Blog Home

AllTop Stories
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
AI & Technology in CX

CX Strategy: The Future of AI in Quality Assurance

March 12, 2023
0 minute read

Customer Support is no longer just a cost center - it's been transformed into a powerful experience center where support agents act as brand ambassadors and drivers of customer retention. But the rise of Artificial Intelligence (AI) is changing the CX landscape yet again.

In this blog, we'll dive into how machine-learning models are impacting Quality Assurance and the role of support agents. We'll also explore whether AI is empowering agents or stifling their creativity and discuss the way AI is affecting CX and the part that support agents play in the customer experience journey. With this in mind, we'll share deep insights from a recent CEO/CX webinar featuring MaestroQA CEO Vasu Prathipati.

This will be a good one, so let’s get started.

Why the Push toward AI in a Quality Program

As Quality Assurance Managers continue to be tasked with finding ways to elevate customer service, the question often arises: How can AI help impact quality assurance? 

“When we ask customers why they are interested in using AI for their quality program, three responses are typically given,” shared Prathipati:

1. We're only sampling 2% of our interactions – AI can help us to do 100% sampling, providing us with more insight. 

2. AI can be used to do sentiment analysis on 100% of interactions, making sentiment analysis more efficient and thorough.

3. Manual QA is expensive and filled with human errors, so AI can be the perfect solution to reduce costs and increase accuracy.

Prathipati suggests taking a more discerning approach when evaluating the potential impacts of AI on QA to help ensure better decision-making. By examining each of the initial reactions mentioned above more thoughtfully, organizations can better understand the implications of their AI-related investments and make informed decisions that align with their long-term goals. 

So, let’s start with the first one.

Actionable Insights vs. UnActionable: Which is More Important

“We're only sampling 2% of our interactions – AI can help us to do 100% sampling, providing us with more insight.”

Of course, 100% sounds better because it means more insights. 

“But let’s challenge that assumption,” said Prathipati. “Would you rather have actionable insight based on 2% of interactions or unactionable insight based on 100% of tickets?” 

The answer for many at this point might be to focus on the 2%, “but what happens when the 2% and the 100% are both actionable, but in different ways?” asked Prathipati. “What if the 2% of actionable insight results in more cost-savings than the 100% of actionable insight?” 

One would focus on the 2%, but is that the right choice? “All of a sudden, what matters more than 2% vs.100%, is the word actionable. We need to start spending more time thinking about what is actionable, whether it's through manual or AI-powered insight,” Prathipati pointed out.

This leads us to a discussion on:

AI and Sentiment Analysis: Hype vs. Reality

“AI can be used to do sentiment analysis on 100% of interactions, making sentiment analysis more efficient and thorough.”

In today’s fast-paced environment, the capabilities of AI in QA have high expectations. “We hear promises, said Prathipati, “that this technology can do the whole end-to-end process from detection to root cause analysis. That’s why when speaking with our customers, we ask a series of questions to get a more balanced point of view on what is a right fit and what are reasonable expectations: innovative but reasonable.”

Shifting back to our discussion on using AI to do sentiment analysis on 100% of interactions, Prathipati said, “we want actionable insights. If sentiment is low, how will you identify the ‘why’ it’s low to determine how to take action to improve it? The same rules in our current world still apply in this world of using AI in quality programs. What we hear from our customers is that CSAT is low, so what do we do? We build a DSAT process where we start to look at all the negative CSAT scores. With scorecards, we can do root cause analysis to determine if it’s a people issue, process issue, technical issue, or some other type of issue.”

When it comes to using AI, definitions matter too! Prathipati said, “how we want to define Sentiment. This is the key to actionability. For example, how do we account for customers coming in upset? How are we looking at sentiment where they're already upset? Or are we looking at customers that get upset later in the conversation?” 

Determining if it’s agent sentiment or customer sentiment leads to asking yourself: which one of these is even that actionable? If AI is just for Sentiment, will you be able to generate 4X the cost-savings or revenue gain to justify that to your CFO?

“We’ve heard from customers that dig into it, ones that have been burned in the past, that it’s already hard to measure a correlation between CSAT and retention or CSAT and churn or CSAT and growth.” This means that “first we might have to do the hard work to determine do we believe sentiment. Do we have data to show internally that sentiment and churn are really correlated before we can go and invest in AI?”

Critical thinking is needed, recommended Prathipati, so that a company doesn’t end up with something that “sounds good on the surface, but when you get into the details, it doesn’t fulfill your goals or promises.”

