How to Build Your Own Conversational AI Tools

A guide to developing conversational bots

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How to Build Your Own Conversational AI Tools v2 - CX Today News

Published: August 3, 2023

Rebekah Carter

Conversational AI (Artificial Intelligence) has changed the world as we know it forever. Offering a new way to empower customers with self-service opportunities, reduce call volumes, and boost contact center efficiency, this powerful technology is growing more valuable by the day.  

As new innovations emerge in the conversational AI space, companies are increasingly searching for ways to build their own pioneering tools for increasing sales, delivering service, and enhancing marketing campaigns. It’s easy to see why, with analysts like Gartner predicting conversational AI will reduce contact center agent labor costs by $80 billion in 2026 

Not only can the right solutions transform customer service experiences, but they can also help businesses to create more loyal clients, boost average order values, and stay one step ahead of the competition. So, how do companies build an effective conversational AI tool? 

Step 1: Defining Conversational AI 

The first step in developing a strong conversational AI solution is understanding what the technology can do, and the different forms it can take. Conversational AI is a broad term, which applies to all bots and AI algorithms capable of responding to human input, either submitted in the form of text, or speech. These tools use a combination of Natural Language Processing, Natural Language Understanding, and Machine Learning technologies to deliver personalized responses to queries. 

Unlike traditional chatbots, conversational AI solutions don’t simply respond to questions with pre-set scripts. They understand the intent of customers, and use context to deliver an intuitive, human-like experience. Some of the most common types of conversational AI tools include: 

  • Chatbots: Intelligent bots built into apps or websites which can filter through customer queries, respond to questions with context, and evolve over time with machine learning. 
  • Voice bots: Specialist solutions for the contact center and sales landscape that are capable of understanding spoken human language, accents, and even customer sentiment. 
  • Interactive voice response: IVR solutions infused with conversational AI use voice bot technology to route customers to the right agent, understand customer needs, and even identify clients for authentication purposes. 

Step 2: Setting Goals for Conversational AI 

After defining the types of conversational AI solutions available, businesses can then begin thinking about the outcomes they want to achieve with their technology. Chatbots, voice bots, and intelligent voice response systems can all address a wide range of use cases within the customer journey.  

Some companies building IVR solutions use conversational AI to determine customer intent, to help route clients to the right agents and increase first-call resolution rates. Others use voice bots and chatbots to improve business efficiency and productivity, reducing call volumes by providing customers with a step-by-step way to resolve problems without human guidance.  

AI solutions can even be designed to enhance the customer experience by first improving employee experience. Agent assistant tools can provide professionals with quick access to contextual data, next-best-action guidance, and even real-time insights into customer sentiment during conversations. 

Setting goals based on key business metrics and desired outcomes will help companies to determine what their AI solutions need to be able to do, and how they can monitor their success.  

Step 3: Find The Right Bot Building Service 

In the past, implementing conversational AI into a business CX strategy would involve heavy time and financial investments in development and configuration. However, innovative businesses now offer companies a more flexible solution to bring their tools to life. A comprehensive conversational AI platform offers access to visual workflows, configurable micro applications, and pre-trained language models, to reduce the time to value for business leaders.  

The right solution comes with everything companies need to get started, from NLP and NLU tools, to intuitive drag-and-drop flow builders, and even APIs and integrations for third-party apps. With an effective platform, companies can leverage the industry knowledge, technology, and expertise of another company, to create custom apps in the shortest timeframe possible.  

Solutions like the Genesys AI platform, which integrates directly with cloud-based contact center tools, customer journey orchestration platforms, and third-party software, are packed with useful features. Genesys even offers access to handy self-service micro apps for repeatable processes, such as secure payment capture, and identity verification.  

Step 4: Create the Prototype 

Effective planning and research goes a long way towards developing a successful conversational AI solution. However, eventually, businesses need to actually put the technology into action, so they can assess its functionality. Creating a prototype gives businesses an opportunity to pull all of the core parts of their AI solution together, into a working app or solution. 

Within the right AI building platform, business leaders will be able to productize their solution, stage the technology, and even run pilot tests to demonstrate the potential value of the new system to stakeholders and business leaders.  

In the prototyping stage, companies also have an excellent opportunity to seek out additional support and guidance from their vendors. Leading AI experts in the CX landscape can offer useful resources, documentation, and even training to streamline the production process.  

Step 5: Train and Optimize the AI Solution 

Finally, while the right AI building platform will come with pre-trained language models, algorithms, and tools for businesses to access, there’s still scope to personalize the AI experience. Business leaders can take advantage of the natural language understanding capabilities of AI algorithms to train systems how to understand specific words, recognize intent, and analyze sentiment.  

Leading AI building platforms even come with supervised learning solutions, which allow companies to gradually improve customer experiences over time. Human-in-the-loop monitoring, analytics, and feedback provide businesses with the resources they need to optimize their self-service strategy. 

Additionally, with APIs and integrations, teams can infuse conversational solutions with valuable data on customer journeys and profiles. This leads to the creation of more personalized and contextual AI-driven journeys.  

Uncovering the Value of Conversational AI 

Alex Doyle, VP Product Management at Verizon Business Group says: “Conversational AI is becoming an essential tool in the communications landscape. Consumers have grown accustomed to receiving exceptional, personalized experiences when working with brands. Conversational AI can empower companies to listen to, and understand the needs of their customers, and act with empathy throughout the entire buyer journey.”   

Demand for conversational AI solutions has grown drastically in recent years, as companies look for new ways to respond to ever-evolving customer expectations. With the power to offer personalized self-service guidance across a range of channels, conversational AI tools can deliver exceptional value to business leaders and consumers alike.  

Thanks to evolving platforms like the Genesys AI ecosystem, creating a conversational AI bot is now easier than ever before.  

Artificial IntelligenceChatbotsConversational AIWorkforce Optimization

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