We are all witnessing a significant adoption of chatbots among businesses. Every sector, be it a healthcare firm or SaaS startup, an e-commerce store, or a financial institution – is experimenting with chatbots & Generative AI technologies. There are numerous benefits enterprises can reap from generative AI and intelligent chatbots. They can automate redundant conversations and transaction processing. Chatbots running on generative AI models can also mimic human-like actions to solve problems. All these initiatives and adoption allow companies to concentrate on core business operations and prepare for strategic activities for generating revenues. However, there are acute reasons why enterprise chatbots are failing. Let’s get a complete walkthrough of the pitfalls of chatbots. It will also highlight why enterprises should introduce data governance for a successful AI system.
Deployment of Chatbots and AI for Enterprise Systems
Chatbots have been in the market for more than a decade. But with the advancement in chatbots through generative AI models, enterprises have opened doors to an entirely new performance area. Managing the data necessary for building these AI models requires significant concern.
Without proper data governance & control, AI models cannot work at their full potential. That is where Chief Information Officers and Chief Data Officers play a notable role. As of September 2023, enterprises are witnessing a significant shift of Gen-AI models from the ‘hackathon phase’ to a fully working stage.
According to a McKinsey’s report, less than a third of firms have already deployed various forms of AI across one or multiple business operations. However, the most basic implementation of this technology – the chatbot has not seen massive deployment in large organizations as expected.
Reason Behind the Pitfalls of Chatbots
There are several reasons why the use of intelligent chatbots is failing. Inadequate identification of customer use cases, poor user experience, lack of transparency, inadequate data governance, and data collection are prominent reasons. Again, cultural barriers in early adoption pose a significant challenge that leads to model unreliability.
Numerous firms across different sectors have tried using intelligent chatbots and generative AI bots to mimic human-like responses. But they got thwarted because most of the finest chatbots have failed to a large extent. Many of them produce inaccurate or hallucinated responses. Thus, it wastes more time than saving it.
Drawbacks of Unsuccessful Enterprise Chatbots
With the rise of using chatbots and AI-based chatting models, the development of chatbots has gone fast. But the quality is not up to the mark, even with the generative AI-driven chatbots. Unsuccessful AI chatbots can drag a business to several concerns. Here are some of them.
- Miscommunication: Hallucinated or incorrect responses to customer queries waste time for both the customers & the business. Misinterpretations of user queries can also compel customers to move away from your service. With miscommunication, chatbots might also give incorrect solutions, causing negativity and confusion in the customers’ minds.
- Poor user experience: Enterprise AI chatbots failing to comprehend user queries or responding with irrelevant or confusing replies can frustrate customers. In such cases, frustrated users often abandon the interaction with utter dissatisfaction. It leads to poor user experience with chatbots.
- Businesses lose credibility: Customers expect chatbots to be reliable and knowledgeable. Everyone knows that artificial intelligence and machine learning models get trained through lots of data. However, when a chatbot provides subpar or vague responses to customer queries, it erodes customers’ trust. It leads to a loss of credibility. Hence, it declines customer loyalty, affecting long-term customer relationships.
- Data security concerns: Many intelligent chatbot developers do not implement filters and restriction techniques in their chatbots. Poorly designed chatbots with low security can be vulnerable to a business. It can pose unauthorized access and leak business data. It may also lead to legal consequences.
Bridging the Gap of Chatbots with Data Governance
Enterprise leaders noted that the primary reason for unreliable performance & failures of chatbots is because of poor data governance. To develop a successful AI, developers & engineers need to consider data governance. Without appropriate data control and management, AI models used for enterprise chatbots will get trained on potentially unrelated and low-profile datasets.
So, let us understand what data governance is. Data governance is a collective technique for providing high-quality data for different purposes. The practice involves deriving and implementing standards, guidelines, protocols, policies, etc., to ensure that the data provides value to the business. The preliminary intent of data governance, especially in Artificial Intelligence (AI), is to enhance data reliability, maintain data ethics, and protect sensitive data from accidental exposure.
Data governance plays a significant role in chatbot model development also. It eradicates outdated, inconsistent, and irrelevant data so that the chatbots can give accurate answers. Chatbots that get trained on Large Language Models (LLMs) feed on data collected from multiple sources. Some popular sources are Slack messages & threads, emails, human/agent chats, employee policies, etc. The demand for these data escalates with the advent of GenAI.
But, recent GenAI development for chatbots started overseeing data governance and quality checks. That led to a massive snag toward steadfast chatbot model deployment. Let us suppose, those chatbots running for financial institutions or healthcare firms. They often answer questions about past investments or patient’s health details containing PII (Personally Identifiable Information).
In these situations, the chatbot might leak confidential data accidentally. That is where data governance for data filtration plays a notable role. Again, in another situation, a chatbot might deliver outdated report with crucial assumptions. Although the LLM of the chatbot might try to supply reasonable answers, it might fail to provide critical contextual information. For these reasons, CIOs and CISOs play a notable role in extracting meaningful data used in chatbot model learning.
Conclusion
Enterprises that use chatbots rigorously are apprehending the reasons for the failure of AI chatbots. They have understood how data governance can bridge the gap for creating a reliable chatbot while keeping the security postures like assessing relevant data, maintaining quality on accuracy, and safety. With the proper data governance within chatbots, enterprises can create large language models to augment customer experience and improve operational efficiencies. Thus, enterprises must establish a solid data governance framework.
To further empower enterprises in this journey towards effective data governance and successful chatbot implementation, VE3 can play a crucial role. With our comprehensive suite of tools and solutions, we help organizations in navigating the complexities of data governance, ensuring the responsible and strategic use of data in the development and deployment of chatbots. By leveraging the our capabilities you can fortify your data governance practices, ultimately leading to more reliable and efficient chatbot implementations that positively impact both customer interactions and operational processes. To know more, explore our innovative digital solutions or contact us directly.
