Conversational AI

Signs of a Great Conversational AI Application

Artificial Intelligence is now seen as a critical component for enterprise digital transformation. AI can take many forms

Artificial Intelligence is now seen as a critical component for enterprise digital transformation. AI can take many forms to help businesses achieve accelerated growth and improved operational performance in highly competitive markets. Among the various forms of AI, voice interfaces are probably the most demanded. For businesses, an AI-driven voice interface is known by a different name – Conversational AI.

The benefits of conversational AI seem to be expanding without limit as AI continuously evolves. From improving customer service to increasing online sales revenue, the wide array of benefits granted by conversational AI are what businesses are willing to invest in.

As a matter of fact, Gartner predicts that by 2020, 25% of customer support & service operations will be driven by virtual assistants across various engagement channels.  

So the question now isn’t about how a company should implement conversational AI. It’s more about where they should start. That said, this blog covers a few major aspects that make a great conversational AI app. Make sure your journey to implement and leverage conversational AI is along the right course.

Implementation efforts centered on the business case

Before you start implementing conversational AI for your business, make sure that you can articulate the business case as well as the returns you expect from the technology. If you aren’t sure about the latter, work collaboratively with your AI services partner to help define the value you would like from your conversational AI.

With your efforts centered on the business case and the expected value, it’d be easier for you to deploy the right technology by making the right design decisions. The key is to stay true to the defined goals. As long as you are sure of the desired result, you can shape the implementation properly while taking into account the business constraints such as time or budget. This way your tech will only possess features that your business really needs.

A UX that streamlines customer journey

The AI-based solution’s adoption and revisit rates depend on a number of factors out of which the UX is possibly the most important. Even if the conversational AI solution features attractive dialogs and integrated data that facilitates personalized responses, it would still appear underwhelming if it lacks a solid user experience.

For a conversational AI, the UX needs to take a few major factors into account. The solution should speak and converse like a human. This doesn’t mean it should pretend to be one. Users may expect digital customer support employees to participate in small talk, remember, understand context, and be smart.

Many companies try to use chatbots that pretend to be humans when interacting with potential customers. This is likely to backfire as the approach can break the trust the end users have on the solution. The conversational app should be able to resolve the user’s issue. If it fails to do so, the user will approach a different channel with a better alternative that can serve them better.

Information pertaining to things that are out of scope for your intelligent bot should be provided early on in your customers’ journey; at least as links. The personality of the bot is also important. It can be humorous, sassy, or formal with a rich UI coupled with modern design standards.

The key is to have the conversational app cover the user journey from the beginning to the end while drawing out a satisfactory conclusion for users with their issues resolved.

A scalable platform that can handle multiple intents

When going for an AI-driven conversational bot, it’s best to start small. Implement the idea as a small project and develop it further in phases after assessing the initial results. Choosing a scalable platform here is a great approach that helps you capitalize on your initial investment and gives you a lot more options when moving forward.

The user will want to achieve something with the app, right? There will be an ‘intent’. You should ask yourself how many intents your chosen platform can handle. Various conversational AI platforms have different capacities when it comes to handling intents. The platforms will have different algorithms to process the intents. Though Machine Learning capabilities make it easier for conversational AI applications to learn intents, such features still won’t be enough if there are hundreds of intents over multiple business departments, divisions, languages, and geographic regions.

There are platforms that are powerful enough to deal with a large number of intents but there tends to be a limit at present which forces enterprises to develop multiple solutions to get by. The bottom-line here is that the conversational AI platform should be capable of delivering precise responses depending on user intent and context. It’s not easy but it is practical provided the chosen platform is highly scalable.

Top-notch security and encryption standards for customer data

Conversational data can be a goldmine in disguise for businesses. Such data can be analyzed and understood much easier than the data gathered from calls. However it also presents a great risk to organizations. They will need to secure the data and ensure the privacy of their users. Any compromise in the conversational data could end up causing your customers to lose trust in your business instantly.

The AI solution should be capable of anonymizing the data while also making it easier to understand the intents for analytics. The identifiable data can be replaced with placeholders. This way the customer identity will remain anonymous while the intent will be clear to your organization for analytics purposes. If there are transactions involved, the application should have robust encryption mechanisms to encrypt the data that would be transmitted over the internet.

Your organization should be certain about the information you wish to collect from customers, the security practices that can be implemented to safeguard the data, and practices to make good use of the data without compromising the privacy of customers even before building a conversational AI application.

The value of the AI solution depends on its capability to continuously learn and improve. Building one such solution requires great proficiency in AI and associated technologies and years of experience in building enterprise-grade business solutions. AOT has both, and we can help you utilize a powerful conversational AI program as well. Talk to our experts today to learn more.

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