Types of conversational AI technology
The rise of digital self-service channels has increased demand for seamless and efficient customer experiences. Over 67% of consumers now prefer self-service over speaking with a contact center representative.
Companies are looking for new technologies, like conversational AI, to improve efficiencies and meet growing customer expectations.
However, in their rush to deploy these technologies, many companies make mistakes due to poor design, inadequate training, and a lack of resources that can negatively impact operations
When deployed correctly, companies will benefit from the ability to automate processes, reduce call volume, retain staff, and positively impact overall customer satisfaction.
Here, we’ll look at five mistakes companies make when deploying conversational AI and, most importantly, how to avoid them.
Chatbots
Rule-based chatbots follow set rules and scripts to give specific answers and can handle straightforward questions but can’t go beyond their pre-set responses. AI-powered chatbots use machine learning to understand and respond to a wider variety of questions.
Smart assistants
Smart assistants like Siri, Alexa, and Google Assistant, can answer questions, play music, and even help manage schedules or reminders, typically through smart devices.
Conversational assistants for customer service
Conversational AI is used in customer service to power voice assistants or chatbots that fully or partially automate common customer queries or transactions. By automating common customer service calls, companies can free up agents to focus on more complex, empathy-requiring tasks, serve customers quicker, gather in-depth insights into customer behavior, and deliver personalized experiences based on data.
How to deploy conversational AI technology in 7 steps
Before you deploy conversational AI technology, it’s important to take stock of where you are today. Running through the following 5 exercises will enable you to develop a voice AI strategy that aligns with your current state as well as it does your future vision and will help project teams of all shapes and sizes remain focused on your business objectives.
1. Map out your current experience
If your current IVR is already infused with some self-service, it may not make sense to rip it out and start again. In that case, voice AI can be used to add more self-service capabilities alongside your IVR. You might decide to put voice AI in front of your IVR and route calls from your voice assistant to where self-service and digital resources already exist. Or you might put voice AI behind certain IVR options to start small and dip your toes in the water.
2. Figure out your use cases and goals
To succeed in deploying conversational AI technology, you must understand your baseline data and set a clear objective for introducing AI into your operations.
3. Understand your technical architecture
At this stage, the goal is to map and document the high-level system architecture required for calls or digital interactions to reach your voice AI solution, how calls will be transferred, and identify API integration points for data.
4. Design for automation
Dialogue design sounds simple in theory. We talk to each other all the time, surely we know how a conversation should go? While common sense is important in dialogue design, it’s not enough to create engaging systems.
5. Consider your integrations
6. Do thorough test runs before launching
You’ll want to run a number of rigorous tests before going live with your customers, which should include steps for quality assurance, load tests, and team demos.
7. Track performance and keep improving over time
Every customer interaction is different and it isn’t until you’ve launched your voice assistant that you’ll get to see how callers react.
5 best practices for deploying conversational AI (and mistakes to avoid)
When deploying conversational AI, organizations should balance customer experience with operational efficiency. By following best practices and avoiding common mistakes, businesses can ensure that their conversational AI solutions effectively align with customer needs and preferences to deliver a seamless, engaging experience.
1. Prioritize voice solutions
61% of consumers still prefer to speak to someone over the phone, and 75% believe that calling a business will lead to the quickest response.
2. Don’t take a DIY approach
Many organizations opt for a DIY approach, using a conversational AI platform. These platforms are often low-cost and seem simple enough to use. They are usually sufficient for basic chat-only use cases but only work well for simple conversations and voice.
3. Avoid over-investing in speech analytics
Working with the right conversational AI vendor eliminates the need to invest in speech analytics. Conversational assistants can leverage Large Language Models (LLMs) that are trained on millions or billions of conversational samples.
4. Have clear objectives
A mistake companies often make when deploying conversational AI is not clearly understanding the challenge they are trying to address or setting unsuitable objectives.
5. Don’t overestimate integrations
Accompanying typical AI myths is a misconception that deep and costly integrations are required from day one for a deployment to be successful. In reality, most companies can expect to automate around 30% of all calls with a simple telephony integration.
Conversational AI deployment FAQs
How can deploying conversational AI benefit my businesses?
Deploying conversational AI can benefit your business by automating customer support, improving response times, enhancing customer satisfaction, and reducing operational costs.
How to implement conversational AI?
To implement conversational AI, start by identifying the goals and needs of your users, then select an AI platform that suits these needs. Train the AI on relevant data, design clear conversational flows, and test it thoroughly.
How to integrate conversational AI into your workflow?
Integrating conversational AI into your workflow takes 5 key steps: identify repetitive tasks, choose an AI platform, set up custom responses/workflows, train your team, and monitor performance.