50 percent of AI Chatbots are not adopted due to cold and static responses

In the rapidly evolving landscape of customer support, the advent of AI-driven technologies has brought about transformative changes. However, despite the leaps made in AI capabilities, many enterprises remain cautious about embracing AI-powered solutions, particularly in their customer-facing operations.

Kapture CX, a customer support automation platform, recently conducted a survey to gain insights into the major objections that hinder the adoption of AI chatbots in customer support. Kapture CX offers vertical focused chatbots tailored for Travel, BFSI, Consumer Durables and Retail Industry. Other leading customer support automation platforms like Zendesk (Answer bot), Freshdesk (Freddy), and Zoho (Zia) had launched their generative AI based chatbots earlier this year.

The survey, which targeted senior customer support managers from leading Indian B2C brands, shed light on crucial concerns and misconceptions that hinder the seamless integration of AI chatbots.

One of the prominent objections (50%)  highlighted by the survey is the perception of “cold and static responses” associated with chatbots. This objection often stems from the confusion between rule-based chatbots and AI-powered chatbots.

Rule-based chatbots operate on predefined conversational paths, limited to providing responses based on predetermined questions and answers. When users deviate from these predetermined paths, rule-based chatbots struggle to provide relevant answers.

On the other hand, AI chatbots leverage generative AI, enabling them to understand and interpret human language, thus offering dynamic and contextually relevant responses.

AI-powered chatbots have emerged as game-changers, capable of assigning meaning to written or spoken words, performing complex operations, categorizing messages, and even translating content.

The synergy between AI and generative language models like ChatGPT empowers these chatbots to excel across a wide array of industries, offering a more personalized and effective customer experience.

Kapture CX’s integration of ChatGPT into their Kapture chatbot demonstrates how the boundaries of customer support are expanding, with advanced industry-specific use cases being rolled out to users.

Complex set up and integration process was highlighted as the major obstacle by 19% of the respondents.

The complexity of implementing, setting up, and integrating AI chatbots for customer support in large enterprise brands can vary based on several factors, including the sophistication of the chatbot, the existing infrastructure, and the specific goals of the organization.

Developing a customized chatbot tailored to the support workflow is obviously a time consuming and costly process. Here is the importance of industry focussed AI chatbots, which can bypass many of the implementation hurdles listed below.

Chatbot Sophistication: The complexity of the AI chatbot itself plays a significant role. The more advanced the chatbot’s capabilities, the more intricate the setup might be.

Data and Training: To develop an effective AI chatbot, training data is crucial. This includes historical customer interactions, relevant documents, and other sources of information. Obtaining, preprocessing, and structuring this data can be complex, especially in large enterprises with diverse data sources.

NLP and Machine Learning: If the chatbot uses NLP and machine learning, developing and fine-tuning the underlying models can be complex.

Integration with Existing Systems: Large enterprises often have multiple systems and platforms in place, such as customer relationship management (CRM) systems, helpdesk software, and databases.

Customization and Personalization: Creating a chatbot that can understand the context of a conversation and tailor responses to individual customers’ needs can be intricate.

Security and Compliance: Enterprises handle sensitive customer data and must adhere to various industry regulations and data privacy standards (e.g., GDPR, HIPAA). Ensuring that the chatbot’s interactions comply with these standards can add complexity to the implementation.

A significant portion (17%) of the survey participants expressed concerns about data privacy as a deterrent to adopting AI-based chatbots.

This apprehension is particularly pronounced in industries such as BFSI (Banking, Financial Services, and Insurance) and Healthcare, where data security and privacy are paramount.  It is crucial to dispel the misconception that AI chatbots indiscriminately access and manipulate user data.

Contrary to this misconception, when SaaS platforms integrate with AI models like ChatGPT, they are purchasing access to APIs rather than sending user data to the model. Only the necessary data required to process information is passed on to the API, and this data is encrypted using secure protocols.

In sectors like BFSI, where concerns are more pronounced, a cautious approach to AI adoption is warranted. Industry players can mitigate risks by conducting thorough vendor assessments, implementing privacy-centric designs, and integrating robust security measures.

14% of the survey respondents said they prefer to have real conversations with their customers rather than automating the support function.

Even when there is potential for increased efficiency and cost savings, many companies believe that the human touch in customer interactions is essential for building relationships. They fear that automation might lead to a loss of empathy and emotional connection.

To balance between automation and the human touch, businesses can consider the following strategies:

Hybrid Approach: Implement a hybrid model where automation is used for routine tasks and frequently asked questions, while human agents handle more complex or sensitive interactions.

Natural Language Processing (NLP): Utilize advanced NLP in automated systems to enable more natural and context-aware interactions, thereby bridging the gap between human-like conversations and automation.

Escalation Paths: Design a clear escalation path from automation to human agents. If the automated system cannot solve a problem, customers should be seamlessly transferred to a human agent

Transparency: Be transparent with customers about the use of automation. Let them know when they are interacting with a chatbot and when a human agent takes over.

Customer Feedback: Involve customers in the process. Ask for their feedback on automated interactions

Pilot Programs: Start with pilot programs to test the effectiveness of automation in a controlled environment.

The obstacles in adopting AI based chatbots in customer support varies from industry to industry.

In the retail sector, the obstacles can be lack of empathy, or complex set up and integration process.

In the Finance sector, it could be security and data privacy. In travel it could be the ability to escalate priority tickets to human agents and so on.

These challenges call for AI chatbots that are tailored for each industry vertical. Customer support platforms like Zendesk (Answer bot), Freshdesk (Freddy), and Zoho (Zia) had also introduced their revamped AI based chatbots earlier this year.

While these chatbots are standardized to implement across all industry verticals, Kapture chat stands out with chatbots that are specifically built to address the support challenges in retail, travel, BFSI and consumer durables industries.