There is a polarizing debate going on in our country right now – and yes – it’s about customer service quality assurance. Customer service professionals have a choice between two distinct and opposing approaches to how they handle QA. On one side are “highly-configurable QA frameworks”, which allow precise customization of every aspect of the QA process, but are limited when it comes to automation, scale, and process efficiency. At the opposite end, “no-human-in-the-loop AI solutions”, which provide full automation and high scalability, but are questionable in terms of quality. This article will attempt to lay out the advantages and disadvantages of each approach and provide you with a clear understanding of which fits your organization’s priorities.
Aside: We’ll also provide an alternative option if you don’t see yourself on either extreme of these options. To be fair, manual spreadsheets and Google forms are also heavily used, but if you’re reading this you’re probably a) already painfully familiar with those tools and b) looking for something better.
A helpful analogy to frame quality assurance approaches
An easy way to think about this market is how you could take a roadtrip today. One approach is to have a map and your own two hands guiding you down the road. You have complete control over every aspect of your journey, but with no GPS, you may not be driving the most optimal route. There may be traffic ahead and roads may be closed, but you won’t realize that until you’re driving right up to it.
On the other end of the spectrum is fully autonomous driving. You enter your destination, sit back, and allegedly keep your hands on the steering wheel but really just focus on something else until you are chauffeured to your destination. You don’t have control over the exact route and must have complete trust that you won’t end up a) somewhere different than your desired destination (e.g. Ontario, California vs Ontario, Canada) and b) somewhere you definitely don’t want to be (e.g. in a lake). You may take a different route than if you were in control, and you may miss out on points of interest along the way, but if the goal is getting from point A to point B, this satisfies that.
With those two options in mind, let’s get into the details of how each serves customer experience quality assurance.
Highly-Configurable Quality Assurance Frameworks
Let’s define terms so it’s clear what exactly fits in this category. These solutions are essentially a digital continuation of the original spreadsheets used for QA. They provide a framework for collecting and conducting quality assurance assessments, agent evaluations, and more. Some of them also include other areas like workforce management. As the name indicates, they, like their spreadsheet predecessors, are highly configurable and customized to each individual organization. While this approach is manual and requires more time for setup, it does have a few advantages:
- Clarity and precision: A more manually executed quality assurance program provides a clear look into any decisions or scoring mechanisms since they are being done by analysts. Each step is defined, and there’s a tangible understanding of the evaluation process. This transparency contributes to a sense of reliability and trust among team members.
- Customization and flexibility: Manual processes allow for a high degree of customization. You can tailor your quality assurance criteria to your specific business or organizational needs. This flexibility ensures that the program aligns exactly to the unique aspects of your customer service operations and what you believe your quality assurance program should look like.
- Human Touch: This more manual approach retains the human touch in evaluation. Human evaluators can discern nuances, understand context, and empathize with customer interactions in a way that AI might struggle. This emotional intelligence can be crucial in customer-centric industries.
This of course is the good old fashioned way of driving in our analogy. May not be the most direct route, and you won’t get there the fastest way, but you’ll get there the way you want.
Where AI automation has an advantage in quality assurance
As with any decision, there are tradeoffs. In this case, the more bespoke you need your quality assurance program, the harder it is to automate. This gives AI-powered solutions a few distinct advantages:
- Efficiency and speed: AI-powered quality assurance programs excel in efficiency and speed. Automated systems can analyze vast amounts of data in a fraction of the time it would take humans. This swift processing contributes to quick issue resolution and improved response times.
- Scalability: AI solutions can assess all conversations giving you a clear sense of which conversations should be reviewed. This transparency is limited in the more manual framework approach, which typically selects a randomized 2-3% of all conversations. By analyzing all conversations, AI can choose the ones that are most impactful or representative of agent performance.
- Consistency: AI ensures a consistent approach to quality assurance. Algorithms apply the same criteria without subjectivity or bias, eliminating the variability that may arise in manual evaluations. This consistency is particularly valuable for large-scale operations.
- Data-driven insights: Automated systems generate extensive data insights. AI can identify patterns, trends, and areas for improvement with precision. These data-driven insights provide valuable information for strategic decision-making.
Using our same analogy, this fully autonomous approach is pretty hands off, freeing you up to focus on other things and eventually removing the need for a driver.
