As companies continue to search for ways to cut costs and improve customer experience (CX), self-service solutions have become increasingly popular.
Conversational AI applications as a self-service option have seen rapid development, resulting in the emergence of no- and low-code platforms that utilize natural language understanding to understand and process the queries of end-users. Such platforms offer greater speed-to-implement at lower cost than core or traditional professional coded or pro-code applications.
In the context of contact centers, these platforms can potentially automate simple customer queries, freeing up agents to focus on more complex issues and enhancing the overall CX. In numbers, it is estimated that the Conversational AI market will reach $26.9 billion this year, according to VentureBeat. But can all technologies deliver the desired outcome for your customers?
The Limitations of No-, Low-Code Platforms: Risks and Challenges
While no-code and low-code platforms can be a cost-effective option for a small enterprise, they may not allow for the level of customization required by certain businesses. And their generic language models may fall short in delivering the desired CX if the need of the user is not correctly understood.
No-code and low-code platforms are a risk if they cannot provide customers with natural and rich dialogs that can seamlessly switch between topics, and if they don’t interact with all channels, such as the phone channel. A bad text-based bot may result in customers calling the contact center instead, making the chatbot less valuable for the business.
While the ability for individuals with little to no coding experience to manage no- and low-code platforms is viewed positively, many of these platforms rely on pre-built content and do not offer much room for modification.
Consequently, businesses may have to settle for generic responses, chatbots that are not tailored to their specific needs, or voicebots that only admit specific commands.
A bad text-based bot may result in customers calling the contact center instead, making the chatbot less valuable...
Thus, the future of Conversational AI may involve the integration of no- and low-code platforms alongside pro-code platforms to achieve greater flexibility, scalability, and customization options.
No- and Low-Code Versus Pro-Code Platforms
No-code platforms provide a simplified way to create applications or workflows without requiring the user to write any code. They rely on pre-built templates with drag-and-drop features, which is a great option for individuals or businesses who need to create simple applications or automate basic processes, but who lack the technical expertise or resources to do so with programmatic coding.
Meanwhile, low-code platforms provide more flexibility than no-code platforms, but still require less coding knowledge than traditional pro-code development platforms. Low-code platforms typically provide a visual interface and drag-and-drop components that allow users to quickly build more complex applications or workflows, allowing for a higher degree of customization of content.
Pro-code platforms may also provide visual interfaces and pre-built components to help speed up the development process, but all components can be modified, and other custom components can be created.
Pro-code platforms therefore require trained developers who have advanced programming skills and can take full control over the development process. These platforms typically provide the most flexibility, scalability, and customization options, but also require a greater investment of time and resources to build and maintain.
To summarize, the value of no-code, low-code, and pro-code platforms depends on the user’s skill level and the complexity of the project they are working on.
...low-code platforms provide more flexibility than no-code platforms, but still require less coding knowledge than traditional pro-code development platforms.
In the evolving landscape of Conversational AI applications in all business areas, such as contact centers where large amounts of customer requests are handled, businesses should strategically choose a platform based on their specific needs. They should consider factors such as the complexity of tasks to be automated and the level of customization required.
No-code and low-code platforms can be a great option for individuals or businesses with limited technical resources, such as start-up companies, while pro-code platforms provide the greatest level of control and customization for professional developers and enterprise contact centers.
Language Understanding Models May Fall Short
The language models powering no- and low-code chatbots have become much more sophisticated over the years. And with this there is an expectation that the language understanding of these platforms has also expanded. But you can’t have everything, right?
The limit to customization means that businesses may not be able to fine tune no- or low-code language models to their specific requirements.
If there are certain parts of the user queries that are difficult to understand, such as product names or business-specific terminology, or responses that need adapting to suit the needs of a specific customer base, the code may be difficult or impossible to change.
A benefit of pro-code platforms is the ability to create a seamless experience in different channels, personalize them, and allow for integration with other language models such as GPT (the model behind ChatGPT), expanding on the possibilities for Conversational AI.
