Speech-based interaction is arguably the biggest thing since the graphical user interface—and a more natural way to interact with technology. Whether you’re considering using a conversational AI platform for employee or customer queries, major efficiencies are to be gained. And good conversational design can increase satisfaction among both audiences. Read on to find out how to get started, build a strategy, and choose the right conversational AI software for your organization.
What is a conversational AI platform?
First things first—let’s talk about what a conversational AI platform really is. Simply put, it’s a software product or service that uses AI with natural language processing (NLP) to replicate the ease of human-to-human conversation, and it can be implemented to support a wide variety of business scenarios.
Conversational AI examples include:
- Customer service chatbots to handle order questions and returns.
- Sales chatbots that guide users to purchase options based on questions about their needs and preferences.
- HR chatbots that handle queries from employees regarding frequently asked questions about company policies.
How can conversational AI help your business?
Truly any process that deals with people and data (which is pretty much every business process!) is a potential candidate for conversational AI solutions. In the conversational AI examples above, technology handles things that humans used to—freeing up personnel for more vital tasks, increasing efficiency, lowering costs, and done right—increasing customer satisfaction. Conversational AI solutions can even increase revenue by making additional sales touches possible and guiding customers through a journey of upgrades or add-ons.
Getting started with conversational AI solutions
Like any business process transformation, you’re going to need a plan. First, you’ll build a strategy, then identify conversational AI software that meets your needs, and finally move on to implementation, optimization, and monitoring.
Five questions to guide your strategy
Every successful business initiative starts with a solid strategy. By defining what you need, who will be involved, and how you will measure success, you will save your teams hours of time re-configuring later by doing it right the first time.
Consider these questions as you start formulating your plan:
Is your organization aligned on a vision? If not, you’ll need to start there. The creation of a strong conversational AI architecture is potentially going to span several functional teams (often called fusion development), so you’ll need cross-functional buy-in to get it done. Identify all the players and steer them toward alignment.
What problems are you trying to solve? If this is your first conversational AI implementation, start by identifying a few processes that you believe could benefit from it. Then prioritize the one you want to start with as your proof of concept.
Who is the audience for your solution? Are you (or really your conversational AI) talking to current customers? Current employees? Prospects? Just like with any business communication, you’ll need to understand your audience, their needs, and the way they talk. Define these things—in writing—and customer centricity will be your ongoing touchstone.
What information do you already have? If you are already using live chat for a business function, for example, this will be a gold mine to discover what questions people are asking and what answers move the audience along to resolution or a sale.
What are your current technology requirements? Gather the requirements of your existing systems up front. This way, you’ll know what your conversational AI platform needs to connect to and work with—or even if you want to consider a more wide-ranging update to your processes, software, and services.
Understanding conversational AI technologies
Conversational AI solutions may contain a lot of different capabilities. Let’s go over some of the terminology you’ll want to understand about conversational AI architecture.
- Artificial intelligence (AI) is the capability of a computer system to mimic human-like cognitive functions such as learning and problem solving.
- Natural language processing (NPL) supports applications that can see, hear, speak with, and understand people interacting with everyday speech.
- Machine learning (ML) is the process of using mathematical models of data to help a computer learn without direct instruction.
- Sentiment analysis is the process of determining positive, negative, or neutral sentiment from the words and context of the speaker or writer, as determined through AI, NLP, and ML.
- Analytics is the process of computers examining data, usually large data sets (also known as big data), to find patterns and insights that may be difficult or impossible to discover otherwise.
- Automation is when the steps of a process are conducted independently by a software product or service with little or no human interaction.
- Bots are a type of automation that carries out a task, including chatbots or virtual agents that interact with humans.
- Live chat is when a chatbot transfers a conversation over to a human agent.
As you can see, there’s a lot that might be going on within a conversational AI architecture. But not every solution requires every technology. Think strategically about the simplest solution to your challenge. If you choose your conversational AI software carefully, you will be able to add more functionality later if the situation warrants it.
The importance of optimizing conversational design
Conversational design is the flow of a conversation, mapped out in advance. It requires anticipating what people may ask and planning responses that direct the conversation towards the appropriate resolution. The importance of good conversational design cannot be overstated. Poor design results in a poor user experience, which defeats the purpose of implementing a conversational AI platform in the first place.
You’ll want to consider:
- Your goals and desired outcome of the conversation.
- The language and tone used in the context of your brand and your audience.
- The flow of the conversation, including time between responses.
- Whether it makes sense to include pictures, video, and links in responses.
- Appropriate error messages.
Commonly mapped out as a visual flow chart, quality conversational design depends on deep thinking about the user’s experience and needs. And once implemented, you’ll need to monitor how well your conversational design is working and collect feedback so the experience can be improved over time.
Why connectivity and integration matter
Part of your strategy should be reviewing your existing systems and exploring which channels your audience uses. Your conversational AI platform needs the ability to interact with those systems, store and access data, and communicate in the channels your audience prefers. Integration with messaging apps, your CRM solution, calendars, and payment systems may all come into play.
What to look for in conversational AI software
Most quality conversational AI software will have some common attributes including AI, ML, NLP, live chat handover, sentiment analysis, and an analytics dashboard. You’ll want those at a minimum. From there though, consider these questions to help you identify the right software for your organization’s needs.
- Does the platform have the ability to connect to our current systems?
- Will it support the channels we use to interact with customers or employees?
- Do we have the development resources to create a conversational AI solution, or do we need low-code or no-code platform that non-developers can use?
- How easily can we prototype conversational design?
- Can the platform collect the data we want for continuous improvement?
With answers to these questions, you are well on your way to finding conversational AI software that’s right for your business.