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Five mistakes companies make when deploying conversational AI – and how to avoid them

February 23, 2023

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The rise of digital self-service channels has increased demand for seamless and efficient customer experiences.

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 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 the overall customer experience.

Here, we’ll look at five mistakes companies make when deploying conversational AI and, most importantly, how to avoid them.

1. Not considering voice

FAQs, mobile apps, and online forms are just some of the digital self-service options companies have invested in to help customers solve their problems more efficiently.

Many companies begin their conversational AI journey with a chatbot to supplement their existing digital self-service channels. It’s a good place to start; chatbots can be deployed on established channels and help deflect call volume.

However, 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. Taking a DIY approach

There are several ways to design, build and deploy conversational AI assistants.

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.

Getting voice right is extremely difficult. Deploying truly conversational assistants requires a large team of Machine Learning Engineers, Product Managers, Dialogue Designers, and speech experts. Companies need access to significant resources and technical capabilities to pursue a DIY approach to conversational AI, which is especially difficult for organizations with limited budgets venturing into conversational technologies for the first time.

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. Over-investing in speech analytics

Many conversational AI vendors require huge amounts of data to train natural language understanding models, including customer conversations, call transcripts, and contact center training materials.

To gather this data, many companies invest in speech analytics projects. But these projects are time-consuming, costly, and often completely unnecessary.

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. Not having 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. Overestimating integrations

There 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.

Conclusion

Deploying conversational AI offers a significant advantage for companies seeking to enhance customer experience, reduce costs and improve efficiencies. However, without the right resources, expertise, and guidance, deploying conversational AI can quickly become a costly and time-consuming process.

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