How to avoid auto-QA becoming a tick-box exercise

Quality Assurance (QA) has become a critical element for modern customer service and sales teams. Auto-QA tools have gained significant traction due to their ability to efficiently assess performance and help organizations scale. However, there’s a risk that auto-QA could turn into nothing more than a box-ticking exercise, speeding through scorecard completion without any real impact on performance improvement. 

Accelerating the completion of these scorecards may feel like progress, but it’s essentially useless if there’s no actionable insight or improvement plan attached. To make the most out of auto-QA, it’s essential to move beyond the “tick box” approach and instead focus on the value that insights can provide and how they can be translated into concrete actions. This is where the power of Generative AI comes in – transforming scoring into a meaningful, actionable process. 

Here’s how you can avoid turning auto-QA into just a mundane task and ensure it becomes a powerful tool for driving improvements and empowering teams. 

1. Generative AI-based scoring: transparency is key 

Many traditional auto-QA systems simply assign scores based on set criteria without any meaningful explanation of how the score was derived. This creates a “black box” scenario where the reasoning behind the score is opaque, leaving teams in the dark about what went wrong or how to improve. 

It’s important for any technology solution to provide not only the score but also the reasoning behind it and explain why certain outcomes were generated. For example, if an agent’s performance is scored poorly, the AI should provide insights like “communication tone lacked empathy” or “missed a critical customer need.” Simply failing an agent for not displaying empathy, without being able to explain why and how they can improve, does not drive performance improvements or improve the customer experience. 

This kind of transparency helps businesses understand the root causes behind the scores, making the results much more actionable. It ensures that agents receive the specific feedback they need to improve, such as advice on how to adjust their tone or approach in future interactions. When feedback is tied to actionable insights, agents are equipped to make real improvements, leading to better performance, more engaged agents and, ultimately, an enhanced customer experience. 

2. Make insights readily accessible 

The true value of auto-QA lies in its ability to give you insights that lead to improvements. However, if these insights are hidden away or locked behind complex systems, they will lose their impact. Insights should be readily accessible to all relevant stakeholders: executives, managers, team leaders, and agents themselves. 

A technology solution should provide an easy-to-navigate dashboard where insights are clearly presented and can be accessed instantly. This allows team leaders to quickly assess trends, spot issues, and implement changes before they become larger problems. The insights should also be broken down into digestible, easy-to-understand formats, such as trends over time, problem areas by category, and improvement opportunities. 

Moreover, the ability to seamlessly share these insights with your existing platforms, such as CRM, CCaaS, and BI tools, is essential. By integrating with other key systems used across the business, insights can be shared across teams, ensuring that all relevant departments have the information they need to drive improvements. Oftentimes, the issues identified in QA may not be created in the contact center, they might span across customer service, sales, product, or even marketing. By sharing insights across platforms, you enable a collaborative approach to problem-solving, where multiple teams can address issues holistically and improve overall business performance. 

The ability to integrate with other systems not only enhances the accessibility of insights but also creates a more cohesive workflow across departments. It ensures that when problems are identified, the right teams can take action, whether it’s training agents, tweaking sales processes, improving product features, or addressing gaps in customer support. 

3. Empowering teams with improvement workflows 

It’s not enough to simply provide insights; actionable steps must follow. To truly drive change, auto-QA systems must come with built-in workflows that allow team leaders to easily transform insights into tangible actions. 

For example, if an agent’s performance review identifies a lack of effective communication with customers, a workflow should immediately suggest relevant training resources, offer access to coaching materials, and prompt the scheduling of a one-on-one meeting with a supervisor. The AI should not only score performance but should trigger a series of automated or semi-automated actions designed to support improvement. 

Seamless integration with platforms like Assembled can be a game-changer in this process. Assembled is known for optimizing workforce management, including forecasting, scheduling, and analytics. By integrating your auto-QA insights with Assembled, you can streamline the process of assigning tasks, scheduling training sessions, and even adjusting agent workloads based on performance feedback. This ensures that improvement workflows are not only based on real-time data but also aligned with operational needs, helping team leaders act quickly and efficiently. 

