Conversational AI and customer experience [Q&A]

These days when you contact a company online it can be hard to know if you're dealing with an actual person or with an AI bot.

Clearly AI has a role to play in automating repetitive tasks and answering straightforward queries. But can it really have a wider role to play in improving customer experience? We spoke to Derek Roberti, VP of technology at Cognigy, a global conversational AI platform provider, to find out.

BN: What exactly is conversational AI?


DR: Conversational AI automates conversations between computers and humans. Because it's communication-centric, this technology is leveraged across the enterprise by customer service, HR, marketing, IT helpdesk, and sales in the form of messaging apps, chatbots and voice-based virtual agents. With technology advancements, conversational AI is now on a level where it can mimic human dialogue, making interactions more natural and conversational, and creating the most personalized experience possible between humans and machines.

Conversational AI is built on natural language processing (NLP) and machine learning (ML). This gives the technology the ability to interpret customer conversations and automatically respond with relevant, contextual answers. This can drastically improve business processes and communication experiences for the end-user.

BN: Can it do more than simply responding to FAQs?

DR: A common misconception about conversational AI is that it only solves knowledge-based problems. Conversational AI can do so much more than simple FAQ bots. Here are several reasons how:

  • It uses natural language processing to understand customer needs
  • It understands the context and nuances of every conversation
  • It solves problems instead of doubling as a search engine
  • It engages and connects with users through personalized conversations
  • It 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 it can more accurately route a customer to a more appropriate customer service representative. The faster a customer can have their 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 voice bot 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.

Let's say the customer goes on to say they aren't happy about the delivery time. By recognizing the customer's dissatisfaction, it says it will route the customer to a live agent, who is then able to help the customer further.

While this may not seem complex in the scheme of modern technology, it is. Conversational automation provides customers with the experiences they want, while providing the support that human agents need to help customers solve requests quickly and efficiently.

BN: What benefits does conversational AI deliver for the enterprise?

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

  • 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 in and outside of IT and helps avoid bottlenecks.

Conversational AI has proven to provide users with high-quality, consistent, and personalized customer service experiences along with increased revenue and support efficiency. It can quickly streamline support infrastructures with the help of self-service portals for customers, and this naturally leads to quicker query resolutions, saving countless hours of support agents, and reducing the associated costs.

Another benefit for the enterprise, AI works around the clock, 24/7. No matter what time it is, or where you’re geographically located, it can always provide quick response times. Conversational AI also offers more scalable and consistent customer support, allowing businesses to maintain quality of service during seasonal spikes and high-demand cycles. This naturally leads to better customer satisfaction, minimizing negative experience associated with long response times. What does this result in? A more positive customer experience.

BN: What are the challenges of implementing the technology?

DR: Implementing the technology also takes time. Like humans, AI takes time to learn and understand language, requests, and actions. This isn't a one-size-fits-all technology. It needs to be deployed properly under the right conditions to prove successful.

Unlike previous technologies, conversational AI can pose a security or privacy risk because it helps enterprises collect personal information from users such as PII, passwords, health, and financial information. However, this risk can be mitigated by using marketing, support, legal, and internal business processes to ensure appropriate data management procedures are followed. IT can also be leveraged to establish standards for data integration, governance, and deployment.

To lessen the implementation impact of conversational AI, a few questions that companies should ask vendors are:

1. Is your platform low-code? When it comes to creating virtual agents, the best people to do this are often those who work on the front line with customers. They know the questions that customers generally ask and what replies they need to be given to answer their queries as quickly as possible. Your ops people may not be technical experts, but they are masters at customer service and maximizing customer satisfaction -- which is exactly what you want your bots to do.

2. Can we build end-to-end customer journeys across channels? Most chatbots are single-purpose and built to work on a single channel. It should be possible however, in a modern and versatile automation platform, to chain bots together in order to create entire customer journeys. Using nodes and endpoints (coding and API terms which allow systems to be connected together) it should be possible to create branching conversation trees that even take in multiple channels, such as passing the customer between voice and chat, or making an automated SMS follow up to a chat to check the customer's query was correctly handled.

3. Is your solution open source, proprietary, or a mix of both? Open source software can be great, as you can potentially make it do exactly what you want, but only if you have a serious level of technical expertise and coding talent in-house, as well as the time to build your own solution from the ground-up. The sweet spot is a solution built on open standards but supported by a strong vendor. This generally gives you a platform which is almost infinitely customizable, easy to integrate with other software, and can be extended to offer whatever functionality you need.

BN: Will there still be a role for human customer service agents in future?

DR: Looking towards the future, conversational AI will always be used together with humans. With phone still the preferred method of resolving customer service issues, rather than replace humans, companies are looking for solutions that support them. Virtual agents help teams across the enterprise handle customer and employee requests in areas that they're specifically trained for. While artificial intelligence can help to determine the route at which a customer takes through the contact center or organization, businesses will always need the human touch to ensure customer needs are met.

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