Where depending solely on AI for quality assurance falls short
As you expected, AI without a human-in-the-loop can have some drawbacks. These include:
- Lack of understanding or context: One of the primary drawbacks of AI-driven quality assurance is the potential lack of understanding. Team members might find it challenging to comprehend the inner workings of complex algorithms, leading to a disconnect between the system and its users.
- Prone to mistakes: While AI solutions are powerful, they are not infallible. Automated systems can make mistakes, especially in interpreting nuanced human interactions. Errors in evaluation might occur, potentially leading to inaccurate assessments and lowered customer satisfaction.
- Limited adaptability: AI systems may struggle with adaptability in dynamic situations. Human evaluators can adjust their approach based on context, but AI might falter in scenarios that deviate from pre-defined parameters.
Using our same analogy, this fully autonomous approach is also highly risky and can be difficult to understand why certain, sometimes obvious, errors were made that a human would easily avoid. However, even if there is no catastrophic failure, the human passengers may miss out on points of interest along the route, or the best roadside taco stand in three states.
Examining an alternative approach to customer service quality assurance
At this point, you may be losing hope that either quality assurance approach is worth investing in. But, like an M. Night Shyamalan movie, there’s a twist. There is a third alternative.
Instead of having to choose either manual or AI, the main idea with this approach is to balance AI and human as complementary capabilities, using AI for certain tasks it can complete quickly, at scale, and with confidence, and using human expertise for other tasks that may require further investigation or interpretation. Let’s call this third option “Human x Machine”.
How Human x Machine strikes the right balance in quality assurance
Leveraging the strengths of each method to create a hybrid system combines the human touch with the efficiency of technology. This has some distinct advantages:
- All the same benefits of AI: This approach brings the efficiency, speed, scalability, consistency, and data-driven insights of AI-powered quality assurance programs, since it is powered by AI.
- None of the AI disadvantages: Since the “Human x Machine” approach includes a QA analyst as part of the process, it doesn’t have the same short-comings when it comes to context, interpretation, and adaptability. It strikes a balance between AI automation for the issues that AI can decide with confidence and humans for the others.
- Faster time to value: One distinct advantage that Human x Machine has over both of the other categories is that it can be up and running fast. It does not have to be painstakingly designed from scratch, like the highly configurable frameworks. Nor does it need months to train AI models, like the no-human AI solutions. This hybrid approach uses pretrained AI models so instead of months of setup, it’s ready in weeks.
- Increasing accuracy and automation: One The hybrid approach also creates a feedback loop whereby AI can progressively learn from human actions. So in addition to an initial jump in automation from pre-train models, additional capabilities can be added with confidence
But in the interest of transparency, there are also some disadvantages, or at least tradeoffs with this approach:
- Requires humans as part of the process: This hybrid approach does require people, so if your idea is to scrap your QA team in favor of an AI solution, this approach doesn’t do that. It can increase efficiency and allow fewer QA analysts to assess significantly more agents and conversations, but it does require people.
- Not bespoke to each organization: Unlike the highly-configurable QA frameworks, “Human x Machine” pre-analyzes your calls, emails, and chats using industry-proven AI analytics – not an army of QA analysts. It even completes part of the scorecard based on conversational attributes, which may not be for everyone.
Again returning to our analogy, this is similar to a car with GPS to guide you, and perhaps some collision warning and automatic braking capabilities, but a human is in the driver’s seat. The technology is there to provide guidance and prevent unnecessary errors, but it’s mainly an enabler of your decision making skills.
Finding your customer service quality assurance approach
In the manual vs. AI-driven vs. hybrid customer service quality assurance debate, there is no one-size-fits-all solution. Nor is there a “right” or “wrong” answer. Each organization and customer service professional will have their own perspective on what is essential to their program, and what they are willing to sacrifice to get it. To simplify that decision, the table below should place you into the solution that fits your needs best.
If… | …And are willing to… | …Then… |
|---|---|---|
You need to define every single aspect of your quality assurance operation | Give up speed, efficiency, transparency, and scalability | The highly-configurable QA framework tools are for you |
You need need a completely hands-off approach to QA | Give up clarity into decision making, precision, and explainability | The no-human-in-the-loop AI solutions are for you |
You need to balance speed, scale, and quality using both AI and human analysts | Keep your human analysts and let AI do some of your job | The Human x Machine hybrid approach is right for you |
Image Credit to DALL-E
