Amazon Connect Simplifies the Adoption of Conversational AI

Discover how AWS streamlines conversational AI deployments with its automated chatbot designer

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Amazon Connect Simplifies the Adoption of Conversational AI
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Published: May 30, 2023

Charlie Mitchell

Without dominant vendors seizing the lion’s share of the conversational AI market, many view it as a vast blue ocean of opportunity.

Yet, since the start of the COVID-19 pandemic, many more ships now sail those waters.

Indeed, the space has become congested, and few vendors stand out from the crowd.

AWS is one of those that does, with its Amazon Lex solution that sits inside its Connect platform.

As such, Lex has become widely used within contact centers, as AWS leads the CCaaS market in customer acquisition with Connect.

Yet, it has not only become a prominent conversational AI platform because of its accessibility. Indeed, analysts generally consider Lex as a market-leading platform – as recent Constellation Research suggests.

One of the features within the platform that perhaps underlines this status is its automated chatbot designer. Alone, it differentiates Lex from its competitors in the vast blue ocean.

How? By automating much of the traditional toil of designing best-in-class virtual agents.

Find out how it does so after considering all the conventional challenges of bot building.

The Traditional Toil of Bot Building

The first stage of bot building involves isolating customer demand drivers. Without an analytics system, this hard graft. After all, contact center disposition data is often inaccurate.

Moreover, it is often tied to IVR inputs, which managers rarely review.

As such, bot developers typically spend weeks trawling through conversation recordings and transcripts to identify the most pressing reasons why customers contact them. These are often referred to as “intents.”

The process is not only time-consuming and error-prone but lacks efficiency, as the contact center struggles to accurately quantify the prominence of each intent.

As a result, it is extremely difficult to quantify ROI for the bot.

Furthermore, some intents may overlap or go missing, confusing the design process further and ultimately leading to a frustrating customer experience.

Such inefficiencies also make it tricky to identify the customer’s desired outcome for each intent and build a logical flow from the initial query to that outcome.

Indeed, the entire process is error-prone, and perhaps a significant reason why 60 percent of customers still face frequent disappointment in their chatbot experiences.

Amazon Lex Automated Chatbot Designer Cuts Through the Toil

Last year, AWS released its Amazon Lex Automated Chatbot Designer. “It uses machine learning to automatically design a chatbot in hours, instead of weeks,” said Annie Weinberger, Head of Business Applications Product Marketing at AWS in conversation with CX Today.

Developers can start by uploading their transcripts into Lex, where the chatbot designer analyzes those transcripts – using machine learning – and creates an initial chatbot design that can be reviewed and deployed.

“It can analyze thousands of lines from transcripts within a couple of hours. This reduces developer effort and the time it takes to create a chatbot. But, because of those ML-powered intents, it leads to a better customer experience.”

Alongside this base design, the solution churns out associated phrases and a list of the information required to resolve each intent. That may include customer, order, or policy numbers.

Harnessing this extra information, developers can iterate on the design and ensure the necessary integrations are in place to retrieve the data.

From there, teams may brief senior agents and leverage their expertise to adjust bot responses, ensuring a smooth experience.

Finally, the business can test, tweak, and deploy the conversational AI solution.

All This Within Amazon Connect

AWS has embedded Amazon Lex into Connect. As such, it is easy for its CCaaS customers to channel customer transcripts into the Automated Chatbot Designer.

In doing so, the vendor further accelerates the bot design process and lowers the risk of errors.

Moreover, it enables integrations between the bot, contact center systems, and customer engagement channels, enabling seamless escalation paths to live agents.

Yet, AWS has not stopped there. Amazon Connect also has native integrations with other AWS services, including Polly and Lambda.

As a speech-to-text tool, Polly allows businesses to transform chatbots into voicebots. Meanwhile, Lambda manages the back-end code behind the bot. As such, companies can easily scale the solution, add patches, and get a transparent view of its real-time performance.

Alongside Lex, these technologies make up what Dan Miller, Lead Analyst & Founder of Opus Research, calls: “The holy trinity” of conversational AI.

Speaking exclusively to CX Today, Miller stated:

Amazon is a master of packaging and pricing of core technologies baked into AWS licenses and activated at the click of a button on the admin panel.

“For companies with existing AWS contracts and (better yet) credits, it can be quite economical to employ Lex for automated speech recognition and Polly for text-to-speech rendering to offer a more than decent voicebot.”

In addition, Amazon Lex Automated Chatbot Designer adds value to other solutions within Connect.

For instance, by offering a clear view of intents – even those too complex to automate – businesses can also transform their IVR into something much more customer-friendly.

Indeed, businesses may migrate from a legacy tool and embrace the future of front-end bots, creating new, innovative conversational experiences.

AWS Ensures Continued Conversational AI Success

Businesses may use the Amazon Lex Automated Chatbot Designer only once when building their bot. Yet, it also delivers value beyond that initial design stage.

Indeed, by having the system parse customer transcripts at intervals across the year, companies can gain insight into emerging intents and address those.

Traditionally, businesses would have to invest in a specialist conversational analytics tool to do so continuously, autonomously, and accurately. Now, they can do so more cost-effectively.

However, Amazon Connect has such an analytics tool: Contact Lens. By also employing this, businesses gain more insights into voice and chat conversations.

Contact Lens tracks customer sentiment across all customer conversations with live and virtual agents on the chat channel. It also splits these insights across intents.

Businesses may harness this information to continuously enhance the conversational AI solution alongside customer performance.

Again, this example highlights how AWS differentiates its approach to conversational AI, and it now has its sights set on automating the entire bot design process.

To find out more about Amazon Connect and the automated chatbot designer, visit:



Artificial IntelligenceConversational AIOmni-channel

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