AI for CX 101: Deploy an AI Solution with Confidence

Written by Emily Peck  on   Jul 13, 2022

As part of our new ‘AI for CX 101’ series, we will cover what to expect when you deploy an AI solution, including how to get started on your AI digital transformation journey. Whether you’re early on in the consideration phase or deep into the process, we’re here to provide you with insights and best practices to ensure you are well-equipped to proceed with confidence.

How to deploy an AI solution for your business

Analyze historical data

As with many business undertakings in the digital age ripe with information and insights, it all begins with data. When launching a customer support AI, you’ll first need to analyze your historical data to identify the queries that are highly repeatable and have high volume, those that carry low to medium business risk, as well as low exception management. These are the types of queries that are ideal for the AI to handle. We recommend analyzing data accumulated over a span of at least three months, so trends can be observed over time.

Facilitating the real-time, human-like conversation between a human and a computer, conversational AI enables companies to make every interaction feel relevant, personal and unique – sparking conversations that are driven by intent. What does the customer intend to accomplish from the engagement? What is their sentiment at this exact moment?

For an excellent conversational experience, it is important to understand all of the various ways that people are communicating a single intent. By analyzing this historical data, you are able to not only identify which queries have high volume, but also how your customers are asking questions. For instance, Comcast found that there are 1,700 different ways of saying “I’d like to pay my bill.” By leveraging AI to analyze your historical data, you will be able to train the AI to better and more accurately pick up on the different ways people are voicing an intent, even if not explicitly trained on a specific variation.

Determine the resolution path

For every automated use case, you’ll need to understand the resolution path. While some queries might be straightforward FAQ-type responses with the same responses for every customer, others might be more contextual, and require a different answer based on factors like a customer’s loyalty status, or the resort where they are vacationing. Baggage costs for an airline, for instance, might vary based on the type of seat a person has (i.e. economy vs. first class). Rather than providing the customer with each possible cost, there might be a simple question or two that can help the AI provide just the specific information that is relevant to the customer, and provides the most value to them. There might also be personalized responses that are possible with integrations. Can the AI look into the order management system to find a person’s exact delivery status? 

Research your customers to plan, understand and improve their journey. What type of experience are they looking for, and how can you help successfully orchestrate that experience?

Create a bot personality

Now comes the creative part! To make an engaging AI-driven experience, chatbots should be endowed with on-brand personalities. For instance, do you see emojis and other rich media such as GIFs as representative of your brand? Are shorter-form answers, punctuated with punctuation marks, or text that is more grammatically correct, more suitable? Taking care to define and hone the conversational style will help to create experiences that are consistent.

Design conversational elements

Incorporate greetings, escalations, and conversation topics that are helpful, natural and persuasive, and align with the personality of your chatbot.

When designing dialogue for these conversations, we can also weave in elements that are inherent to human-to-human interactions, such as empathy and helpfulness, also ensuring the seamless alignment with those elements that artificial brains can be trained to comprehend, such as intent and context. Statements of acknowledgment can also be incorporated, which will help to build trust and make the conversation flow naturally (such as: ‘Got it. So you would like to upgrade your room to the executive suite)?’

Integrate with your agent desk, knowledge base and back-end systems

Integrations are a key ingredient to having your AI provide the most value:

  • Integration with an agent desk enhances the customer experience even further, for the ultimate combination of power and productivity.
  • Integration with your company’s knowledge base, so that if the AI is not trained on a particular topic, it can easily source and surface relevant materials for the customer. When the two are used in parallel, this also caters to customers’ desire for self-service, so they can access information that they need, when they need it the most.
  • Integration with back-end systems such as CRM (which houses rich customer data such as prior interactions and order history) allows the AI to pull in data from these sources to resolve customer service issues on an individual basis without human intervention.

Invest in ongoing learning and optimization

Over time, the AI will learn from more customer service interactions, and become more proficient in creating engaging interactions that count. However, as intelligent as chatbots can be, they are not ‘set it and forget it’ type of machines – rather, they require continual training and human supervision. It is important that support teams play an active role to optimize the chatbot’s performance over time, including tracking which queries are not understood and mapping them to the correct topics.

For instance, if a customer asks about extending their hotel room booking in an eccentric way and the AI doesn’t map it to the correct topic, the team can inform the AI of the correct topic the user’s query should correlate to so it can understand it in the future. Additionally, some topics may semantically appear very similar (such as ‘please cancel my service’ vs. ‘please cancel my subscription’, the former referring to account termination, and the latter referring to canceling a plan). In this situation, optimization is required in order for the AI to accurately learn the difference between the two use cases. We can also identify new and emerging patterns or topics, which will keep the chatbot’s knowledge updated as well as improve its understanding of your customers and your business.

Be patient, as deploying an AI solution is an ongoing journey. With some planning and forethought, your customer service team can take steps to AI-powered success.