pilot program to gather data and fine-tune your AI systems. Involve your agents in the feedback loop to ensure that the AI solution is actually helping them, rather than becoming another layer of complexity.
4. Scale Based on Results: Once you’ve demonstrated success in your pilot, gradually expand the use of conversational AI to other areas of your contact center, such as customer sentiment analysis, coaching, or even agent recruitment.

The Future of Conversational AI in Contact Centers

Looking ahead, conversational AI is poised to be more than just a tool for QA—it can transform the entire contact center operation. From workforce management to customer sentiment analysis, the possibilities are endless. But the key to unlocking AI’s full potential lies in how it’s integrated into current workflows and made a seamless part of the agent experience.

The future of contact centers is one where human agents and AI-powered systems work hand-in-hand to deliver exceptional customer experiences. Those who begin adopting and experimenting with conversational AI now will be the ones to shape that future.

As we move into this new era of AI, I encourage you to start thinking about where you can begin your own journey. The challenges of manual QA, inconsistent customer experiences, and agent burnout are solvable, and conversational AI holds the key. Now is the time to take action, pilot these technologies, and position your contact center for long-term success..