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The cloud, combined with conversational artificial intelligence (AI), is dramatically expanding the capabilities of the modern-day contact center. These solutions are the twin pillars of contact center success, allowing them to serve more customers faster and more effectively.

The two technologies go hand in hand for creating the flexible, flawless customer experience (CX) that companies everywhere are striving for. The market for cloud contact center solutions is expected to reach $11.74 billion by 2028, and 80% of those companies that migrate to the cloud plan to use AI and machine learning technologies to further improve customer experience in the cloud.[1] In fact, in most cases, investing in a cloud contact center includes access to the platform’s proprietary AI solutions.

Layer upon layer of shifting complexity

In many ways, moving to the cloud simplifies the systems, hardware, networks, and databases contact centers are built on. But it also opens the door for new business-driven complexities around processes and integrations between the APIs, channels, and platforms that are now working together to support the contact center.

The shift to the cloud coincides with a pivot away from VOIP and traditional telephony and a move toward real-time communication where customers open a web browser and go through a website or use voice or video directly from the browser to engage support. With this evolution, digital will become more prominent than voice and will add additional volume for the contact center to accommodate. Customers will also use multiple channels concurrently. It is difficult to transition from one medium to another while providing a seamless customer experience, adding more complexity and volume.

AI adds yet another layer of complexity. In the contact center, AI powers chatbots and voicebots, but it also helps personalize your experience, such as by customizing prompts or changing the order of menu items. It also drives reporting and analytics to enable better understanding of customer feedback and intent, as well as providing real-time agent assistance. All of these things need to be integrated, and the integration and resulting customer experience need to be tested and monitored.

As the cloud and AI expand contact centers’ capacity for customer service, they also introduce a testing burden of such a scale and scope that no company can keep up without automation. With manual testing, it is impossible to keep up with the complexity. 

 

What it takes to execute flawless CX

When it comes to the customer journey, it has always been critical to test every possible path to ensure that the customer can navigate without anything getting in their way.

For instance, even in a simple IVR setup, a customer will call in and receive several options at each menu. Any given customer might select any number of combinations and permutations as they journey through the IVR, creating numerous potential pathways through the system — each of which needs testing. In practice, it’s best to test every potential pathway from end to end. That’s the only way to ensure you can find defects and resolve them before they impact customers.  Keep in mind, too, that traditionally a human tester (or a team of testers) manually tests their way through every identified journey to make sure everything works as expected. It’s no small task to keep it all straight and make sure everything is tested thoroughly and regularly.

The true cost of conversation: creating the impossible manual task

When considering traditional IVRs and basic chatbots, customer journeys through these systems are linear with one step naturally following the next, making testing a significant but manageable task. But what happens when you introduce the complexities we discussed above?

Let’s first consider what conversational AI adds to your IVR or chatbot solutions. The real value of this technology is its ability to create natural language and conversational flows within your customer journeys. Unlike the legacy self-service solutions, which were confined to a highly limited and predefined set of customer inputs and responses, today’s chatbots and conversational IVRs have the natural language processing (NLP) capabilities to navigate much more complex conversations.

That increasingly complex, natural language flow quickly multiplies the potential pathways for your customers. Let’s consider the example of an IVR for a bank.

Traditional IVR without NLP capabilities

Traditional IVRs follow linear call flows, and each needs to be tested. For example, when you call into your bank, you can check your balance, transfer funds, hear information about the bank, or speak to a representative. Each of these options represents a different call flow, and each needs to be tested with all the potential combinations of responses a user might input at each step. With all these possible combinations, the number of potential unique call flows grows to be quite large. In our experience, a standard IVR can easily have 1,000 call flows. And each of these call flows needs to be tested – if you are manually testing, that is a big task, but it’s probably still possible.