Let’s move to the last reason why some push for AI in quality programs.

Manual QA vs. AI: Is AI the Silver Bullet?

“Manual QA is expensive and filled with human errors, so AI can be the perfect solution to reduce costs and increase accuracy.”

One of the last common reasons for the shift to AI is the manual and expensive process of QA, which some feel can be filled with human bias. Hence, the perception that AI should be the perfect solution.

Today, “some feel that AI is the silver bullet, and they have this great vision for it, but pairing that vision with reality hasn't been fully thought through, “ shared Prathipati. “Some of the questions that we ask our customers after reviewing their current scorecards revolve around the sections of those scorecards; we want to know which are the hard skills and soft skills. 99% of our customers have hard and soft skills in their scorecards.” 

"Whether they are looking at other QA software vendors or us, we ask: what are vendors showing you auto-scoring around, and what will you do about the parts they aren’t covering? Most typically, we see use cases that are soft skills based." What’s interesting in this finding, as Prathipati pointed out, is that troubleshooting or probing questions come from the hard skills section, but it’s this section where you can save time and impact metrics like first call resolution or reduce total time to resolution." That’s why it’s important to consider the coverage when considering AI for this. "There’s a role for AI, but it’s not the silver bullet for everything." The goal, said Prathipati, is to think about "how they should be coupled together" so you can "really create a dynamic and exciting quality program."

What about using AI for auto-grading?

“So many of our customers use quality programs where they're not only reviewing the conversation in a Zendesk or Salesforce in their case management system or listening to the call. There are a lot of back-end systems, so they must check to see if the case or issue was resolved properly. This requires going into your internal tooling. And that's a whole different beast,” said Prathipati, “How do you make sure you're accounting for that type of deep work when you're thinking about auto-grading? From our research, that's a whole different set of work. And so when we start to unpack this with customers, we can help our customers with this amazing vision that we still want to hold on to, but bringing it down so that we get the best of both worlds: the great work we're doing as people, paired with the great work of technology.”

AI In Quality Assurance: The Wrap-Up

In the end, Prathipati said, “we’ve talked about three common perceptions of AI and why people are thinking about AI in their quality program, but when you start to pull back the layers, we try to take the conversation to the next level, there’s a lot of hype for AI, but it’s really important not to look at your quality program just as a cost, but to look at the current value that it’s delivering. So much so, “that when you invest in a technology such as AI, is it clearing that bar? It needs to be delivering more value than your current quality program. So it’s a one plus one equals three situation.”

If you would like to learn more about MaestroQA, request a demo today.

Previous Article

Mastering Customer Interactions in the Age of DSAT

The Essential Guide to Chatbot Quality Assurance: Ensuring Excellence in Every Interaction

Navigating AI Implementation Strategy in Customer Experience: Risks and Strategies

Elevating Call Center Performance with Six Sigma and MaestroQA

Elevating Business Excellence Through Non-Customer-Facing QA: A Strategic Imperative

Elevating Trust and Safety through QA: How TaskRabbit Sets the Standard

Unlocking Superior CX: The Bombas Blueprint for Quality and Coaching

Unleashing the Power of Customer Conversations: Top 6 Tech Trends Revealed at the CX Summit

Important Factors to Consider when Exploring Sentiment Analysis in Customer Support QA: A CX Community Discussion

Agent Empowerment: 5 Tactics for Customer Retention from Industry Leaders

Mastering Agent Onboarding: Quality Assurance Lessons from ClassPass

Driving Business Impact with Targeted QA: Insights from an Expert

The Transformation of QA: Driving Business Results - Key Takeaways from MaestroQA’s CX Summit

How Angi Unlocked Growth and Continuous Improvement with QA

De-Villainizing QA Scorecards with Hims & Hers Customer Service

How to Revamp QA Scorecards for Enhanced Quality Assurance

The Art of Outsourcing Customer Support: Lessons from Stitch Fix's BPO Partnership

Writing the Auto QA Playbook & Transforming Customer Support

Advancing Customer Service Metrics with AI Classifiers

MaestroQA Named One of Comparably’s 2023 Best Workplaces in New York for the Second Consecutive Year

How to Maximize Call Center & BPO Performance | MaestroQA

MaestroQA Named on Comparably’s Best Workplaces in New York

CX Strategy: The Future of AI in Quality Assurance

Elevating Customer Satisfaction with Visibility & Coaching

Champion-Challenger Model: Improve Customer Service In BPOs

5 Key Strategies to Supercharge Your BPO Partnership

How Customers Collaborate with Their BPO Partners Today

Kick Start Your Customer Service BPO Partnership Successfully

BPO Call Centers: Best Practices for Quality Assurance

Empathy in Customer Service: Everything You Need to Know

Call Calibration: What is It & What are the Benefits?