The launch of ChatGPT has put Conversational AI into the limelight due to its ability to understand and generate human-like text with fluency.
For those reasons, many have connected GPT with their technology. In the context of contact centers, this could mean leveraging advanced AI and linguistic modeling tools to enhance the automation of customer service processes and improve the CX.
One Size Does Not Fit All
No- and low-code platforms have been viewed as the future of Conversational AI development since they are agile solutions for businesses that want to incorporate bots into their customer service offerings.
But language understanding is just one part of the Conversational AI experience. In order for a conversational application to do its job successfully, it needs to respond in the correct way to deliver valuable customer outcomes. Otherwise, it will only make the customer frustrated and turn to customer service.
Chatbots and voicebots are used as an alternative to speaking to a live agent at a contact center. But if no-and low-code bots are unable to answer customer queries, their value is very limited.
To address this, businesses can consider implementing solutions, which offer advanced AI capabilities to streamline call routing and automate repetitive tasks, enhancing the efficiency and effectiveness of contact centers.
Businesses need to also think about...if they are skilled enough to use a pro-code, or even low-code, platform.
Customers expect a seamless experience when interacting with businesses over both chat and phone. The success of any system depends on its ability to scale and provide a seamless and an engaging experience for customers. They need to include the following.
- Multichannel support. The system should allow businesses to deploy AI (artificial intelligence) applications across multiple channels, including voice assistants, Conversational IVR, chatbots, and messaging platforms, giving customers a consistent experience across all channels.
- Advanced analytics. Built-in analytics and reporting tools enable businesses to gain insights into customer behavior and preferences, and to continuously improve applications over time.
- Enterprise-grade security. Security features that are designed to protect user data and maintain compliance with relevant regulations, including role-based access controls and encryption.
- Scalability. A cloud-based architecture that allows businesses to scale up or down the application based on demand, ensuring that performance and availability are maintained even under high volumes of user interactions.
- Accuracy in understanding customer queries. It is imperative for the system to accurately interpret and understand the customer’s queries to facilitate appropriate responses. Leveraging advanced linguistic technologies can enhance the precision in understanding the nuances of customer language, thereby avoiding miscommunications and ensuring that the responses are aligned with the customer’s intent.
- Integration with other applications, on demand. Businesses need the freedom to tap into other applications – backend systems or language models: as they need, when they need it. Having full control over what happens when will let the business fine-tune the user experience and maximize the value.
By focusing on these essential features, businesses can build a system that not only meets the current demands but is also equipped to adapt to evolving customer needs, ensuring a service that is both efficient and satisfactory.
...the integration of no- and low-code platforms with pro-code platforms can achieve greater flexibility, scalability, and customization options.
Businesses need to also think about the type of Conversational AI they want to adopt, consider the language model needed, and if they are skilled enough to use a pro-code, or even low-code, platform. It’s also important for businesses to assess how much value they want out of their customer service, which will also determine what level of code they go for.
On the whole, businesses need to think critically about what CX they want to deliver, and what their objectives are. They must then choose a technology that delivers value for their organization.
What is certain though, is that the CX is not just a single-handed channel. The future of the CX needs to include both the chat and the telephone experience.
By focusing on the best user experience and improving it, companies will drive the highest customer satisfaction. However, enterprises should be careful with chatbots and IVR systems when designed only to automate tasks and reduce costs, rather than to create a positive user experience.
The increasing demand for self-service solutions in customer service has led to the rise of Conversational AI and the emergence of no- and low-code platforms. While these platforms offer a cost-effective option for small enterprises, they may not provide the level of customization required by certain businesses, and their generic language models may fall short in delivering the desired CX.
Therefore, the integration of no- and low-code platforms with pro-code platforms can achieve greater flexibility, scalability, and customization options.
Ultimately, the success of any Conversational AI system depends on its ability to provide a seamless and engaging experience for customers, on its ability to scale across channels and to provide insights into the interactions via advanced analytics.