How To Properly Implement Conversational AI

Conversational AI has been trending in business automation solutions recently – the intelligent virtual assistant market size was valued at $3,5 billion in 2019, and is projected to reach $44,2 billion by 2027. Considering numerous benefits intellectual agents can bring to both brands and their customers, it’s no wonder why more and more businesses adopt AI-based solutions.

Successfully deploying Conversational AI in a company requires thorough planning and certain preparations. It’s a bit more complicated than simply purchasing a SaaS subscription and training your team to use it.

This article will cover the most important steps you should take to implement Conversational AI in your organization.

Develop an optimal use case

Before deciding that you want an AI system, make sure it’s something you really need. The answer is positive if there’s a repetitive task that your employees have to do dozens or hundreds of times each day.

Providing this procedure can be formalized somehow and supplies enough data for AI training, there’s a space for automation here. Examples of such activities include responding to customer requests or calls, collecting feedback, calling job candidates, launching marketing campaigns, etc.

As soon as the problem has been identified, try to gather as much information as possible about the process you’re going to automate and describe it as a formal workflow broken down into stages. In other words, you’ll need to devise an automation use case.

At Neuro.net, for example, the initial training phase takes a week, and after only several thousand calls, the system reaches 95% recognition accuracy.

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Define your audience

Another important preliminary stage is defining who your Conversational AI will be dealing with – specifically, your intended audience. Pick a few key customer ‘personas’ and describe their demographic data, aspirations, desires, pains and interests. This information will help you immensely at the stage of conversational design.

By determining your target audience, you will be able to design a conversational flow so that it meets the needs of your customers and delivers appropriate experiences. Besides, different audiences might require using different tones of voice or argumentation patterns. What’s more, if you connect your virtual agent to a CRM with the client’s data and provide it with the right context, it will be able to hold even more personified conversations.

Engage your team

It is also essential that you involve your team members when planning AI implementation. Employees who are constantly dealing with customers (such as support managers, sales and account managers, marketers) can provide you with lots of information and insights regarding common issues and typical conversation scenarios.

If implementing automation means some role changes, discuss this with the corresponding team members. Perhaps, they will need some extra training. There can also be people who might feel skeptical about the whole thing, so it’s important to elaborate on the benefits everyone will enjoy after deploying an AI system (and make sure this is realistic, of course). In general, the more support you can get from your team at this stage, the quicker it will go in the next steps.

Collect data

One of the prerequisites for deploying a Conversational AI in your company is having sufficient data which will be used to train the machine learning engine. In case you have little or no data at all, launching voice bots can be problematic. Although modern solutions like Neuro.net are capable of learning on small datasets (only 600 records are required), you’ll need at least something to start with.

So make sure you’ll be able to provide sufficient data for the training purposes. It may also be useful to collect some extra information like FAQs, conversation transcripts from salespeople or customer service, customer data, etc., which can help in the next stage.

Design conversational flow

Conversational flow is at the core of AI-user interactions. A poorly designed flow will result in a bad customer experience and subsequent customer churn. Building optimal scenarios can be a complicated task because you’ll need to foresee all possible turns a conversation might take. In some cases, a virtual agent might not be able to help the customer, so there should be points where human agents should engage in the conversation.

Some AI providers will only give you a framework to build your virtual agents, so you’ll have to design the conversational flow yourself. Other providers offer a conversation design service as part of their turnkey solution, which spares you from this laborious task.

Train the engine

This part is usually done by the AI provider unless you’re developing your proprietary solution. As already mentioned, you’ll need to prepare a dataset comprising users’ voice or textual data in order to train the system to accurately determine customers’ intents and extract details (or ‘entities’).

A pre-trained engine is already capable of dealing with most requests; however, the training continues after the implementation, so the accuracy improves over time. At Neuro.net, for example, the initial training phase takes a week, and after only several thousand calls, the system reaches 95% recognition accuracy.

Integrate with other systems and test

Finally, you’ll need to integrate your newly trained virtual agent with the channels of your choice (like email, VoIP, messengers) and the software you use to streamline your communications (like CRM, analytical systems, knowledge bases, helpdesks, etc.).

The testing stage is indispensable before you scale up to a larger audience. Make sure you try as many conversation scenarios as possible to ensure maximum call center productivity and customer service quality.

Final thoughts

Conversational AI can serve many purposes, such as empowering brand communications and contact centers while saving money that can be reinvested into business development. Implementing an AI solution is a multi-stage process that involves accurate planning, data collection, conversational design, AI training, and testing. We at Neuro.net take care of the entire implementation process, including conversational design and integrations, so you won’t need to hire additional tech guys to handle that.

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