Contact centers are paying the price for underinvestment in automation technologies. While centers are increasingly embracing these tools for simpler interactions, they are understandably cautious when using them for complex customer interactions. All too often automation tools have underdelivered: and too often resulted in poor customer experiences (CXs).
New artificial intelligence (AI)-powered technologies have shown significant promise in handling higher-order interactions. But are centers too “once burned, twice shy” when it comes to their adoption?
The Automation Promise
Most customers still prefer to speak with a live agent on the phone for more complex inquiries. At the same time, they overwhelmingly prefer self-service over speaking with a human agent for common and simple customer service questions.
In many cases, customers only resort to calling the contact center when they have failed to resolve their issue online via self-service and expect a high level of service from a human agent. As a result, contact centers are facing increasing pressure to find the right balance between automation and human support, while also providing a seamless and personalized CX across all channels.
Repetitive service inquiries like tracking shipments, changing passwords, or asking simple product questions or FAQs and IVRs are more commonly being diverted from agents to chatbots in an automation-first approach. But how can companies use AI-powered options to further extend customer self-service and the cost reductions it yields, thereby living up to the promise of automation?
The Problems With Chatbots and Self-Service
Despite the many benefits of chatbots and self-service solutions, there are still customers who dislike these technologies for customer service help.
Automated solutions can also be difficult to use and understand for customers who are not tech-savvy.
For older or less technically proficient customers, these tools may be confusing or intimidating, leading to frustration and a negative overall experience. Additionally, non-AI chatbots may not be able to understand regional accents or dialects, leading to miscommunications and customer dissatisfaction.
One of the primary reasons for these difficulties, resulting in poor CXs, is that non-AI-based chatbots lack the ability to understand the needs of the customer due to poor natural language processing (NLP). Or they aren’t integrated with systems that enable them to provide the answers. As a result, these chatbots cannot provide personalized responses.
Non-AI chatbots rely on a predetermined set of answers, leading to frustrating interactions for the users. They cannot adapt to varying contexts or emotions, making them rigid and impersonal. Customers today expect personalized and efficient customer service, which non-AI chatbots simply cannot deliver, especially in responding to more nuanced or complex issues.
A Shifting Perspective on AI-Powered Chatbots
AI capabilities can be used to help assist in and automate tasks typically handled by people. The main advantage of using an AI application in business is that it can be created for specific verticals, using vast datasets and narrow knowledge to create highly tailored systems.
AI can significantly improve customer service by augmenting human agents and delivering a faster, more efficient response to customers who have simple, easy-to-understand-and-resolve issues.
For example, Conversational AI for the customer contact center can learn what specific questions customers are likely to ask, the different ways they may phrase them, and what kind of response is most likely to lead to a positive outcome.
Another type of complimentary AI, Generative AI, or Large Language Models (LLMs), focuses on creating new content or output based on an understanding of data inputs and patterns. For the contact center, this means truly natural, empathetic, lifelike experiences for customers.
...chatbots have the potential to play an increasingly important role in customer service in the years to come.
In recent years, there has been a notable shift in the perspective and attitude toward AI-powered chatbots.
Once seen as a relatively new and untested technology, chatbots are now being embraced by businesses of all sizes as a way to improve customer service and reduce costs.
With advances in NLP and machine learning, chatbots are becoming more sophisticated and capable of handling a wider range of customer interactions. As a result, many businesses are seeing the benefits of chatbots in terms of improved efficiency, increased customer satisfaction, and reduced support costs.
Another factor driving the shift in attitude toward chatbots is the increasing importance of digital channels for customer service. As more customers turn to digital channels, such as social media, messaging apps, and chat, businesses are finding that chatbots can be an effective way to provide quick, personalized support around the clock.
As businesses continue to prioritize digital channels and embrace automation, it seems likely that chatbots have the potential to play an increasingly important role in customer service in the years to come.
However, while the COVID-19 pandemic and a labor shortage motivated contact centers to turn to automation, there is still much to be done around transforming the CX. Without adequate investment in automation, contact centers risk falling behind the competition and failing to meet the evolving needs and expectations of today’s customers, and businesses could suffer as a result.
Why Conversational AI Brings the Magic
Conversational AI is often described as magic because of its ability to understand natural language and carry on human-like conversations with customers, unlike rules-based software.
Unlike traditional customer service channels, Conversational AI can handle large volumes of inquiries and requests simultaneously, providing fast and efficient service around the clock. This has major implications for businesses, as it enables them to scale their customer service operations without sacrificing quality or incurring high costs.
Conversational AI is also incredibly versatile, with the ability to handle a wide range of customer interactions.
These range from simple inquiries to complex problem-solving. As examples, chatbots can help customers track orders, book appointments, and troubleshoot technical issues. This not only reduces wait times for customers but also improves overall efficiency and reduces costs for businesses.
Perhaps most importantly, Conversational AI has the potential to greatly improve overall CX. By providing fast, personalized service around the clock, businesses can demonstrate their commitment to customer satisfaction and build stronger relationships with customers.
