It’s never easy for an enterprise to adopt new technology, and conversational AI is no different. But the technology offers excellent potential ROI. Like, for example...
By following a few best practices in implementation, you can maximize the ROI of conversational AI.
What’s the “secret sauce” of conversational AI? A machine can process data much more quickly than a human. This means a conversational AI assistant can act more proactively than a human, potentially anticipating why a customer has initiated an interaction. But that capability is only as valuable as the data it relies on.
Thus, the first step to successfully implementing conversational AI is to ensure that your virtual assistant will have access to updated, high-quality data. That data might come from a variety of existing sources, including the website, CRMs, internal knowledge bases, or ERP systems. Generally speaking, modeling your data as knowledge graphs is a great way to ensure that it’s complete and well-structured.
Before you start any implementation, take time to evaluate the state of your data. Then make sure your virtual AI assistant has been configured to access the data through APIs, ODBC, or even RPA bots. This step will help your virtual assistant to learn more efficiently, improving ROI.
Implementing virtual assistant's as a “first line of defense” for any interaction can be tempting, but this approach isn’t usually the right one; some interactions lend themselves to automation better than others. As you consider which processes to automate, consider two key factors first:
Given these two criteria, the best candidates for conversational AI are interactions with high volume and relatively low complexity. These might include simple customer questions about account status and payment schedules or repetitive processes like booking services or onboarding customers. Starting with these interactions gives your conversational AI assistant ample chance to learn, and it can also instantly reduce your employees’ workload--providing immediate ROI.
Next, move forward by automating interactions with moderate complexity or lower volume. These might impact ROI less dramatically, but they help your conversation AI assistant continue to learn.
Thanks to technology like NLU (Natural Language Understanding) and NER (Named Entity Recognition), conversational AI is now more sophisticated than ever. This means it can handle more complex interactions that require an understanding of tone and context or the ability to navigate multi-step processes. Automating these interactions makes much more sense if they also occur at a high volume.
What shouldn’t be automated? Highly complex interactions occur infrequently. These can require significant resources in the virtual assistant configuration, offsetting potential time savings. Usually, this category of interactions is best left to humans.
Conversational AI aims to give users an experience that equals or surpasses what they would experience with a human user. Sometimes, a virtual AI assistant might not be able to deliver that experience. This could be because a request is too complex or because the AI hasn’t learned how to navigate that interaction.
In either case, it’s important to think through this handoff from a conversational AI assistant to a human agent before implementation begins. One best practice is always offering users the option to interact with a human operator when the virtual assistant cannot provide the answer. This ensures that users get the experience they are most comfortable with. Other triggers for a handoff might include the following:
Additionally, there may be instances where some human oversight is still desirable, but the virtual AI assistant can still do most of the work. For instance, if the conversational AI assistant is helping a user with complicated troubleshooting, a human might have to approve the bot’s suggestions before they are sent to the user. This configuration might be preferable for highly technical interactions.
How do you know if your AI virtual assistant is a success? First and foremost, it’s whether people get the right answers from the AI assistant, making accuracy rate one of the critical metrics. That is why the conversational AI solution needs to provide ways to analyze both AI virtual assistant performance and NLP model performance through analytics dashboards and language model evaluation tools. Here's an example of one of the tools available in the DRUID Platform that allows conversational AI developers to enhance the virtual AI assistant's performance.
Other accuracy-related criteria to consider might be how many intents (what the user wants to achieve, like changing a password or finding out the status of their order) the virtual assistant can respond to and how many training phrases are configured per intent. Generally speaking, virtual AI assistants with more conversation flows and training phrases per intent will provide a superior user experience.
It’s also essential to think about core metrics. Regardless of industry, it’s worthwhile to measure engagement rate, total and active users, total conversations, fallback rate (rate of intents not recognized), and the top triggered flows and the intents the bot failed to understand. Additional core metrics might vary depending on the purpose of the project. For example, lead capture or conversion rate might be important for a conversational AI assistant whose primary function is marketing. However, a resolution rate might be more useful for customer service bots.
Not all conversational AI technologies are created equal. Look for solutions that offer the right features for your business:
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