Mythbusting: Top Three Misconceptions of Conversational AI 

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In this special guest feature, Derek Roberti, Vice President of Technology at Cognigy, offers 3 top misconceptions about conversational AI. Derek delivers insights and understanding of conversational AI and chatbots as the next level of automation. For the past 15 years, he has focused on bringing innovation to the enterprise, and combines his passion for technology with an honest dialog about its possibilities and limitations in real-world organizational contexts.

Nowadays, virtual assistants are performing many simple and complex tasks in the form of chatbots and voicebots. When bots are AI-based, as conversational partners to live agents, they bring significant value to the entire user experience. 

In the service center, virtual agents fulfill a very important role, which is to automate parts of the interaction that don’t require a live agent. On one hand, they don’t make human mistakes, but they also don’t have natural, human instincts that allow them to make judgments. This is why the two working alongside each other is an ideal scenario – and why AI-focused companies are aiming to build out human and AI collaboration. 

Despite the success stories, many contact centers and companies have yet to embrace conversational AI innovations due to misconceptions about AI and chatbots in general. However, the tide is beginning to turn. In the 2021 Global Contact Center Survey, Deloitte Digital noted that two out of three companies surveyed plan investments in automation technologies over the next two years. 

To dive deeper into the misconceptions, in this article we’ll demystify conversational AI in a way that makes deploying it a no-brainer and in turn will drastically improve customer and employee experiences.

3 top misconceptions about conversational AI 

Conversational AI is too complex 

Incorporating new technology into an organization’s day-to-day workflow can feel daunting, but with conversational AI it doesn’t have to be. 

For one, low-code or no-code platforms enable employees who aren’t developers or machine learning experts to iterate quickly instead of making time- and cost-intensive changes through technical teams or outside vendors. This type of conversational AI empowers non-technical personnel to design, evolve and improve automated conversations at will – which will also help AI to become more incorporated into more areas of businesses, furthering cost- and time-savings down the line.

For another, conversational AI is also incredibly flexible in where, when and how it can be used as well as how it works with other systems and processes. As traditional on-premise – or legacy – contact centers shift to the cloud, automation is enabling IT teams to transform as well. Conversational AI captures digital footprints across the customer journey to identify common issues and strengthen the knowledge base for customer support. It also serves as a first line of contact for more basic customer inquiries, which reduces the stress load on the live support agents and allows them to address more complicated or sensitive customer needs. With the digital shift, companies can leverage conversational AI whether they are working in the cloud or on-premise. By enabling implementation no matter where the back-end technical work is happening, IT teams can integrate conversational AI quickly and easily into existing legacy systems, enterprise-grade technologies or intelligent automation platforms, thanks to pre-built channel integrations and APIs. These are crucial points, as a recent industry survey showed that 40 percent of companies say they are unable to connect their different channels, and 39 percent are also struggling to integrate call center solutions with broader enterprise systems. 

Deciding to use conversational AI to support and augment existing services does not mean organizations must bring on every feature or offering right out of the gate either. We see many companies starting with voicebot and/or chatbots, becoming familiar with that technology before eventually expanding capabilities. Once organizations begin reaping those benefits, they naturally tend to scale their AI advancements, getting more comfortable with offerings like speech analytics and real-time speech-to-text – two tools that businesses are continually adding to their toolboxes. 

Conversational AI only addresses knowledge-based problems

Conversational AI can do so much more than simple FAQ bots. How? Because it: 

  • Uses natural language processing to understand customer needs
  • Understands the context and nuances of every conversation
  • Solves problems instead of doubling as a search engine
  • Engages and connects with users through personalized conversations
  • Makes use of automation capabilities to bridge process gaps

Conversational AI gives customer service virtual agents the intelligence to handle more complex requests so that they can more accurately route a customer to an appropriate customer service representative. The faster a customer can have an issue solved, the more effective the overall experience is.

Imagine a scenario where a customer wants to know a company’s refund policy. Instead of engaging a live agent for this query, a voicebot or chatbot can deliver the information by tapping into the knowledge base. But then the customer asks when their order will be delivered. The bot can access that information from back-end systems and deliver it directly to the customer. At the same time, there may be languages to translate or switching across channels from phone to text, for example. This quick access to simple and not-so-simple information speeds up the handling time.

Conversational automation provides customers the experiences they want, while providing the support that human agents need to help customers solve requests quickly and efficiently.

The three key benefits of using a conversational AI platform for developing chatbots and voicebots are as follows:

  • They are governable and provide multiple user interfaces. Since the platforms are governable, it is possible to make them more intelligent.
  • By integrating with internal systems like CRM, ticketing, HRIS, or inventory management, users can complete end-to-end processes through a more conversational interface.
  • User interfaces may be either code-based or UI-based, which empowers users both inside and outside IT and helps avoid bottlenecks.

Conversational AI requires a separate analytics platform

A common misconception about conversational AI is that the analytics are siloed. Analytics tools allow the systematic examination of conversational data so that users have a reliable foundation for modifying their offerings. With conversational AI platforms, insights from all chatbot and voicebot interactions are captured and analyzed for users.                     

For example, conversational AI analytics solutions allow users to track and evaluate every step of a conversation. The use of special technologies, such as detailed journey analysis, can also precisely identify potential problem points. Regardless of whether the course is to be identified in an individual case or as an overarching pattern, at its core it’s about truly understanding conversation data in order to use it profitably. In concrete terms, if such smart analyses can significantly improve the customer experience, this will also be reflected in figures. 

And it is not just about conversations between virtual assistants and customers. Interactions with employees also provide valuable insights. Users can provide an overview of the entire contact center process – always with the aim of granularly measuring the success of service automation and identifying the neuralgic points. Powerful machine learning capabilities allow teams to track and evaluate each step in a call. This gives them an evidence-based starting point in terms of measures and projects, allowing them to analyze and adapt to deliver even better customer service while also driving more efficient internal processes.

Is conversational AI there yet? 

As a technology, we are still early in the conversational AI development cycle. We’ve taken on the technological hurdles sufficiently but haven’t yet created all the reference experiences that we can model for diverse use cases. That’s the next step. 

Developers, product managers and business analysts – they have never had to design a conversation before, so a lot of what we see on the web and hear on the phone aren’t necessarily examples of a good user experience. The industry needs to deliver best practices and educate stakeholders on what good conversational interactions look like. 

Once we bridge that user experience gap, we will see a fundamental shift in how conversational automation and the value it provides are perceived, allowing businesses to unlock the true potential of conversational analytics to help customers achieve what they want to achieve quicker, easier and with a more positive experience, 24/7. 

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