Conversational AI can also help businesses collect valuable data and insights about their customers. This functionality allows them to identify pain points and areas for improvement in their products and services.
Ultimately, by leveraging the power of Conversational AI, businesses can drive greater customer loyalty and long-term success.
Key Challenges and Barriers With Using Conversational AI
While Conversational AI has many benefits for businesses, there are also several key challenges and barriers to its successful implementation.
One of the biggest is the quality and accuracy of the AI models used to power chatbots and other Conversational AI tools. Without robust training data and ongoing optimization, chatbots can struggle to accurately understand customer inquiries and respond appropriately. This can result in frustrating CXs and damage to the brand’s reputation.
To overcome this challenge, businesses are investing in high-quality training data. They are also partnering with experienced AI vendors to ensure that their chatbots are accurate and effective.
Another challenge with Conversational AI is maintaining the right balance between automation and human support. While this technology can handle a greater variety and depth of inquiries than its predecessors, there are still many complex issues that require human intervention.
To address this, businesses are implementing hybrid solutions that combine the best of both worlds: AI-powered automation for simple inquiries and human support for more complex issues. This not only improves customer satisfaction but also ensures that businesses are equipped to handle a wide range of customer interactions.
A third challenge with Conversational AI is ensuring that the technology is inclusive and accessible to all customers. For example, chatbots that rely heavily on NLP may not be effective for customers who speak different languages or have accents or dialects that are difficult to understand. To overcome this challenge, businesses are investing in technologies such as multilingual chatbots and voice assistants that can understand a wider range of languages and dialects.
Adding the “Human Touch” Into Automation: How Generative AI Enhances Conversational AI
Generative AI (e.g., ChatGPT) refers to AI models that can generate new content, such as text or images, based on a given prompt or context. It is the next generation of automation. When applied to Conversational AI, Generative AI can greatly enhance the ability of chatbots and other Conversational AI tools to engage in human-like conversations with customers.
If Conversational AI is magic, consider Generative AI as taking it to the next level by adding that all-important human touch. And by doing so it makes chatbots truly mainstream, and not just on the front ends of interactions.
By generating new responses based on the context of the conversation, Generative AI can help chatbots provide more personalized and natural-feeling responses to customer inquiries, improving overall customer satisfaction. And, equally critically, it allows more interactions to be automated by handling an even greater depth and volume of inquiries by providing greater accuracy and effectiveness.
One key way that Generative AI enhances Conversational AI is by enabling chatbots to handle more complex and nuanced interactions. For example, chatbots powered by Generative AI can understand the meaning behind customer inquiries and generate responses that are tailored to the customer’s specific needs and preferences.
Generative AI can also help Conversational AI tools better understand the emotional tone of customer interactions, enabling chatbots to respond appropriately to customers who may be frustrated or upset.
By analyzing the context and tone of the conversation, Generative AI can help chatbots generate empathetic and supportive responses that show customers that the business cares about their concerns. This can improve overall CX and build stronger relationships between businesses and their customers.
In addition, to the benefit of the team building bots, Generative AI can make bot building faster by automating the process of generating natural language responses to customer inquiries.
Traditionally, building a chatbot required extensive manual effort to create a database of responses and link them to specific user inputs. These then follow the rules laid out in the software.
With Generative AI, chatbot builders can instead provide the system with a set of training data: and the AI model can automatically generate new responses based on that data. This significantly reduces the time and effort required to create and train a chatbot, while also improving the accuracy and naturalness of the bot’s responses.
As a result, Generative AI is increasingly being used to speed up the development and deployment of chatbots and other Conversational AI tools, helping businesses to respond to the needs and preferences of their customers more quickly and effectively.
Overall, Generative AI is a powerful tool for enhancing Conversational AI, enabling businesses to provide more effective, personalized, and human-like customer service.
Usage Recommendations of Conversational AI in the Contact Center
When it comes to using Conversational AI and Generative AI in the contact center, there are several key usage recommendations that businesses should consider.
First, it’s important to start with a clear understanding of the business objectives and customer needs that the technology is intended to address. This will help businesses identify the most appropriate use cases for Conversational AI and Generative AI and ensure that the technology is aligned with business goals and customer expectations.
Second, businesses should carefully consider the integration of the technology into their existing contact center workflows and processes. This includes selecting the right technology vendors and tools, training employees on how to use the technology effectively, and designing processes that ensure a seamless customer experience across all channels.
It’s also important to ensure that the technology is properly monitored and optimized over time. This ensures that it continues to deliver value to the business and meet the evolving needs of customers.
Overall, the successful use of Conversational AI and Generative AI in the contact center requires a strategic and thoughtful approach, focused on delivering value to both the business and the customer. But one that will deliver the ROI businesses expect from automation investments.
By carefully selecting and integrating the right technology, and by designing workflows and processes that prioritize customer experience and efficiency, businesses can leverage Conversational AI and Generative AI to deliver powerful new capabilities and enhance the overall effectiveness of their contact center operations.