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Next generation chatbots are now writing poetry and giving math lessons, but these smart applications have a bigger job to do. Advanced chatbots simulate human interaction via complex artificial intelligence (AI) processes, or conversational AI. As business-ready systems, conversational AI is joining mainstream tech to deliver strategic benefits to customers and employees. For companies looking to adopt or expand their use of conversational AI, there’s quite a bit to understand and consider. 

Now that humans and machines are talking to each other, decision-makers will need clarity around the capabilities—especially as they vet various products and platforms. It helps to start by defining some key terms.

Artificial intelligence (AI): A wide-ranging category of technology that allows computers to “strive to mimic human intelligence through experience and learning.”[1] Common AI applications involve analysis of language, imagery, video, and data. Machine learning (ML): In its definition of AI, Gartner cites ML as one of AI’s notable “advanced analysis and logic-based techniques,”[2] whereby computer systems can learn from their experiences without explicit programming. Natural language processing (NLP) focuses on machine reading comprehension through grammar and context, enabling it to determine the intended meaning of a sentence.  Known for applications such as voice-to-text and language translation, NLP uses AI and often ML to enable a computer to understand spoken or written human language. Natural language generation (NLG) focuses on text generation, or the construction of text in English or other languages, by a machine and based on a given dataset.Conversational AI: This advanced application of NLP is what allows people to have a spoken or written conversation with a computer system. At their best, conversational AI systems closely match human conversation—passing a measure called the Turing test.[3] Here’s how it works from a technical perspective: During the automatic speech recognition (ASR) stage, a person may ask a question and the application converts that audio waveform to text. During the NLP phase, the question is interpreted, and the device generates a smart response. Finally, the text is converted back into audio for the user during the text-to-speech (TTS) stage. 

A Quick Rundown of How Conversational AI Works

Asking a smart phone whether it’s going to rain, telling a virtual assistant to play ’90s hip hop, requesting a navigation system give directions to a new sushi restaurant—each are examples of interacting with conversational AI. By speaking in a normal voice, a person can communicate with a device that understands, finds answers, and replies with natural-sounding speech.

Conversational AI may seem simple to the end user. But the technology behind it is intricate, involving multiple steps, a massive amount of computing power, and computations that occur in less than 300 milliseconds. When an application is presented with a question, the audio waveform is converted to text in what’s known as the automatic speech recognition stage. Using NLP, the question is interpreted and a response is generated. At the next step, called text-to-speech, the text response is converted into speech signals to generate audio. 

Why Customers and Employees Prefer Conversational AI
Most people have experienced the frustration of talking to a legacy chatbot, and perhaps even resorted to anger or shouting “Representatitive!”. But once chatbots are enhanced with conversational AI capabilities, research shows customer satisfaction rates to be three times higher, attributed to shorter wait times and more accurate, consistent customer support.[4]

For employees, conversational AI can reduce stress and boost productivity by handling most low-level tasks and easing their day-to-day human-machine interactions. This frees up staff for other valuable and higher-level functions, benefiting customers and increasing morale.

Overall, for companies, the benefits may seem obvious: more productive staff and better customer service leading to increased productivity as well as higher customer satisfaction and retention rates. An additional benefit comes from the learning and training of models that continually improve and enhance employee and customer experiences.

Conversational AI in Action, From Retail to Healthcare to Real Estate

In constant search of competitive advantage, companies are increasing their investments in AI to the tune of a projected $204 billion by 2025.[5] Across industries, the technology promises to deepen customer insights, drive employee efficiency, and accelerate innovation. 

In retail, conversational AI is giving shoppers a streamlined experience with call centers and customer service interactions. As the clunky chatbots of yore are replaced with savvy AI chatbots, customers can quickly get their questions answered, receive product recommendations, find the proper digital channel for their inquiry, or connect with a human service agent. 

In healthcare, applications for conversational AI can support telehealth patient triage to identify potential medical conditions. Systems can also be trained to securely manage patient data—making it easier to access information such as test results or immunization records. And the technology can support patients who are scheduling an appointment, checking on insurance eligibility, or looking for a provider.

