In a business landscape where customer expectations are constantly evolving, customer experience (CX) can make or break a business — which means delivering exceptional service is more crucial than ever. 

For many companies, increased focus on enhancing CX has led to a closer examination of internal processes, including quality assurance (QA). Additionally, questions are being raised about how to best streamline these processes to deliver exceptional CX while also optimizing resources.

The integration of artificial intelligence (AI) into QA processes offers a promising enhancement to ensure these experiences not only meet, but exceed, customer expectations. What is the impact of QA on CX, and how can AI augment it?

‍

Understanding the Impact of CX QA

QA processes help uncover issues like disparities between CX teams, insufficient knowledge bases, and inefficient processes — all of which significantly impact CX. 

But QA isn't just about ticking off checkboxes to determine whether or not your CX meets company standards. It's about creating a dynamic system that adapts and evolves based on actionable insights — gleaned from data, customer feedback, industry trends, and more — to inform processes and policies that will drive positive change throughout the customer lifecycle.

In other words, QA isn’t only necessary for ensuring individual day-to-day customer interactions go according to plan — it’s also necessary for your overall, long-term CX success.

‍

Enhancing CX with AI in QA Processes

Manual processes are typically less efficient, more prone to human error, and harder to scale than AI-enabled processes. Here are just a few ways in which AI can augment manual QA processes to enhance efficiency, accuracy, and scalability:

Insight generation — Most companies understand that they need to monitor CX KPIs like first contact resolution time, hold times, CSAT, escalation rate, and more. But once that data is collected, how should it be interpreted, and how should it inform potential process changes? Beyond mere analytics, AI-driven automation can identify trends and insights from a vast amount of data, highlighting specific areas for individual or department-wide improvement.

Automated analysis — Leveraging machine learning and natural language processing (NLP) to examine customer interactions provides insights across multiple platforms, ensuring the support level meets company standards and flagging problem areas. This enables companies to reduce the frequency of time-consuming, resource-intensive manual CX audits.

Predictive measures — One of AI's most significant advantages is its ability to predict issues before they escalate. From identifying abnormal complaint patterns about a particular product to pinpointing instructions that customers find confusing, AI can often raise the alarm about issues before humans notice them, with enough time to proactively to course-correct.

Considering AI’s ability to quickly generate valuable insights, assist in automating analysis processes, and flag potential issues based on real-time data, it makes business sense to implement AI-enabled QA solutions. But how reliable is AI-powered QA? Can AI effectively QA its own workflows? 

‍

Blending Technology and Human Insight

Human in the Loop (HITL) is the idea that, despite recent advances in AI, humans still need to maintain oversight over machine learning processes. 

It’s a concept we firmly believe in at SupportNinja — humans and AI are better together, and that applies to performing QA. 

Implementing a HITL approach to AI-enabled QA can help strike the right balance between humans and AI. Here’s what that might look like:

• Initially, AI supplements human-led QA processes, taking on straightforward, repetitive tasks.
• Over time, the AI system gets smarter by learning from corrections and refinements made by humans.
• Human intervention is gradually reduced as more and more processes are automated. Humans still intervene when necessary (to prevent a chatbot from going rogue, for example), and are still in charge of more complex tasks.

This adaptive, iterative approach — combining the strengths of AI technology with human intelligence — ensures the healthy balance between efficiency, accuracy, and empathy that customers expect when they reach out for support.

‍

Striving for Perfection

Can QA for CX interactions truly be flawless? While perfection may be the ultimate goal, the unpredictable nature of human behavior (which sometimes extends to the AI tools that humans develop and deploy) means errors are inevitable. However, by working together, AI and humans can boost one another’s accuracy and efficiency, leading to better — and continuously improving — CX outcomes for companies and their customers.