Best Practices for Automated Conversational AI Testing in 2024


For some time now, contact centers have felt the pressure building to adopt or upgrade their existing conversational AI solutions. In 2023, the dam finally broke. After ChatGPT went mainstream, it quickly became clear that conversational AI would forever change the way brands interact with their customers.

Disgruntled man looking at giant confused robot

As a result, budgets for the technology are surging. Gartner forecasted a 16.2% increase in conversational AI spending in 2023, followed by another 24% increase in 2024. Although AI only handled about 3% of customer interactions in 2023, it’s expected to manage nearly a quarter of them by 2027.

The appeal of conversational AI for customer experience (CX) is undeniable. Brands have already employed it to deliver more personalized support, provide 24/7 service, and even make more proactive recommendations to customers. Yet, these outcomes are not automatic results of deploying conversational AI — they assume that your chatbots and conversational IVR are used effectively and trained properly.

To ensure that’s the case, your contact center needs a comprehensive testing program for these tools. Below, we’ll explore four best practices in conversational AI testing to help you adopt this technology with confidence.

1. Understand the Challenges of Conversational AI Testing

Before you make a testing plan, you must understand the scope of the task at hand. To that end, it’s critical to realize that conversational AI testing is in an entirely different league from basic IVR testing.

In the past, customer queries and potential IVR pathways were limited to a relatively small set of options. With preset prompts and response options, there were only so many ways the customer interaction could go. Now, with chatbots and conversational IVR systems, the possibilities are virtually limitless. AI is not only built to offer dynamic responses to customer queries but also to learn and develop new ways of responding. This creates not hundreds, but hundreds of thousands of potential twists and turns in the customer experience (CX), leading to what we call the “impossible manual task” of conversational AI testing.

Today’s contact center CX must account for these near-limitless possible pathways, and that requires a testing strategy that’s expansive enough to handle the scope. Automation is essential, as this is far beyond the capacity of a manual testing team. So, too, is an AI chatbot testing solution that can account for the many nuances and quirks of human conversation.

That’s only the tip of the iceberg. Chatbots introduce new security concerns around handling customer data. These conversational AI systems are also typically based in the cloud and designed to handle a wide range of service channels. The shift toward self-service is only increasing businesses’ reliance on them. Bots must be built to handle an ever-growing volume and complexity of queries, and that requires high-load stress testing beyond the scale of what many contact centers have done before.

2. Test with an Eye toward Training

If the scope of the conversational AI testing task weren’t enough to make it seem impossible, without a comprehensive testing solution, its sheer complexity would certainly put success out of reach. That’s because AI chatbot testing isn’t merely about testing, finding problems, and fixing them. Bots need more than a simple code correction — they must be continuously trained to provide better CX in the future.

What makes this especially complex is the fact that conversational AI deals not simply with customer queries at face value but with the intent beneath them. And teaching a bot to understand human intent like a … well, human, and respond appropriately — that’s the central challenge of training conversational AI.

To achieve that task, testers and developers must have access to a wide range of data about bot behavior. Response time is foundational, but it’s much more than that. Are its responses accurate and reflective of historic customer data? Can it recover quickly after making an error? How does its personality land with the user? How does it interpret natural human errors like spelling errors, or incomplete questions? Can it make the leap across channels to deliver the same seamless, high-quality CX? How does it handle different languages?

Answering these questions with thorough AI chatbot testing is essential to creating a conversational-AI-driven CX that lives up to the hype. This is how you train your bots to better contain calls — and effectively hand off the ones they can’t handle. Businesses that emphasize this kind of training see big results, too. Walmart, for instance, reported a 38% jump in customer satisfaction scores when it focused on training bots in Chile to better handle local idioms and phrases.

3. Expand Your AI Chatbot Test Coverage

Automation is a non-negotiable for testing of this scale and complexity, but automation alone isn’t enough. The testing process itself requires a foundational shift that will unlock greater efficiency, accuracy, and speed.

For many contact centers, this means learning to “shift left” with their testing. Instead of reserving conversational AI testing for the end of the development cycle, when discovering a problem leads to time-consuming and expensive backtracking, integrate that testing throughout the entire development lifecycle. This allows you to uncover issues and course correct earlier in the process, preventing small errors from snowballing into major CX problems at release time.

It also means taking a more expansive approach to chatbot and conversational AI testing — one that encompasses everything from NLP testing and conversational flow testing to security testing and performance monitoring. Testing should target a much broader range of potential use cases so you can effectively train bots and prevent problems.

By combining an expansive, integrated testing mentality with automation, contact centers can have much more confidence in their releases when it’s go time for a release.

4. Follow a Detailed Process

Finally, successful conversational AI testing also requires a commitment to a detailed, systematic process. To achieve consistent results, you must employ the same steps every time. We can break this down into seven essentials:

  1. Create your testing data based on current information you have around customer intent, queries, bot responses, etc.
  2. Test all possible target use cases that the bot is specifically designed to handle.
  3. Test non-target use cases, or potential curveballs that may take the AI in different directions. The more tests you can build to anticipate these curveballs, the better.
  4. Test chatbots in light of omnichannel customer journeys to see how they handle switching and whether they function properly in different channels.
  5. Test how chatbots manage escalations to live agents to see if they make the handoff at the appropriate time and provide sufficient information to the agent.
  6. Test chatbot performance under peak load conditions to simulate the busiest times of customer activity.
  7. Monitor the chatbot in production to catch issues in real time.

Ultimately, the shift-left mentality isn’t possible without these steps. This process allows you to effectively integrate testing throughout the development cycle and ensure your bots are receiving essential testing throughout their development and in the customer journey.

Automate Conversational AI Testing with Cyara

Whether your contact center has already embraced conversational AI or you’re eager to catch up, you shouldn’t deploy or upgrade your chatbots without first bolstering them with in-depth testing. It may sound like a significant undertaking, but it will make your foray into chatbot and conversational IVR technology much more profitable in the long run.

More importantly, it need not be a daunting challenge. With the right conversational AI testing solutions, you can automate the process, save time, and build smarter bots.

Cyara Botium is the industry standard for testing and optimizing your conversational AI tools. Our end-to-end chatbot testing and monitoring ensures your chatbots perform up to standard throughout every phase of their lifecycle. You can automate every aspect of AI chatbot testing, from regression and performance testing to creating test and training data. Botium tests your bots’ natural language processing capabilities, multilingual translation skills, accent and dialect comprehension, data privacy compliance, and so much more.

AI is pushing CX to an entirely new level, and it’s happening fast. The question isn’t whether your contact center should embrace it, but how you should ensure it’s done well. With Botium, you have everything you need to scale up your AI-driven CX, integrate automated chatbot testing into development, and train your bots to provide better, smarter service. Learn more about what Botium can do for your contact center.