How to deploy conversational AI for exceptional customer service

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.

Types of conversational AI technology

Conversational AI technologies come in various forms. From simple chatbots to advanced virtual assistants, these tools are designed to understand and respond to user questions, making communication easier and more efficient across personal, business, and customer service settings.

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 can be used 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.

Taking stock of your current experience is a crucial step towards understanding what better looks like. You’ll want to gather the following resources:

• Call reports
• Metrics
• Agent training material
• Contact center architecture
• API documentation
• Security questionnaire
• Analytics and transcripts
• Screenshots and call recordings

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.

Your organization can approach deployment by picking a call type that needs addressing immediately, such as high-volume and routine calls that are taking up too much time for agents and not making the best use of their skills. At this stage, you will need to decide what an impactful improvement in your metrics would look like for your company. Remember to be realistic.

Here are some goals you may want to consider. Remember to be specific and define the before-state as well as the after.

However you decide to measure success, make sure you create a solution that focuses on the customer. By doing so, you can drive engagement that naturally creates efficiencies. In other words, if customers want to talk to the voice AI solution, they won’t push to speak to an agent.

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.

Your technical discovery can be broken down into 3 key areas:

• Contact center: Where does your solution sit? Is it on-site or in the cloud? And who’s managing it? Understanding these basics will help you plan your integration.
• Voice delivery: Check if you can send custom header information in the SIP header and transfer the information back to your contact center. SIP headers will enable you to send messages in a screen pop alongside the call transfer to assist your agents with a more seamless handoff.
• APIs: It’s possible to automate a good portion of phone calls without API integrations, but if you want your voice AI to connect with other systems, APIs will be necessary.

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.

That’s because we don’t always speak to automated systems in the same way we speak to other people. And with no visual interface to help callers through complex transactions, design know-how is key. Ensure you work with a dialogue designer who has a proven track record of designing conversations for enterprise voice assistants at scale.

Here are 5 tenets of voice design to get you started:

1. First impressions matter: When designing a voice assistant, pay special attention to the first ‘turn’ of the conversation to earn the caller’s trust.
2. Empathy is important: Word choice, intonation, volume, and the use of silence work together to relay meaning. Expressions of empathy are one of the hardest things to get right in conversational design.
3. Conversations must feel human. But not too much: It’s important to strike a balance and avoid feeling too human. From the pacing and intonation of the voice to the way in which audio is edited together or synthesized, each customer requires an appropriate tone of voice.
4. Adjusting tone to match appropriate situations: Changing a voice assistant’s tone of voice to match the caller’s social and cultural expectations is an effective way of creating the best experience for the customer.
5. Reducing cognitive load over voice is key: Overly complex instructions make a conversation hard to follow. You must design a voice assistant that is easy to understand and gives each customer control over the conversation.

5. Consider your integrations

A simple SIP or PSTN connection is all that’s required to route calls between your voice assistant and your team. This is virtually the same for every voice assistant, and your IT team will handle this with support from your voice AI vendor.

A typical high-level approach is shown here:

Connecting to your back-end systems (APIs)

While many calls can be automated without them, API integrations are needed to connect your voice assistant to other systems. Securing the necessary resources to build these APIs can be challenging, so many companies initially launch a voice assistant on simple call routing and FAQs, choosing to open up integrations once they’ve already demonstrated the value of automation.

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 the following steps:

1. Quality assurance: To test various user interactions, commands, and responses to ensure your voice assistant delivers the expected user experience and identifies any issues.
2. Load tests: To ensure the voice assistant can handle the number of calls you want it to answer.
3. Team demos: Have your team call in and try the system for themselves. Your agents are great for this, as they know what customers will most likely say!

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.

This sounds scary, but the most successful teams see this stage as an opportunity. By closely monitoring early calls and making adjustments where needed, you’ll improve customer engagement and experience rapidly.

Key metrics such as AHT, call volume, and containment should be reviewed regularly with stakeholders, and tweaks and enhancements can be made to the ASR, machine learning, and dialogue design to ensure the best results for your customers.

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.

Overlooking the phone channel and deflecting customers to digital self-serve can harm customer experience. While most customers call out of habit, they also call because they feel their query is urgent and want to be understood. For other customers, digital channels aren’t accessible, so they rely on the phone channel to access support.

Companies can supplement their digital self-service offerings by deploying conversational assistants without sacrificing the phone channel. This technology allows companies to bridge the gap between voice inquiries and digital self-service channels and lets customers choose a channel that suits their needs best.

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.

While the upfront cost of DIY conversational AI platforms is often low, many hidden costs are associated with these projects. This makes investment unpredictable, leaving companies scrambling for additional budget to meet their goals. Without dedicated conversational design and machine learning teams, these projects often hit roadblocks that prevent deployment or cause issues further down the line once integrated into an organization.

To reduce the cost and risk of deploying conversational AI, companies can partner with a vendor specializing in voice that uses speech technology developed explicitly for spoken interactions.

This low-stakes, high-ROI approach removes the need to hire multiple technical teams. Companies will benefit from fast deployment and low risk of execution, with predictable upfront costs and operating expenses.

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. As a result of these vast training datasets, LLMs enable conversational assistants to accurately understand the meaning behind words and sentences without having been exposed to those examples before.

This eliminates the need for companies to provide extensive datasets for training. A couple of hours of talking through common call flows should be enough to design and build a custom conversational assistant ready to deploy with real customers within weeks.

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.

As a result, organizations will embark on a project that tries to solve a number of ill-defined challenges simultaneously. Deployments that take this approach will likely experience unforeseen roadblocks and lose momentum, costing companies time and money. By setting clear objectives from day 1, project leaders can focus on what’s most important to their business.

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.

This is often an excellent way for project leaders to secure further investment. Using the data surfaced from the initial deployment, companies can identify high-impact areas for further automation. When the value is proven with no integrations, you’re more likely to get additional support from IT teams to open up APIs and support deeper integrations.

To get started with implementing voice AI in your contact center, join our voice AI experts for a live demo of remarkably lifelike voice assistants.

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