In real estate, conversational AI tools are being applied to the time-sensitive lead generation process, automating functions for accuracy and efficiency. Chatbots are also handling initial conversations to assess what a customer is looking to buy or sell. Given AI’s ability to handle thousands of calls per day, a program can be integrated with the customer relationship management system, or CRM, to create more positive experiences.

Five Questions to Ask Before Deploying a Conversational AI System

Once a company is ready to explore a conversational AI project, there will be groundwork. Here are five essential questions—and clues to finding the answers.

What kind of hardware do you need? The answer depends on the application scope and throughput needs. Some implementations rely on ML tools and run best on high-performance computing. Others may be more limited in scope. In any case, Dell Technologies Validated Designs offer tested and proven configurations to fit needs based on specific use cases.Which user interface options will your project support? Whether it’s a text-only chatbot or the more user-friendly voice interface, the decision must be based on what’s best for the customer and the budget.What platforms will be supported? Determine how customers might access the chatbot—via mobile app, web, social media—and think about whether to integrate with popular voice assistants. Will you build your own or rely on a vendor? Doing it in-house requires expertise and time but offers more control. If selecting a vendor, consider whether one vendor or multiple vendors will be needed for the end-to-end system. What kind of infrastructure will you need? This depends on whether the implementation will be hosted in a private or public cloud service. For those hosting in their own data centers, likely for compliance or security reasons, be sure the vendor’s systems are designed specifically to meet the speed and performance for conversational AI. 

As consumers become more familiar with AI, using it to create art and pay bills and plan their workouts, the technology holds greater professional promise. Conversational AI is already supporting a number of essential business functions—a boon for customers, staff, and the bottom line. Executives can set the foundation for their own advanced chatbots and other applications by ensuring their IT systems are ready for innovation. 

Read the Guide to Conversational AI for Financial Services, and explore AI solutions from Dell Technologies and Intel. 

IT Leadership

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Conversational AI is changing the way we do business.

In 2018, IBM boldly declared that chatbots could now handle 80% of routine customer inquiries. That report even forecasted that bots would have a 90% success rate in their interactions by 2022.[1] As we survey the landscape of businesses using conversational AI, it appears to be playing out that way.

Not many customers are thrilled with these developments, however. According to recent research by UJET, 80% of customers who interacted with bots reported that it increased their frustration levels. Seventy-two percent even called it a “waste of time.”[2]

While it’s true that chatbots and conversational IVR systems have made significant strides in their ability to deliver quality service, they still come with serious limitations. Most notably, they tend to take on the biases of their human designers — sometimes even amplifying them. If contact center leaders want to rely heavily on this technology, they can’t ignore this issue.

What is chatbot and conversational AI bias?

At first glance, the idea of a computer holding biases may seem paradoxical. It’s a machine, you might say, so how can it have an attitude or disposition for or against something?

Remember, though, that artificial intelligence is created by humans. As such, its programming reflects its human creators — including any of their biases. In many cases, those biases may even be amplified because they become deeply encoded in the AI.

There have been a few extreme (and well-known) examples of this. Microsoft’s chatbot, Tay, was shut down after only 24 hours when it started tweeting hateful, racist comments. Facebook’s chatbot, Blender, similarly learned vulgar and offensive language from Reddit data.

As disturbing and important as those extreme examples are, they overshadow the more pervasive and persistent problem of chatbot bias. For instance, the natural language processing (NLP) engine that drives conversational AI often does quite poorly at recognizing linguistic variation.[3] This regularly results in bots not recognizing regional dialects or not considering the vernacular of all the cultural and ethnic groups that will use chatbots.

More subtle is the tendency of chatbots and other forms of conversational AI to take on female characteristics, reinforcing stereotypes about women and their role in a service economy.[4] In both cases, it’s clear that these bots are mirroring biases present in their human authors. The question is: what can be done about it — especially at the contact center level?

Confronting the problem

Many of the solutions for chatbot bias lie in the hands of developers and the processes they use to build their chatbots. Most importantly, development teams need a diverse set of viewpoints at the table to ensure those views are represented in the technology.