Conversational AI-based IVRs

This task becomes exponentially more complicated when you introduce conversational AI, where the potential ways a user can respond to each step grows exponentially. The reason being that instead of fixed options of responses – “press/say 1 for checking,” “press/say 2 for savings,” etc. – you now need to understand all the possible ways someone could respond to the prompt. A conversational AI system would typical have 60-100 different ways to respond (we call these utterances) to any prompt.

So now, take that same IVR where you have 1000 call flows:

1000 call flows10 steps per flow60 utterances per step

You now have 600,000 call flows to test. That task is impossible to do manually.

This scenario isn’t far-fetched. In fact, it’s conservative. When you consider that many bots are programmed to speak multiple languages and that they often must “disambiguate” or ask clarifying questions, it’s easy to see how those test cases can balloon even further.

Making the impossible possible through automated testing

This ultimately leaves contact center executives with two options. You can, of course, forego adopting these advanced conversational AI technologies and stick to a more manageable manual testing task. In reality, however, this isn’t a viable choice. Research from Aberdeen shows that companies that deploy AI solutions have 2.5 times higher customer satisfaction rates and generate 2.4 times greater increases in annual revenue.[2] Adopting AI is the only way to remain competitive in a changing market.

Instead of avoiding AI adoption, the better option is to adapt your testing processes to fit the new technology. Cloud-based, AI-driven contact centers need to expand the scope of their testing exponentially, and that requires automated testing solutions that can handle the full range of the customer journey through their IVR and chatbot systems.

Cyara Botium is the only solution on the market that can truly make the impossible task possible by covering every pathway. To see the power of AI testing AI for yourself, check out our on-demand demo today.

[1] SkyQuest Technology Consulting. “Global Contact Center as a Service (CCaaS) Market to Hit Sales of 11.74 billion by 2028.”

[2] Aberdeen. “Contact Center & CX Trends 2019.”

Artificial Intelligence, Machine Learning

Posted on Posted in Digital transformation, General topics Tagged with , , , , , , ,

Chatbots have been maturing steadily for years. In 2022, however, they showed that they’re ready to take a giant leap forward.

When ChatGPT was unveiled a few short weeks ago, the tech world was abuzz about it. The New York Times tech columnist Kevin Roose called it “quite simply, the best artificial intelligence chatbot ever released to the general public,” and social media was flooded with examples of its ability to crank out convincingly human-like prose.[1] Some venture capitalists even went so far as to say that its launch may be as earth shattering as the introduction of the iPhone in 2007.[2]

ChatGPT does indeed look like it represents a major step forward for artificial intelligence (AI) technology. But, as many users were quick to discover, it’s still marked by many flaws — some of them serious. Its advent signals not just a watershed moment for AI development, but an urgent call to reckon with a future that’s arriving more quickly than many expected.

Fundamentally, ChatGPT brings a new sense of urgency to the question: How can we develop and use this technology responsibly? Contact centers can’t answer this question on their own, but they do have a specific part to play.

ChatGPT: what’s all the hype about?

Answering that question first requires an understanding of just what ChatGPT is and what it represents. The technology is the brainchild of OpenAI, the San Francisco-based AI company that also released innovative image generator DALL-E 2 earlier this year. It was released to the public on Nov. 30, 2022, and quickly gained steam, reaching 1 million users within five days.

The bot’s capabilities stunned even Elon Musk, who originally co-founded OpenAI with Sam Altman. He echoed the sentiment of many people when he called ChatGPT’s language processing “scary good.”[3]

So, why all the hype? Is ChatGPT really that much better than any chatbot that’s come before? In many ways, it seems the answer is yes.

The bot’s knowledge base and language processing capabilities far outpace other technology on the market. It can churn out quick, essay-length answers to seemingly innumerable queries, covering a vast range of subjects and even answering in varied styles of prose based on user inputs. You can ask it to write a resignation letter in a formal style or craft a quick poem about your pet. It churns out academic essays with ease, and its prose is convincing and, in many cases, accurate. In the weeks after its launch, Twitter was flooded with examples of ChatGPT answering every type of question users could conceive of.