By equipping team leaders with the right tools to take immediate, meaningful action, you ensure that the feedback process is continuous and actionable. It’s not just about catching mistakes but providing the right kind of support that can turn those mistakes into learning opportunities. When workflows are integrated seamlessly into the systems already used by teams, they become part of the natural flow of work – accelerating improvement and fostering a culture of continuous development. 

4. Personalized coaching and training programs 

Every individual is different, and cookie-cutter training programs rarely work. This is where generative AI truly shines – it can help tailor coaching and training based on the specific needs of each agent. 

For instance, if the AI detects that an agent struggles with tone or customer empathy, it can suggest personalized coaching focused on emotional intelligence. If an agent struggles with product knowledge, it can recommend additional resources or courses specifically designed to improve that skill. Personalization ensures that coaching is more efficient and more effective in addressing individual performance gaps. 

By empowering agents to take ownership of their learning and providing them with the tools to improve in specific areas, you not only help them grow but also increase their engagement with the process. When agents feel that the feedback and training they receive are directly relevant to their performance, they are more likely to embrace it. 

5. The importance of customizing your scorecards 

While standardized scorecards may seem like an easy solution, they can often miss the mark when it comes to reflecting the unique needs and goals of your organization. This is where the ability to customize scorecards becomes crucial. Every business has different performance expectations, and your auto-QA system should be flexible enough to tailor scorecards to align with those specific requirements. 

Customizing scorecards allows businesses to prioritize the key areas that matter most to them. For instance, a customer service team may place a higher emphasis on empathy and resolution time, while a sales team might focus more on closing techniques and product knowledge. By customizing the scorecards, you ensure that the evaluation process is aligned with your team’s specific goals, making the feedback more relevant and actionable. 

Moreover, customization enables teams to adapt to changing business needs or new objectives. For example, if your organization shifts its strategy or introduces a new product line, you can quickly modify the scorecard to reflect the new focus areas. This level of flexibility ensures that your auto-QA system stays relevant over time and continues to drive improvement in the right areas. 

Additionally, customizing scorecards allows businesses to incorporate various metrics that go beyond the typical quantitative scores, such as qualitative feedback, contextual factors, or Voice of the Customer insights. By including a broader range of criteria, you gain a more holistic view of team performance, which provides a more accurate basis for actionable insights and improvement efforts. 

6. Avoiding the “black box” approach 

The most important takeaway is the need to avoid the “black box” approach that many AI-led tools with opaque scores and rigid workflows suffer from. When AI operates like a closed system with no visibility into how decisions are made, the result is confusion and disengagement. Transparency is critical to ensure that auto-QA can be trusted and leveraged to its fullest potential. 

By opting for a solution that provides clear, understandable reasoning behind its scoring, businesses can empower team leaders and agents alike. This transparency encourages trust in the system, ensuring that teams take the feedback seriously and are motivated to act on it. With the ability to clearly understand how scores are derived and why specific outcomes are reached, businesses can create a more effective feedback loop – one where both agents and team leaders are equipped to make improvements that lead to tangible, measurable results. 

Avoiding opaque AI systems also means moving away from rigid workflows. Businesses need to ensure that workflows are flexible, personalized, and aligned with the unique needs of each agent and department. By allowing these workflows to be adaptable, businesses foster an environment where feedback can be quickly actioned, driving continuous improvement and a more responsive, engaged team. 

Final thoughts 

Auto-QA doesn’t have to be a mere checkbox exercise that fails to provide any real benefit. With the right approach, businesses can transform the feedback process into one that drives improvement. Instead of rushing through scorecard completion, businesses should focus on generating insights, creating actionable workflows, and offering personalized coaching opportunities. 

Empowering your teams with the right tools and insights ensures that auto-QA becomes a powerful, continuous cycle of improvement, not just an afterthought. After all, the true value of auto-QA is not in how fast you complete scorecards, but in how you use the insights to improve performance and elevate your team’s capabilities.