The difference that an enterprise conversational AI platform can make

It wouldn’t be fair to say that the GenAI bubble has burst, but there is certainly less enthusiasm for the technology than a year ago.

Gartner has flagged this already. In their 2024 Hype Cycle for Emerging Technologies, Generative AI has passed what they call the ‘peak of inflated expectations’ and is the furthest new tech down the slope to the ‘trough of disillusionment’.

ChatGPT blew all our minds when it first launched, but its well-publicized tendency to hallucinate and its many legal problems have eroded confidence. It also triggered a tsunami of technology companies adding ‘AI’ to products that probably don’t need it. Perhaps we are all suffering from AI fatigue and impatient for a time when we can leave the tedious chores to machines while we go to the beach.

But that isn’t the point.

AI is here to stay, but business leaders are realizing that its true value is in facilitating a better experience for customers, not dominating the experience. Along with the back-office automation roles that it excels at, conversational interfaces can be used to support, market, sell, and evangelize a business’s core products at a scale that wasn’t possible before.

Conversational AI is the technology that enables machines to engage in natural, dynamic conversations with humans using spoken or written language. The AI is trained to understand intent, context, and even sentiment to provide relevant and meaningful responses.

However, the novelty of ‘Hey, I’m talking to a chatbot that sounds just like a person’ has worn off. If AI isn’t offering obvious benefits, then what’s the point? As our recent research has shown, if a chatbot is badly designed, people of all ages would rather wait on hold to speak to a human that can actually help.

This is where enterprise conversational AI platforms can help.

While an individual chatbot implemented on your website or WhatsApp channel could be classed as conversational AI, an enterprise conversational ai platform enables business to add intelligent AI to all the channels and touchpoints where a customer may interact with the brand throughout their relationship.

It helps to deliver useful, personalized, orchestrated, and gimmick-free experiences for customers. The AI isn’t the experience, but it contributes to making the overall experience memorable for all the right reasons.

The enterprise bit refers to the ability of the platform to cope with complex businesses that have many products and services, and a range of compliance regulations that they need to adhere to.

Imagine an international bank that offers retail and corporate banking, loans, insurance, mortgages, and wealth management consultancy. A customer may use one or multiple services, for example a personal bank account and credit card and then a mortgage product and insurance that they have taken out with their spouse.

Each product is managed by a different team at the bank with specialized chatbots being used by each department. For an effective and consistent service, information about the couple and their communication preferences needs to be shared across the organization without breaking any compliance rules or exposing the data to outside threats.

This is only possible with an enterprise conversational AI platform.

Every business is different, and each one would probably have their three top reasons for choosing to implement a conversational AI platform. Here are three that you might wish to consider.

As consumers we always remember a business that goes above and beyond to help us, maybe an employee that helped us out when we are in a jam by locating a part that we needed desperately or staying late to complete a task. With a conversational AI platform this type of experience can be extended to all your customers. Just a handful of examples include:

Enterprise conversational AI can help you service your customers on any channel they choose. We all have our favorite way of communicating with a business, whether that is email, by phone, or on our preferred chat app. Conversational chatbots can be deployed on every one of these channels and can cope when a person wants to switch channels by maintaining the context and history of past interactions.

This means that your human and virtual agents will always be on the same page and customers won’t get a disjointed or repetitive experience.

When talking about improving customer experience, the benefits of cost savings for the business are seldom mentioned. There is a myth that cost efficiencies lead to a degradation in service levels. This is simply not true. By automating repetitive and low value processes means that humans and budget can be used for creating exceptional experiences that set a business apart from its competitors.

When choosing to invest in an enterprise conversational AI platform, here are some of the most important attributes that you need to consider.

A key component of any conversational tech stack is the ability to build and deploy chatbots for any channel – easily. This means a chatbot builder with a no-code interface that enables business users to build, train and deploy both simple rule-based chatbots and sophisticated ones that use natural language processing (NLP) to understand customer intent and respond appropriately.

The platform should also allow you to deploy chatbots to multiple channels from the same base architecture and customer data platform (CDP).

As we have alluded to already, a conversational approach is not just a checkbox that you can satisfy by simply bolting a chat interface to your existing tech stack. It is about facilitating conversations across all channels and throughout the customer journey, with the option to switch channels when it makes sense to do so.

Imagine a customer Googling your sales support number to enquire about a product – the search results include a chat link which allows them to connect instantly with a WhatsApp chatbot, which supplies the answer they are looking for, but also suggests an alternate product that may be a better option for them.

The chatbot is able to display product information in the chat with the option to purchase or click through to the website to view more detail and product reviews. The person purchases the product and opts in to receive order status updates by SMS and related product news by email. Their full details and interaction history are saved to the customer database and a few weeks later the marketing automation solution selects them to be included in a new Facebook campaign based on their customer profile and purchase history.

The person shares the post on their social feeds, triggering a whole new set of conversations and opportunities for engagement – all facilitated by a conversational AI platform able to support all channels in the mix.

For most organizations, the contact center is the human face of their operation and where most conversations with customers happen. Any conversational AI platform needs to have an enterprise-ready contact center component. Covering both traditional channels like voice and email, but also including live chat, chat apps, video calling, and social media.

It is also important that the platform supports a ‘human in the loop’ design so that people can request to speak to a human agent if they are not comfortable interacting with AI. This ensures a conversational experience that is memorable for the right reasons.

Conversational AI’s worth grows exponentially the more information that it has access to. This means integrations – lots of them. Real-time access to payment and account systems for account queries, biometric systems for ID verification – all made possible by a thorough and flexible API that is easy to work with. Adding new use cases with integrations is a key driver for success.

Possibly the most attractive feature of conversational AI is the ease and low cost of scaling it. While it would extremely expensive and time-consuming to double the headcount of a human contact center, doubling the number of conversations that a chatbot can handle is just a matter of provisioning extra server space.

An enterprise conversational AI platform should amplify this strength rather than hinder it by making it simple to increase the bandwidth of the solution and roll out additional use cases.

Conversational AI must have access to personal customer data in order to provide a useful and accurate service, but it has to ensure that this data is protected and not displayed inappropriately. For example, some messaging interfaces will display the first lines of a message even when a device is locked. No sensitive information should be included in this part of the message.

Data protection regulations are becoming widely adopted, such as GDPR in Europe and CCPA in California. Compliance with these regulations is a legal requirement for enterprise businesses and is crucial to win the trust of customers interacting with AI.

Every organization is unique, and this is certainly true of how they capture customer, sales and operational data, and how they use it to create actionable insights and reports.
Quite simply, an effective conversational platform needs to be able to extract, aggregate and incorporate key data from conversations to enrich existing data sources and metrics, whether that be customer preferences, activity and purchase history or marketing stats like lead qualification, engagement rates, or conversion metrics.