The technology is, as Roose points out, “Smarter. Weirder. More flexible.” It may truly usher in a sea of change in conversational AI.[1]

A wolf in sheep’s clothing: the dangers of veiled misinformation 

For all its impressive features, though, ChatGPT still showcases many of the same flaws that have become familiar in AI technology. In such a powerful package, however, these flaws seem more ominous.

Early users reported a host of concerning issues with the technology. For instance, like other chatbots, it quickly learned the biases of its users. Before long, ChatGPT was spouting offensive comments that women in lab coats were probably just janitors, or that only Asian or white men make good scientists. Despite the system’s reported guardrails, users were able to train it to make these types of biased responses fairly quickly.[4]

More concerning about ChatGPT, however, are its human-like qualities, which make its answers all the more convincing. Samantha Delouya, a journalist for Business Insider, asked it to write a story she’d already written — and was shocked by the results.

On the one hand, the resulting piece of “journalism” was remarkably on point and accurate, albeit somewhat predictable. In less than 10 seconds, it produced a 200-word article fairly similar to something Delouya may have written, so much so that she called it “alarmingly convincing.” The catch, however, was that the article contained fake quotes made up by ChatGPT. Delouya spotted them easily, but an unsuspecting reader may not have.[3]

Therein lies the rub with this type of technology. Its mission is to produce content and conversation that’s convincingly human, not necessarily to tell the truth. And that opens up frightening new possibilities for misinformation and — in the hands of nefarious users — more effective disinformation campaigns.

What are the implications, political and otherwise, of a chatbot this powerful? It’s hard to say — and that’s what’s scary. In recent years, we’ve already seen how easily misinformation can spread, not to mention the damage it can do. What happens if a chatbot can mislead more efficiently and convincingly?

AI can’t be left to its own devices: the testing solution

Like many reading the headlines about ChatGPT, contact center executives may be wide-eyed about the possibilities of deploying this advanced level of AI for their chatbot solutions. But they first must grapple with these questions and craft a plan for using this technology responsibly.

Careful use of ChatGPT — or whatever technology comes after it — is not a one-dimensional problem. No single actor can solve it alone, and it ultimately comes down to an array of questions involving not only developers and users but also public policy and governance. Still, all players should seek to do their part, and for contact centers, that means focusing on testing.

The surest pathway to chaos is to simply leave chatbots alone to work out every user question on their own without any human guidance. As we’ve already seen with even the most advanced form of this technology, that doesn’t always end well.

Instead, contact centers deploying increasingly advanced chatbot solutions must commit to regular, automated testing to expose any flaws and issues as they arise and before they snowball into bigger problems. Whether they’re simple customer experience (CX) defects or more dramatic information errors, you need to discover them early in order to correct the problem and retrain your bot.

Cyara Botium is designed to help contact centers keep chatbots in check. As a comprehensive chatbot testing solution, Botium can perform automated tests for natural language processing (NLP) scores, conversation flows, security issues, and overall performance. It’s not the only component in a complete plan for responsible chatbot use, but it’s a critical one that no contact center can afford to ignore.

Learn more about how Botium’s powerful chatbot testing solutions can help you keep your chatbots in check and reach out today to set up a demo.

[1] Kevin Roose, The Brilliance and Weirdness of ChatGPT, The New York Times, 12/5/2022.

[2] CNBC. “Why tech insiders are so excited about ChatGPT, a chatbot that answers questions and writes essays.”

[3] Business Insider. “I asked ChatGPT to do my work and write an Insider article for me. It quickly generated an alarmingly convincing article filled with misinformation.”

[4] Bloomberg. “OpenAI Chatbot Spits Out Biased Musings, Despite Guardrails.”

Artificial Intelligence, Machine Learning

Posted on Posted in Digital transformation, General topics Tagged with , , , , , , , , , ,

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