It’s also crucial to acknowledge the limitations of conversational AI and build solutions with those limitations in mind. For instance, chatbots tend to perform better when their sets of tasks aren’t so broad as to introduce too many variables. When a bot has a specific job, it can more narrowly focus its parameters for a certain audience without risking bias.

Developers don’t operate in a vacuum, though, and it’s critical to consider the end user’s perspective when designing and evaluating chatbots. Customer feedback is an essential component of developing and redesigning chatbots to better eliminate bias.

An effective approach for fine-tuning chatbot algorithms involves all the above — and more. To accelerate the process and dig deeper, you need to harness the power of AI not only for building chatbots but for testing them.

Digging deeper to uproot bias

These aren’t the only ways to teach bots to do better, though. One of the most effective options is to let AI do the work for you. In other words, instead of only waiting for diverse perspectives from your development team or customers, why not be proactive to uproot bias by throwing diverse scenarios at your bots?

An effective conversational AI testing solution should be able to perform a range of tests that help expose bias. For instance, AI allows you to add “noise” to tests you run for your conversational IVR. This noise can be literal, but it can also include bias-oriented changes such as introducing the IVR to different accents, genders, or linguistic variations to see if it responds appropriately.

On the chatbot side, AI enables you to test your bots with a wide array of alternatives and variations in phrasing and responses. Consider the possibilities, for instance, if you could immediately generate a long list of potential options for how someone might phrase a request. These might include the simple rephrasing of a question or paraphrased versions of a longer inquiry. Armed with these alternatives, you could then test your bot against the ones with the most potential for a biased reaction.

Testing can take you even further in your quest to mitigate bias. Training data is one of the most critical components for teaching your bot to respond appropriately, and you can use NLP testing to analyze the training data you’re using and determine whether it’s instilling bias in your chatbots. You can even use AI-powered test features to expand the available set of test data to bring more diverse conversational angles to the table. In effect, this allows you to diversify your bot’s perspective even if your development team isn’t yet as diverse as it could be.

AI-powered testing solutions are capable of these types of tests — and more. And, when you use AI, you rapidly accelerate your capacity for testing your conversational AI systems, whether for biases or many other issues.

You don’t have to wait until you’ve assembled the perfect team of developers or accumulated a diverse set of customer data to weed out bias in your chatbots and conversational IVR. Cyara Botium’s AI-powered testing features can help you get started right away. Take a look at our Building Better Chatbots eBook to learn more.

[1] IBM. “Digital customer care in the age of AI.”

[2] Forbes. “Chatbots And Automations Increase Customer Service Frustrations For Consumers At The Holidays.”

[3] Oxford Insights. “Racial Bias in Natural Language Processing.”

[4] UNESCO. “New UNESCO report on Artificial Intelligence and Gender Equality.”

Artificial Intelligence, Machine Learning

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Contact centers are evolving rapidly. The days of single-channel, telephony-based call centers are long gone. This old model has given way to the omnichannel customer experience center.

In legacy call centers, the customer’s pathway through sales or service was relatively linear. Call in, speak to an agent, and (hopefully) resolve the issue. In this system, the manager’s focus was strictly on ensuring there would be enough well-trained staff to handle every call as efficiently as possible.

Nowadays, however, the customer journey is more complex, and the path to successful customer experience (CX) may weave its way through various channels, touching both human and robot agents along the way. Today’s managers must not only build an adequate staff, but they must also choose the right solutions to effectively meld together technological and human elements to deliver a near-flawless CX. 

Although many solutions have proved important for managers seeking to create successful contact centers, none are more important than the cloud and conversational AI. You might think of these as the twin pillars of success for today’s contact centers. However, as we’ll discuss here, they’re not sufficient on their own. There’s a third pillar to consider: quality assurance, or dedication to ensuring a finely tuned customer experience at every stage in the customer journey.

The cloud makes the contact center omnipresent

It looks like we’ve reached the tipping point for cloud adoption in contact centers. Deloitte reports that 75% of contact centers plan to migrate their operations to the cloud by mid-2023, if they haven’t already done so. IDC forecasts that investments in cloud solutions will account for 67% of infrastructure spending by 2025, compared to only 33% for non-cloud solutions. Genesys, a major contact center provider, recently announced that, going forward, it will focus its efforts on its Genesys Cloud CX software rather than its on-premises solutions.  

