7 Misconceptions about AI-Based Auto QM in Contact Centers

Every contact center managers has their tried and true method of evaluating calls they have developed over the years. For me, it was my trusted Excel spreadsheet I had developed to evaluate my agent's performance. So I get it if trading in the trusted spreadsheet or other familiar method you are using for an AI-based QM solution that automatically scores and evaluates agent performance seems scary. 

Here at MiaRec, we have successfully helped hundreds of organizations across the globe implement automated quality management and have seen and heard your struggles. In fact, I used to be a contact center supervisor myself, and I loved my spreadsheets, so I know exactly where you are coming from. 

In this article, I will address the seven most common misconceptions contact center managers struggle with when faced with AI-powered Auto QM. By the end of the article, you will understand the impact that AI-based Auto QM will have on your job security and day-to-day tasks, and how you can benefit from giving up your trusted spreadsheet and upgrading to an AI-based solution.

1. Auto QA Isn't Accurate Enough To Deploy At Scale

Contact center managers spend hours and hours every week listening to call recordings to evaluate their agents. Due to the sheer number of calls, they can assess only 2-5% of the calls. A Generative AI-powered Auto QA could automatically "read" the call recording transcripts and score the agent's performance based on a customizable scorecard. 

However, one common misconception is that it is still better to evaluate 2% of the call volume using humans than 100% using AI. In other words, contact center managers fear that AI-based Auto QA solutions are not accurate or reliable enough for effective quality management. This skepticism often stems from a need for more trust in AI technology and its ability to understand the complexities of human interactions accurately.

Contact center managers, for example, worry that AI tools cannot grasp the nuanced aspects of conversations, such as tone, context, and emotional undercurrents, which are crucial in assessing an agent's performance. Moreover, there's a concern that AI, without human oversight, might make erroneous judgments, leading to unfair evaluations and potentially damaging employee morale.

While these concerns are understandable, the opposite is true. By gaining insights across 100% of your calls, you will have a much more accurate picture of your agent's performance than if you randomly evaluate a few calls. You are still the human in the loop that gets to override or correct evaluation outcomes if needed! In addition, Generative and Conversational AI have evolved over the years to detect sentiment accurately and understand the context within human conversations very well. 

2. Auto QA Will Replace Human-Led Performance Evaluations

Another misconception closely related to the first is that the goal of implementing Auto QA is to completely replace the human element in the quality management process by evaluating 100% of your calls with AI and leaving it at that.

This couldn't be further from the truth. View Auto QA as your pre-scanning assistant that does all the hard legwork for you, freeing you up to look into the calls that need to be evaluated closer. In other words, Auto Call Scoring and QA should always be used in tandem with human-led manual evaluations.

But by using Auto QA, you not only know which calls need your closer attention (e.g., you see an agent's performance rapidly declining over the last month), but you will also have more time and energy to devote to the work that really moves the dial, such as providing personalized feedback and coaching to your agents. 

3. AI-Based Auto QA Is Rigid & Will Provide Generic Results

Another apprehension that contact center managers often express about AI-based Auto QA solutions is the perceived need for more customization and flexibility. This worry stems from the idea that AI, while sophisticated, might come with a "one-size-fits-all" approach and be unable to adapt to the specific needs and unique dynamics of different contact centers. 

This concern is entirely understandable. After all, each contact center has its own goals, challenges, and customer demographics, and therefore requires a tailored approach to quality assurance and performance management.

But here is the thing: Generative AI is "just" a tool that you use to make your unique process more streamlined and efficient. You customize it to whatever specific business needs you have. Generative AI provides you with more flexibility to easily customize your quality assurance evaluations using plain prompts in plain English (over dozens of other languages) depending on your goals.

For example, you can completely customize the quality assurance forms that measure specific agent behaviors that are statistically linked to reducing costs, minimizing customer churn, and increasing sales. 

4. Auto QA Is Too Complicated To Install, Onboard, And Use

This leads directly to misconception # 4: Auto QA needs to be simplified and you will need deep technical expertise or even need to hire a data scientist to install, onboard, and use it. Specifically, there is this misconception that deploying and maintaining AI solutions requires specialized IT staff, which small- or medium-sized contact centers may need to have. We have also heard concerns about integrating new AI solutions with existing IT infrastructure and software systems.

In reality, Auto QA is installed very fast and is easy to onboard and use. For example, MiaRec's cloud-based Auto QA solution takes a few hours to install and 1-2 days to configure, depending on the complexity of your requirements. Onboarding and training are intuitive, as our platform is easy to use. We have detailed the onboarding process here. Moreover, it effortlessly integrates with external applications like CRM and help-desk systems through APIs.

5. Auto QA Isn't Secure Enough (Data Privacy Concerns)

Many contact center managers are (rightfully) concerned about data security. Regarding Auto QA, they are specifically worried that the solution isn't secure enough, causing data security concerns.

They are often worried that any transcript evaluated by Generative AI might be used to train the Large Language Model (LLM) used to perform the task. However, this is different. Your usage doesn't wait to update the model. Most vendors allow you to opt out of this.

As a contact center manager, it is crucial to prioritize data privacy due to the sensitive information you handle on a daily basis. However, there is no one-size-fits-all security solution that can address all concerns.

To ensure data security, it is essential to thoroughly investigate each vendor's security and compliance measures. Here at MiaRec, as well as other reputable vendors, we have implemented robust safeguards to protect customer data and comply with the strictest security and industry compliance regulations. Your data privacy is our top priority.

6. Auto QA Is Too Expensive & Does Not Offer An ROI

The exact pricing of Auto QA solutions is murky because most vendors do not publish their pricing, and these solutions are often baked into more extensive Conversation Intelligence solutions.

But let's take MiaRec's pricing as an example. MiaRec charges $50 per month per agent for its Auto QM solution. A contact center of 100 agents cost $50,000 per year. This includes manual (software-supported) and AI-based Quality Management, Topic Analysis, Sentiment Analytics, Advanced Reporting, Speech-to-Text transcription, and more.

Let's do a quick calculation to calculate the ROI. Let's say you have ten supervisors who have to evaluate 50 calls per month, and they take 1 hour for each evaluation. You pay them an hourly rate of $30.

By deploying Auto QA, you will save the following:

• 500 total hours every month (or 1.67 work weeks/manager/month)
• $180,000 total savings by deploying Auto QA

Compared to the $50,000 investment, you can achieve a 260% ROI or $130,000 in savings year after year! And this does not count the ROI of other features such as Topic and Sentiment Analysis!

Head over to our Auto QM ROI Calculator to run your numbers.

7. Auto QA Will Take My Job

Finally, the scariest misconception of all: will AI-powered Auto QA solutions be the beginning of the end for your job? This fear is as old as AI itself. While artificial intelligence has undergone tremendous innovation and progress since Alan Turing first posed the question, "Can computers think?" in the 1950s, we are still miles away from any science fiction fantasies of robots taking over the world. 

As explained above, Auto QA is an assistant that helps you to do your job better. It frees up more of your time and eliminates tedious manual labor so you can focus on supporting and empowering your agents. However, that doesn't mean your job is safe because the adage is true: "AI won't replace you; a human using AI will."

But don't worry. Embracing AI is more manageable than it may seem. Here are some resources to get you started:

• Read this article to understand the basic definitions of Contact Center AI, such as Machine Learning, Large Language Models, etc.
• Discover the endless possibilities of using Artificial Intelligence in contact centers to enhance customer service, optimize operational efficiency, and unlock a world of other benefits. Dive into our comprehensive article on "Best Contact Center AI Use Cases" to explore the full potential of AI-driven solutions.