3 Reasons Chatbots Fail and How to Fix Them

Today’s chatbots are increasingly useless. Enterprises have been adopting chatbots, seeing the benefits of creating more seamless customer experiences and enhancing productivity. More than 50% spend more yearly on chatbots than on developing traditional mobile apps. However, implementation has proven to be a major sticking point as enterprises fail to understand the nuances and best practices fundamental to the high functionality of enterprise chatbots. 

One study shows that most enterprise chatbots were poorly implemented, leading to consistent customer complaints. Nothing is more frustrating than a customer or employee talking to a chatbot that constantly repeats what’s being said or has little to no understanding of context. Enterprise chatbots, at their best, should simplify knowledge management, automate low-value and repetitive tasks, and create hyper-personalized experiences that extract strong value from customer conversations. Instead, chatbot failure continues happening at an alarming rate. More practical knowledge and understanding of why chatbots fail are necessary for enterprises to address their implementation pain points. Here are the three biggest reasons they fail and the best practices to fix them. 

Success Metrics Misalignment 

One of the biggest challenges for enterprises when creating chatbots is that they fail to adequately define the right key performance indicators (KPIs) that determine the effectiveness of the bots. For instance, an enterprise creates a chatbot for answering customer queries and FAQs but bases the success of the bot’s pilot run on how natural-sounding the chatbot is. Using such an indicator to determine chatbot functionality is flawed and doesn’t serve any purpose to business objectives. 

Having the wrong APIs also leads to inefficient use cases. Whether the enterprise chatbot is meant for procurement, the IT desk, or human resources, not having a definitive API that successfully tracks the knowledge base a chatbot possesses leads to ineffective dialogue, a diminished return on investment (ROI), and a lot of maintenance headaches. 

Success metrics should be based on actionable traits that ensure functionality and interactivity for every use case that enterprise chatbots are used. Enterprise chatbots should have strong conversational skills, with short and long-term memory, as well as deep contextual parse to ensure high-level comprehension. 

Too many of today’s enterprise chatbots rely on statistical data, meaning that their comprehension level is shallow and restrictive, preventing them from understanding the specific needs of a customer. With deep context at the fore, enterprise chatbots reflect the best elements of conversational AI, extracting high value from conversations, and allowing enterprises to directly address unique needs. Success metrics should be achievable and measurable while matching the use cases enterprises intend for the chatbots. 

Enterprises should also approach chatbot development with a long-term focus, dedicated to achieving results at every stage of the development cycle and using actionable KPIs as markers to optimize chatbot performance going forward, using best practice checklists as guides.  

Business involvement is necessary during the enterprise chatbot development process as stakeholders should understand the importance of providing optimal customer experiences with quality chatbot responses. Business leaders can establish the right KPIs, outline the design parameters, identify clear use cases and immediately demonstrate the value. If the importance of highly-optimized enterprise chatbots is stressed from the outset, a better alignment of success metrics becomes achievable.

A Clear Lack of Specificity For Chatbots

Another major sticking point preventing enterprise chatbots from reaching anywhere is a clear lack of specificity caused by clear design flaws. 

Customers and employees alike seek specific answers when interacting with an enterprise chatbot. Instead of the chatbots understanding the context and phrasing of the specific queries that customers/employees pose, enterprise chatbots bombard them with unrelated information, FAQ documentation, web pages, or the typical ‘I didn’t get that’ type of response. Such frustration drives customers away while preventing employees from fulfilling their roles to their best. 

Enterprise chatbots must be designed to solve pain points directly, or they are doomed at the pilot stage. As a result, enterprises are less likely to implement chatbots on a larger scale in the long term, causing them to miss out on dynamic business opportunities and denting their reputations with customers. 

Too often, the lack of specificity for chatbots is caused by an overreliance on training data as they are very large and labeled. Enterprise chatbots that leverage ontology yields better results because they understand the process or product-specific terms and their synonyms. Ontology also allows enterprise chatbots to understand product-specific properties, plus FAQs and different units of measure. The amount of training data needed for chatbots to succeed should be minimal, with the chatbots learning in real-time through natural language to develop the required knowledge and skills to interact on a hyper-personalized level with customers/employees. To know more how to train chatbot on your own data, you can check out a comprehensive guide to train a chatbot effectively.

Too Much Emphasis On Data 

Data can be a gift and a curse in one. It often goes hand-in-hand with technology. But, data can also strip enterprise chatbots and various other technologies of their dynamism. 

One reason enterprise chatbots fail on a wide-scale level is that they are too scripted and hard-coded, leaving little room for interpretation and eradicating personalization. Enterprise chatbots should steer away from a read-only structure and focus on dynamic personalization. With real-time adaptive dynamic context, enterprise chatbots develop more wide-ranging conversations, possess high-level reasoning, and possess interactive unsupervised learning capabilities. As a result, enterprise leaders don’t have to micromanage the chatbots when they’re put into practice. They would have all the learning capabilities necessary to interact with others without pre-determination, mimicking human abilities and emotions when answering queries. 

It can be easy to fall into old habits if there are issues along the way. But, with the right business involvement to effectively manage change, enterprises can avoid reverting to excessive reliance on data and scripting to optimize chatbot functionality. Investing the right time and effort into improving chatbot quality while ensuring chatbots provide the natural and personalized experiences people seek when interacting with technology.

Enterprise chatbots only have a future when the leaders that implement them move away from traditional development methodologies, focusing more on delivering the highly contextual and interactive experiences people need to be productive.

Read Also: 6 Tips for Providing Excellent Customer Service


Srini Pagidyala

Srini Pagidyala is a seasoned digital transformation entrepreneur with over twenty years of experience in technology entrepreneurship. In 2017, he Co-Founded, a new category “chatbot with a brain” that delivers hyper personalized conversational experiences. 



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