Considering the cloud’s potential, it’s not surprising to see that it’s taking over. Fundamentally, the cloud allows contact centers to keep pace with the changing expectations of employees and customers simultaneously.

The pandemic quickly changed what both groups were looking for. Employees came to expect more accommodating remote work arrangements, and those expectations have held strong even in 2022. According to research by Gallup, only 6% of workers who can do their jobs remotely actually want to return to a full on-site arrangement. Expectations for CX, meanwhile, have continued to rise to new heights, whether in terms of omnichannel service or personalized experiences.

The cloud makes it much easier for contact centers to meet these expectations. Without the need to rely on legacy, brick-and-mortar infrastructure, remote agents can deliver service to customers from anywhere at any time. Plus, the cloud more effectively facilitates seamless omnichannel service delivery and efficient software updates.

From setup to ongoing execution, the cloud is simply easier to manage. With no telecom hardware to purchase, installation and setup happen more quickly. And contact centers can rapidly scale up and down as needed, and when needed, allowing them to effectively manage costs.

The net effect of these benefits is that the cloud creates a new kind of contact center — one that’s omnipresent to deliver a modern customer experience from anywhere and to anyone.

Conversational AI transforms CX

One of the key benefits of moving to the cloud is the availability of conversational AI that can power self-service solutions. This technology, which is indispensable to chatbots and IVR, enables bots to interact with customers in natural — even human — ways.

Thanks to powerful components of AI, such as natural language processing and machine learning, bots are increasingly able to provide much of the service customers seek. In fact, in today’s self-service economy, conversational AI allows consumers to solve many of their own issues. Even more, the machine learning capabilities of AI allow it to easily and quickly collect customer data and use it to personalize the service experience. Unsurprisingly, organizations that employ conversational AI see a 3.5-fold increase in customer satisfaction rates.

That boost in customer satisfaction stems not only from offering personalized self-service, but also from organizations making the most of their human service. While bots handle many of the simpler requests, they reserve agents’ time for handling more complex matters. Ultimately, companies that deploy them can improve customer service while also cutting costs by between 15% and 70%.

This AI-powered CX transformation is already well underway in many industries. Banks use conversational AI to power customer self-service with simple tasks, like money transfers and balance inquiries. Hotels employ it to offer streamlined booking and concierge services. And retailers put it to work engaging customers in more personalized ways.

These are only a few of the basic benefits that forward-thinking companies can gain from deploying conversational AI. Its more advanced forms will power a new kind of proactive CX in the years ahead, shaped by powerful tools like sentiment analysis. 

True success requires a third pillar: quality assurance

Although critical for today’s contact centers, those two pieces are incomplete without the third pillar of quality assurance.

The expanded service capacities enabled by the cloud and conversational AI add new layers of complexity to a contact center’s CX delivery. Cloud migration, for instance, often involves bringing together many disparate legacy systems and remapping the entire customer journey. It requires extensive testing and mapping to make sure it’s done right. 

And as powerful as conversational AI is, it still requires a lot of human guidance to ensure it’s doing its job correctly. Without the capacity for that guidance, IVR or chatbot solutions may cause more CX problems than they solve. They can also be more costly — defects discovered in the IVR or chatbot production environment are much more expensive to undo than they would be when discovered in design.

The best way to provide cost-effective quality assurance is through a robust set of testing solutions that can work with any cloud, IVR, or chatbot solution that a contact center uses. As a platform-agnostic CX assurance solution, that’s exactly what Cyara is designed to do. 

With a powerful solution like Cyara, businesses can speed up cloud migration, correct voice quality issues, load-test IVRs, and performance-test chatbots, regardless of which solutions they use. They can even run more advanced chatbot tests to see how well they follow natural human conversation flows and recognize various speech patterns.

This kind of quality assurance allows contact centers to jump to the cloud and deploy conversational AI with confidence, knowing that both will push their CX forward. Together, these three pillars provide a firm foundation for contact centers of the future.

Ready to get started? Cyara can provide assurance for your cloud migration so you can start building these pillars. Reach out to get started today.

Digital Transformation