Increase QA Team Alignment with Call Calibration & GraderQA

Measuring An Organization's 3 Ps: People, Process and Product

How to Onboard Your Customer Service Team to a New QA Program

Average Handle Time (AHT): How to Calculate & Reduce It

Should You Have Dedicated Quality Assurance Specialists?

The Top 4 CX Books Recommended by Our QA Community

How Top eCommerce Brands Ensure Exceptional Customer Service in a Remote World

21 Key Customer Experience Definitions for QA Professionals

5 Key Components of a Remarkable Customer Service Experience

Customer Service Management 101: Everything You Need to Know

A Guide to Customer Service Quality Assurance Programs

The Ultimate Guide to Improving First Call Resolution (FCR)

The Key to Customer Service Coaching Is More Data (and Fewer Opinions)

How to Refresh Your Call Center Quality Monitoring Scorecard

How to Update Your QA Scorecard

The 9 Customer Service KPIs Needed To Improve CX

3 Ways to Test Your Call Center Quality Assurance Scorecard

Leveraging Customer Sentiment to Improve CX in Call Centers

This Is What an Effective Customer Service Coaching Session Looks Like

What is DSAT and 5 Steps to Improve It

Customer Experience Management and Quality Assurance Jobs

How Deeper CX Analytics Lead to Better CSAT | MaestroQA

Achieving Effortless Customer Experiences (CX) with QA

How to Create an Omnichannel Call Center Quality Assurance Scorecard

Beyond Low CSAT Scores: Finding the Root Cause of Poor CX

Customer Service Coaching 101: Improve Agent Performance

Build the Ultimate QA Scorecard Process for Email and Chat

MaestroQA's Aircall Integration: Bring Your Calls to Life

Call Center Quality Assurance with Zola and Peloton

Auto-Fail in Call Center QA: What It Means and When to Use It

Why Poor Agent Experiences Happen (and How to Fix Yours)

20 Call Center Coaching Tips to Boost Agent Performance

Why Getting Buy-in for Quality Assurance is Essential

Setting Up a Grading Cadence for Your QA Scorecard

How to Avoid Bad Customer Service as you Scale your Business

Building a New Call Center Quality Assurance Scorecard

What CX Leaders Need to Know About Ecommerce Industry Trends

Call Center Cost Per Call: How to Calculate & Reduce It

6 Tips to Automate Your Customer Service Management Process

Quality Assurance and Training with Seismic Learning & MaestroQA

11 Customer Service Training Ideas and Skills for Your Agents

The Past, Present, and Future of Quality Assurance

고객센터 품질관리를 위한 QA전문가가 꼭 필요할까요?

Improve CSAT Scores: Understanding Your Experience Blindspot

What CX Leaders Need to Know About Security and Compliance

Streamline Your Call Center's QA Program With 4 Key Features

How Customer Experience Teams Can Impact a Company's Brand

Customer Loyalty vs. Customer Retention: Which Matters More?

Empathy & Authenticity: Customer Service Skills to Improve CX

How High-Performing CX Teams Build Accountability

Five Questions to Jumpstart your QA Scorecard Research Process

How to Improve Call Center Agent Performance: 6 Key Tips

3 Ways to Improve Your CSAT Score through Quality Assurance

3 Strategies on How to Increase Customer Loyalty

A Guide to Net Promotor Score (NPS) for Customer Service

Reasons for Call Center Attrition Rate and How to Reduce It

Voice of the Customer (VOC): A Guide for Great CX Teams

What’s Really Behind Your CSAT Scores? Diving Deeper

How CX University Improves Brooklinen’s Agents Performance

Understanding Customer Effort Score (CES) & How to Measure It

Improving the Customer Experience with DSAT Scores

5 Tips for Customer Service Coaches in Call Centers

Your Most Important CX Metric Is Your QA Score - Here's Why

How Agents Can Make the Most of Customer Service Coaching

How to Grade Customer Service Calls

Why Top-Performing CX Teams Focus on Workforce Engagement

Improve Customer Satisfaction with a CX Quality Management Program

Customer Service Training and Quality Assurance – How Lessonly and MaestroQA Close the Loop

How to Calculate CX Quality Assurance Scores